├── .github
├── CODEOWNERS
├── dependabot.yml
└── workflows
│ ├── pr-and-push.yml
│ ├── pypi-publish-on-release.yml
│ └── test-lint.yml
├── .gitignore
├── .pre-commit-config.yaml
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── NOTICE
├── README.md
├── pyproject.toml
└── src
└── strands_mcp_server
├── __init__.py
├── __main__.py
├── content
├── model_providers.md
├── quickstart.md
└── tools.md
└── server.py
/.github/CODEOWNERS:
--------------------------------------------------------------------------------
1 | # These owners will be the default owners for everything in
2 | # the repo. Unless a later match takes precedence,
3 | # @strands-agents/contributors will be requested for
4 | # review when someone opens a pull request.
5 | * @strands-agents/maintainers
--------------------------------------------------------------------------------
/.github/dependabot.yml:
--------------------------------------------------------------------------------
1 | version: 2
2 | updates:
3 | - package-ecosystem: "pip"
4 | directory: "/"
5 | schedule:
6 | interval: "daily"
7 | open-pull-requests-limit: 100
8 | commit-message:
9 | prefix: ci
10 | - package-ecosystem: "github-actions"
11 | directory: "/"
12 | schedule:
13 | interval: "daily"
14 | open-pull-requests-limit: 100
15 | commit-message:
16 | prefix: ci
17 |
--------------------------------------------------------------------------------
/.github/workflows/pr-and-push.yml:
--------------------------------------------------------------------------------
1 | name: Pull Request and Push Action
2 |
3 | on:
4 | pull_request: # Safer than pull_request_target for untrusted code
5 | branches: [ main ]
6 | types: [opened, synchronize, reopened, ready_for_review, review_requested, review_request_removed]
7 | push:
8 | branches: [ main ] # Also run on direct pushes to main
9 | concurrency:
10 | group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
11 | cancel-in-progress: true
12 |
13 | jobs:
14 | call-test-lint:
15 | uses: ./.github/workflows/test-lint.yml
16 | permissions:
17 | contents: read
18 | with:
19 | ref: ${{ github.event.pull_request.head.sha || github.sha }}
20 |
--------------------------------------------------------------------------------
/.github/workflows/pypi-publish-on-release.yml:
--------------------------------------------------------------------------------
1 | name: Publish Python Package
2 |
3 | on:
4 | release:
5 | types:
6 | - published
7 |
8 | jobs:
9 | call-test-lint:
10 | uses: ./.github/workflows/test-lint.yml
11 | permissions:
12 | contents: read
13 | with:
14 | ref: ${{ github.event.release.target_commitish }}
15 |
16 | build:
17 | name: Build distribution 📦
18 | permissions:
19 | contents: read
20 | needs:
21 | - call-test-lint
22 | runs-on: ubuntu-latest
23 |
24 | steps:
25 | - uses: actions/checkout@v4
26 | with:
27 | persist-credentials: false
28 |
29 | - name: Set up Python
30 | uses: actions/setup-python@v5
31 | with:
32 | python-version: '3.10'
33 |
34 | - name: Install dependencies
35 | run: |
36 | python -m pip install --upgrade pip
37 | pip install hatch twine
38 | - name: Validate version
39 | run: |
40 | version=$(hatch version)
41 | if [[ $version =~ ^[0-9]+\.[0-9]+\.[0-9]+$ ]]; then
42 | echo "Valid version format"
43 | exit 0
44 | else
45 | echo "Invalid version format"
46 | exit 1
47 | fi
48 | - name: Build
49 | run: |
50 | hatch build
51 | - name: Store the distribution packages
52 | uses: actions/upload-artifact@v4
53 | with:
54 | name: python-package-distributions
55 | path: dist/
56 |
57 | deploy:
58 | name: Upload release to PyPI
59 | needs:
60 | - build
61 | runs-on: ubuntu-latest
62 |
63 | # environment is used by PyPI Trusted Publisher and is strongly encouraged
64 | # https://docs.pypi.org/trusted-publishers/adding-a-publisher/
65 | environment:
66 | name: pypi
67 | url: https://pypi.org/p/strands-agents-mcp-server
68 | permissions:
69 | # IMPORTANT: this permission is mandatory for Trusted Publishing
70 | id-token: write
71 |
72 | steps:
73 | - name: Download all the dists
74 | uses: actions/download-artifact@v4
75 | with:
76 | name: python-package-distributions
77 | path: dist/
78 | - name: Publish distribution 📦 to PyPI
79 | uses: pypa/gh-action-pypi-publish@release/v1
80 |
--------------------------------------------------------------------------------
/.github/workflows/test-lint.yml:
--------------------------------------------------------------------------------
1 | name: Lint
2 |
3 | on:
4 | workflow_call:
5 | inputs:
6 | ref:
7 | required: true
8 | type: string
9 |
10 | jobs:
11 | lint:
12 | name: Lint
13 | runs-on: ubuntu-latest
14 | permissions:
15 | contents: read
16 | steps:
17 | - name: Checkout code
18 | uses: actions/checkout@v4
19 | with:
20 | ref: ${{ inputs.ref }}
21 | persist-credentials: false
22 |
23 | - name: Set up Python
24 | uses: actions/setup-python@v5
25 | with:
26 | python-version: '3.10'
27 | cache: 'pip'
28 |
29 | - name: Install dependencies
30 | run: |
31 | pip install --no-cache-dir hatch
32 |
33 | - name: Run lint
34 | id: lint
35 | run: hatch run lint
36 | continue-on-error: false
37 |
--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
1 | build
2 | __pycache__*
3 | .coverage*
4 | .env
5 | .venv
6 | .mypy_cache
7 | .pytest_cache
8 | .ruff_cache
9 | *.bak
10 | .vscode
11 | dist
12 | venv/
13 | *.egg-info
14 |
--------------------------------------------------------------------------------
/.pre-commit-config.yaml:
--------------------------------------------------------------------------------
1 | repos:
2 | - repo: local
3 | hooks:
4 | - id: hatch-format
5 | name: Format code
6 | entry: hatch fmt --formatter
7 | language: system
8 | pass_filenames: false
9 | types: [python]
10 | stages: [pre-commit]
11 | - id: hatch-lint
12 | name: Lint code
13 | entry: hatch fmt --linter
14 | language: system
15 | pass_filenames: false
16 | types: [python]
17 | stages: [pre-commit]
18 | - id: commitizen-check
19 | name: Check commit message
20 | entry: hatch run cz check --commit-msg-file
21 | language: system
22 | stages: [commit-msg]
--------------------------------------------------------------------------------
/CODE_OF_CONDUCT.md:
--------------------------------------------------------------------------------
1 | ## Code of Conduct
2 | This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
3 | For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
4 | opensource-codeofconduct@amazon.com with any additional questions or comments.
5 |
--------------------------------------------------------------------------------
/CONTRIBUTING.md:
--------------------------------------------------------------------------------
1 | # Contributing Guidelines
2 |
3 | Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional
4 | documentation, we greatly value feedback and contributions from our community.
5 |
6 | Please read through this document before submitting any issues or pull requests to ensure we have all the necessary
7 | information to effectively respond to your bug report or contribution.
8 |
9 |
10 | ## Reporting Bugs/Feature Requests
11 |
12 | We welcome you to use the GitHub issue tracker to report bugs or suggest features.
13 |
14 | When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already
15 | reported the issue. Please try to include as much information as you can. Details like these are incredibly useful:
16 |
17 | * A reproducible test case or series of steps
18 | * The version of our code being used
19 | * Any modifications you've made relevant to the bug
20 | * Anything unusual about your environment or deployment
21 |
22 |
23 | ## Contributing via Pull Requests
24 | Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that:
25 |
26 | 1. You are working against the latest source on the *main* branch.
27 | 2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already.
28 | 3. You open an issue to discuss any significant work - we would hate for your time to be wasted.
29 |
30 | To send us a pull request, please:
31 |
32 | 1. Fork the repository.
33 | 2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change.
34 | 3. Ensure local tests pass.
35 | 4. Commit to your fork using clear commit messages.
36 | 5. Send us a pull request, answering any default questions in the pull request interface.
37 | 6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation.
38 |
39 | GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and
40 | [creating a pull request](https://help.github.com/articles/creating-a-pull-request/).
41 |
42 |
43 | ## Finding contributions to work on
44 | Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start.
45 |
46 |
47 | ## Code of Conduct
48 | This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
49 | For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
50 | opensource-codeofconduct@amazon.com with any additional questions or comments.
51 |
52 |
53 | ## Security issue notifications
54 | If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue.
55 |
56 |
57 | ## Licensing
58 |
59 | See the [LICENSE](LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution.
60 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 |
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/NOTICE:
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1 | Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
2 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 |
2 |
7 |
8 |
9 | Strands Agents MCP Server
10 |
11 |
12 |
13 | A model-driven approach to building AI agents in just a few lines of code.
14 |
15 |
16 |
17 |

18 |

19 |

20 |

21 |

22 |

23 |
24 |
25 |
26 | Documentation
27 | ◆ Samples
28 | ◆ Python SDK
29 | ◆ Tools
30 | ◆ Agent Builder
31 | ◆ MCP Server
32 |
33 |
34 |
35 | This MCP server provides documentation about Strands Agents to your GenAI tools, so you can use your favorite AI coding assistant to vibe-code Strands Agents.
36 |
37 | ## Prerequisites
38 |
39 | The usage methods below require [uv](https://github.com/astral-sh/uv) to be installed on your system. You can install it by following the [official installation instructions](https://github.com/astral-sh/uv#installation).
40 |
41 | ## Installation
42 |
43 | You can use the Strands Agents MCP server with
44 | [40+ applications that support MCP servers](https://modelcontextprotocol.io/clients),
45 | including Amazon Q Developer CLI, Anthropic Claude Code, Cline, and Cursor.
46 |
47 | ### Q Developer CLI example
48 |
49 | See the [Q Developer CLI documentation](https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/command-line-mcp-configuration.html)
50 | for instructions on managing MCP configuration.
51 |
52 | In `~/.aws/amazonq/mcp.json`:
53 |
54 | ```json
55 | {
56 | "mcpServers": {
57 | "strands": {
58 | "command": "uvx",
59 | "args": ["strands-agents-mcp-server"]
60 | }
61 | }
62 | }
63 | ```
64 |
65 | ### Claude Code example
66 |
67 | See the [Claude Code documentation](https://docs.anthropic.com/en/docs/claude-code/tutorials#configure-mcp-servers)
68 | for instructions on managing MCP servers.
69 |
70 | ```bash
71 | claude mcp add strands uvx strands-agents-mcp-server
72 | ```
73 |
74 | ### Cline example
75 |
76 | See the [Cline documentation](https://docs.cline.bot/mcp-servers/configuring-mcp-servers#editing-mcp-settings-files)
77 | for instructions on managing MCP configuration.
78 |
79 | Provide Cline with the following information:
80 |
81 | ```
82 | I want to add the MCP server for Strands Agents.
83 | Here's the GitHub link: @https://github.com/strands-agents/mcp-server
84 | Can you add it?"
85 | ```
86 |
87 | ### Cursor example
88 |
89 | See the [Cursor documentation](https://docs.cursor.com/context/model-context-protocol#configuring-mcp-servers)
90 | for instructions on managing MCP configuration.
91 |
92 | In `~/.cursor/mcp.json`:
93 |
94 | ```json
95 | {
96 | "mcpServers": {
97 | "strands": {
98 | "command": "uvx",
99 | "args": ["strands-agents-mcp-server"]
100 | }
101 | }
102 | }
103 | ```
104 |
105 | ## Quick Testing
106 |
107 | You can quickly test the MCP server using the MCP Inspector:
108 |
109 | ```bash
110 | npx @modelcontextprotocol/inspector uvx strands-agents-mcp-server
111 | ```
112 |
113 | Note: This requires [npx](https://docs.npmjs.com/cli/v11/commands/npx) to be installed on your system. It comes bundled with [Node.js](https://nodejs.org/).
114 |
115 | The Inspector is also useful for troubleshooting MCP server issues as it provides detailed connection and protocol information. For an in-depth guide, have a look at the [MCP Inspector documentation](https://modelcontextprotocol.io/docs/tools/inspector).
116 |
117 | ## Server development
118 |
119 | ```bash
120 | git clone https://github.com/strands-agents/mcp-server.git
121 | cd mcp-server
122 | python3 -m venv venv
123 | source venv/bin/activate
124 | pip3 install -e .
125 |
126 | npx @modelcontextprotocol/inspector python -m strands_mcp_server
127 | ```
128 |
129 | ## Contributing ❤️
130 |
131 | We welcome contributions! See our [Contributing Guide](CONTRIBUTING.md) for details on:
132 | - Reporting bugs & features
133 | - Development setup
134 | - Contributing via Pull Requests
135 | - Code of Conduct
136 | - Reporting of security issues
137 |
138 | ## License
139 |
140 | This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
141 |
142 | ## Security
143 |
144 | See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.
145 |
146 | ## ⚠️ Preview Status
147 |
148 | Strands Agents is currently in public preview. During this period:
149 | - APIs may change as we refine the SDK
150 | - We welcome feedback and contributions
151 |
--------------------------------------------------------------------------------
/pyproject.toml:
--------------------------------------------------------------------------------
1 | [project]
2 | name = "strands-agents-mcp-server"
3 | dynamic = ["version"]
4 | description = "A Model Context Protocol server that provides knowledge about building AI agents with Strands Agents"
5 | readme = "README.md"
6 | requires-python = ">=3.10"
7 | license = {text = "Apache-2.0"}
8 | authors = [
9 | {name = "AWS", email = "opensource@amazon.com"},
10 | ]
11 | classifiers = [
12 | "Development Status :: 3 - Alpha",
13 | "Intended Audience :: Developers",
14 | "License :: OSI Approved :: Apache Software License",
15 | "Operating System :: OS Independent",
16 | "Programming Language :: Python :: 3",
17 | "Programming Language :: Python :: 3.10",
18 | "Programming Language :: Python :: 3.11",
19 | "Programming Language :: Python :: 3.12",
20 | "Programming Language :: Python :: 3.13",
21 | "Topic :: Scientific/Engineering :: Artificial Intelligence",
22 | "Topic :: Software Development :: Libraries :: Python Modules",
23 | ]
24 | dependencies = [
25 | "mcp>=1.1.3",
26 | "pydantic>=2.0.0",
27 | ]
28 |
29 | [project.scripts]
30 | strands-agents-mcp-server = "strands_mcp_server.server:main"
31 |
32 | [build-system]
33 | requires = ["hatchling", "hatch-vcs"]
34 | build-backend = "hatchling.build"
35 |
36 | [tool.hatch.metadata]
37 | allow-direct-references = true
38 |
39 | [tool.hatch.version]
40 | # Tells Hatch to use your version control system (git) to determine the version.
41 | source = "vcs"
42 |
43 | [project.urls]
44 | Homepage = "https://github.com/strands-agents/mcp-server"
45 | "Bug Tracker" = "https://github.com/strands-agents/mcp-server"
46 | Documentation = "https://strandsagents.com"
47 |
48 | [project.optional-dependencies]
49 | dev = [
50 | "commitizen>=4.4.0",
51 | "hatch>=1.0.0",
52 | "pre-commit>=2.20.0",
53 | "ruff>=0.4.4",
54 | ]
55 |
56 | [tool.hatch.build]
57 | packages = ["src/strands_mcp_server"]
58 |
59 | [tool.hatch.envs.hatch-static-analysis]
60 | dependencies = [
61 | "mcp>=1.1.3",
62 | "pydantic>=2.0.0",
63 | "ruff>=0.4.4",
64 | ]
65 |
66 | [tool.hatch.envs.hatch-static-analysis.scripts]
67 | format-check = [
68 | "ruff format --check"
69 | ]
70 | format-fix = [
71 | "ruff format"
72 | ]
73 | lint-check = [
74 | "ruff check"
75 | ]
76 | lint-fix = [
77 | "ruff check --fix"
78 | ]
79 |
80 | [tool.hatch.envs.default.scripts]
81 | list = [
82 | "echo 'Scripts commands available for default env:'; hatch env show --json | jq --raw-output '.default.scripts | keys[]'"
83 | ]
84 | format = [
85 | "hatch fmt --formatter",
86 | ]
87 | lint = [
88 | "hatch fmt --linter"
89 | ]
90 |
91 | [tool.ruff]
92 | line-length = 120
93 | include = ["src/**/*.py"]
94 |
95 | [tool.ruff.lint]
96 | select = [
97 | "E", # pycodestyle
98 | "F", # pyflakes
99 | "I", # isort
100 | "B", # flake8-bugbear
101 | ]
102 |
103 | [tool.commitizen]
104 | name = "cz_conventional_commits"
105 | tag_format = "v$version"
106 | bump_message = "chore(release): bump version $current_version -> $new_version"
107 | version_files = [
108 | "pyproject.toml:version",
109 | ]
110 | update_changelog_on_bump = true
111 |
--------------------------------------------------------------------------------
/src/strands_mcp_server/__init__.py:
--------------------------------------------------------------------------------
1 | from . import server
2 |
3 |
4 | def main():
5 | """Strands Agents MCP Server"""
6 | server.main()
7 |
8 |
9 | if __name__ == "__main__":
10 | main()
11 |
--------------------------------------------------------------------------------
/src/strands_mcp_server/__main__.py:
--------------------------------------------------------------------------------
1 | from strands_mcp_server import main
2 |
3 | main()
4 |
--------------------------------------------------------------------------------
/src/strands_mcp_server/content/model_providers.md:
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1 | Strands Agents supports many different model providers. By default, agents use the Amazon Bedrock model provider with the Claude 3.7 model.
2 |
3 | You can specify a different model in two ways:
4 |
5 | 1. By passing a string model ID directly to the Agent constructor
6 | 2. By creating a model provider instance with specific configurations
7 |
8 | ### Using a String Model ID
9 |
10 | The simplest way to specify a model is to pass the model ID string directly:
11 |
12 | ```python
13 | from strands import Agent
14 |
15 | # Create an agent with a specific model by passing the model ID string
16 | agent = Agent(model="us.anthropic.claude-3-7-sonnet-20250219-v1:0")
17 | ```
18 |
19 | Models passed as string IDs will use the Bedrock model provider.
20 |
21 | ### Amazon Bedrock (Default)
22 |
23 | For more control over model configuration, you can create a model provider instance:
24 |
25 | ```python
26 | import boto3
27 | from strands import Agent
28 | from strands.models import BedrockModel
29 |
30 | # Create a BedrockModel
31 | bedrock_model = BedrockModel(
32 | model_id="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
33 | region_name='us-west-2',
34 | temperature=0.3,
35 | )
36 |
37 | agent = Agent(model=bedrock_model)
38 | ```
39 |
40 | You will also need to enable model access in Amazon Bedrock for the models that you choose to use with your agents, following the [AWS documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html) to enable access.
41 |
42 | ### Anthropic
43 |
44 | First install the `anthropic` python client:
45 |
46 | ```bash
47 | pip install strands-agents[anthropic]
48 | ```
49 |
50 | Next, import and initialize the `AnthropicModel` provider:
51 |
52 | ```python
53 | from strands import Agent
54 | from strands.models.anthropic import AnthropicModel
55 |
56 | anthropic_model = AnthropicModel(
57 | client_args={
58 | "api_key": "",
59 | },
60 | max_tokens=1028,
61 | model_id="claude-3-7-sonnet-20250219",
62 | params={
63 | "temperature": 0.7,
64 | }
65 | )
66 |
67 | agent = Agent(model=anthropic_model)
68 | ```
69 |
70 | ### LiteLLM
71 |
72 | LiteLLM is a unified interface for various LLM providers that allows you to interact with models from OpenAI and many others.
73 |
74 | First install the `litellm` python client:
75 |
76 | ```bash
77 | pip install strands-agents[litellm]
78 | ```
79 |
80 | Next, import and initialize the `LiteLLMModel` provider:
81 |
82 | ```python
83 | from strands import Agent
84 | from strands.models.litellm import LiteLLMModel
85 |
86 | litellm_model = LiteLLMModel(
87 | client_args={
88 | "api_key": "",
89 | },
90 | model_id="gpt-4o"
91 | )
92 |
93 | agent = Agent(model=litellm_model)
94 | ```
95 |
96 | ### Llama API
97 |
98 | Llama API is a Meta-hosted API service that helps you integrate Llama models into your applications quickly and efficiently.
99 |
100 | First install the `llamaapi` python client:
101 | ```bash
102 | pip install strands-agents[llamaapi]
103 | ```
104 |
105 | Next, import and initialize the `LlamaAPIModel` provider:
106 |
107 | ```python
108 | from strands import Agent
109 | from strands.models.llamaapi import LLamaAPIModel
110 |
111 | model = LlamaAPIModel(
112 | client_args={
113 | "api_key": "",
114 | },
115 | # **model_config
116 | model_id="Llama-4-Maverick-17B-128E-Instruct-FP8",
117 | )
118 |
119 | agent = Agent(models=LLamaAPIModel)
120 | ```
121 |
122 | ### Ollama (Local Models)
123 |
124 | First install the `ollama` python client:
125 |
126 | ```bash
127 | pip install strands-agents[ollama]
128 | ```
129 |
130 | Next, import and initialize the `OllamaModel` provider:
131 |
132 | ```python
133 | from strands import Agent
134 | from strands.models.ollama import OllamaModel
135 |
136 | ollama_model = OllamaModel(
137 | host="http://localhost:11434" # Ollama server address
138 | model_id="llama3", # Specify which model to use
139 | temperature=0.3,
140 | )
141 |
142 | agent = Agent(model=ollama_model)
143 | ```
144 |
145 | ### Custom Model Providers
146 |
147 | We can even connect our agents to custom model providers to use any model from anywhere!
148 |
149 | ```python
150 | from strands import Agent
151 | from your_company.models.custom_model import CustomModel
152 |
153 | custom_model = CustomModel(
154 | model_id="your-model-id
155 | temperature=0.3,
156 | )
157 |
158 | agent = Agent(model=custom_model)
159 | ```
160 |
--------------------------------------------------------------------------------
/src/strands_mcp_server/content/quickstart.md:
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1 | This quickstart guide describes the core concepts of Strands Agents and shows you how to create your first AI agent with Strands Agents.
2 |
3 | The full documentation can be found at https://strandsagents.com.
4 |
5 | ## Concepts
6 |
7 | Strands Agents is a Python SDK for building AI agents. It may also be referred to as simply 'Strands'.
8 |
9 | - Agent: Agents are defined using the Strands SDK. They are primarily defined by specifying 1) a model, 2) tools, and 3) prompts.
10 | - Model: Agents can use any model that supports reasoning and tool use, including models in Amazon Bedrock, Anthropic, and many more through LiteLLM.
11 | The default model in Strands is Anthropic Claude Sonnet 3.7 in Amazon Bedrock in region us-west-2.
12 | - Tools: Tools are the primary mechanism for extending agent capabilities, enabling them to perform actions beyond simple text generation.
13 | Tools allow agents to interact with external systems, access data, and manipulate their environment.
14 | The agent automatically determines when to use tools based on the input query and context.
15 | Strands provides some example tools, supports Model Context Protocol (MCP) servers, and can use any Python function decorated with @tool.
16 | - Prompts: A system prompt and user messages are the primary way to communicate with AI models in an agent using Strands.
17 |
18 | ## Main Python Packages
19 |
20 | The main Strands Agents SDK Python package is `strands-agents`. The SDK package provides the `strands` Python module.
21 |
22 | The Strands Agents SDK additionally offers the `strands-agents-tools` package with many example tools.
23 | The tools package provides the `strands-tools` Python module.
24 |
25 | Your requirements.txt file may look like:
26 |
27 | ```
28 | strands-agents>=0.1.0
29 | strands-agents-tools>=0.1.0
30 | ```
31 |
32 | ## Example Agent
33 |
34 | We'll create an example agent Python project, with this directory structure:
35 |
36 | ```
37 | my_agent/
38 | ├── __init__.py
39 | ├── agent.py
40 | └── requirements.txt
41 | ```
42 |
43 | The `my_agent/__init__.py` file contains:
44 |
45 | ```python
46 | from . import agent
47 | ```
48 |
49 | The `agent.py` file contains:
50 |
51 | ```python
52 | from strands import Agent, tool
53 | from strands_tools import calculator, current_time, python_repl
54 |
55 | # Define a custom tool for the agent as a Python function using the @tool decorator
56 | @tool
57 | def letter_counter(word: str, letter: str) -> int:
58 | """
59 | Count occurrences of a specific letter in a word.
60 |
61 | Args:
62 | word (str): The input word to search in
63 | letter (str): The specific letter to count
64 |
65 | Returns:
66 | int: The number of occurrences of the letter in the word
67 | """
68 | if not isinstance(word, str) or not isinstance(letter, str):
69 | return 0
70 |
71 | if len(letter) != 1:
72 | raise ValueError("The 'letter' parameter must be a single character")
73 |
74 | return word.lower().count(letter.lower())
75 |
76 | # Define the agent
77 | agent = Agent(
78 | # If you provide the model ID as a string, Bedrock is the
79 | # default model provider.
80 | # Remember to ensure you have model access in Bedrock or request it:
81 | # https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html
82 | model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
83 | # Use tools from the strands-tools example tools package
84 | # as well as our custom letter_counter tool
85 | tools=[calculator, current_time, python_repl, letter_counter],
86 | # System prompt for the agent
87 | system_prompt="You are a helpful assistant"
88 | )
89 |
90 | # Ask the agent a question that uses the available tools
91 | message = """
92 | I have 4 requests:
93 |
94 | 1. What is the time right now?
95 | 2. Calculate 3111696 / 74088
96 | 3. Tell me how many letter R's are in the word "strawberry" 🍓
97 | 4. Output a script that does what we just spoke about!
98 | Use your python tools to confirm that the script works before outputting it
99 | """
100 | agent(message)
101 | ```
102 |
103 | ## Running Agents
104 |
105 | Our agent is just Python, so we can run it using any mechanism for running Python!
106 |
107 | To test our agent we can simply run:
108 |
109 | ```bash
110 | python -u my_agent/agent.py
111 | ```
112 |
113 | And that's it! We now have a running agent with powerful tools and abilities in just a few lines of code 🥳.
114 |
115 | ## Debug Logs
116 |
117 | To enable debug logs in our agent, configure the `strands` logger:
118 |
119 | ```python
120 | import logging
121 | from strands import Agent
122 |
123 | # Enables Strands Agents debug log level
124 | logging.getLogger("strands").setLevel(logging.DEBUG)
125 |
126 | # Sets the logging format and streams logs to stderr
127 | logging.basicConfig(
128 | format="%(levelname)s | %(name)s | %(message)s",
129 | handlers=[logging.StreamHandler()]
130 | )
131 |
132 | agent = Agent()
133 |
134 | agent("Hello!")
135 | ```
136 |
137 | ## Capturing Streamed Data & Events
138 |
139 | Strands Agents provides two main approaches to capture streaming events from an agent: async iterators and callback functions.
140 |
141 | ### Async Iterators
142 |
143 | For asynchronous applications (like web servers or APIs), Strands Agents provides an async iterator approach using `stream_async()`. This is particularly useful with async frameworks like FastAPI or Django Channels.
144 |
145 | ```python
146 | import asyncio
147 | from strands import Agent
148 | from strands_tools import calculator
149 |
150 | # Initialize our agent without a callback handler
151 | agent = Agent(
152 | tools=[calculator],
153 | callback_handler=None # Disable default callback handler
154 | )
155 |
156 | # Async function that iterates over streamed agent events
157 | async def process_streaming_response():
158 | query = "What is 25 * 48 and explain the calculation"
159 |
160 | # Get an async iterator for the agent's response stream
161 | agent_stream = agent.stream_async(query)
162 |
163 | # Process events as they arrive
164 | async for event in agent_stream:
165 | if "data" in event:
166 | # Print text chunks as they're generated
167 | print(event["data"], end="", flush=True)
168 | elif "current_tool_use" in event and event["current_tool_use"].get("name"):
169 | # Print tool usage information
170 | print(f"\n[Tool use delta for: {event['current_tool_use']['name']}]")
171 |
172 | # Run the agent with the async event processing
173 | asyncio.run(process_streaming_response())
174 | ```
175 |
176 | The async iterator yields the same event types as the callback handler callbacks, including text generation events, tool events, and lifecycle events. This approach is ideal for integrating Strands Agents agents with async web frameworks.
177 |
178 | See the [Async Iterators](concepts/streaming/async-iterators.md) documentation for full details.
179 |
180 | ### Callback Handlers (Callbacks)
181 |
182 | We can create a custom callback function (named a [callback handler](concepts/streaming/callback-handlers.md)) that is invoked at various points throughout an agent's lifecycle.
183 |
184 | Here is an example that captures streamed data from the agent and logs it instead of printing:
185 |
186 | ```python
187 | import logging
188 | from strands import Agent
189 | from strands_tools import shell
190 |
191 | logger = logging.getLogger("my_agent")
192 |
193 | # Define a simple callback handler that logs instead of printing
194 | tool_use_ids = []
195 | def callback_handler(**kwargs):
196 | if "data" in kwargs:
197 | # Log the streamed data chunks
198 | logger.info(kwargs["data"], end="")
199 | elif "current_tool_use" in kwargs:
200 | tool = kwargs["current_tool_use"]
201 | if tool["toolUseId"] not in tool_use_ids:
202 | # Log the tool use
203 | logger.info(f"\n[Using tool: {tool.get('name')}]")
204 | tool_use_ids.append(tool["toolUseId"])
205 |
206 | # Create an agent with the callback handler
207 | agent = Agent(
208 | tools=[shell],
209 | callback_handler=callback_handler
210 | )
211 |
212 | # Ask the agent a question
213 | result = agent("What operating system am I using?")
214 |
215 | # Print only the last response
216 | print(result.message)
217 | ```
218 |
219 | The callback handler is called in real-time as the agent thinks, uses tools, and responds.
220 |
221 | See the [Callback Handlers](concepts/streaming/callback-handlers.md) documentation for full details.
222 |
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/src/strands_mcp_server/content/tools.md:
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1 | Tools are the primary mechanism for extending agent capabilities, enabling them to perform actions beyond simple text generation. Tools allow agents to interact with external systems, access data, and manipulate their environment.
2 |
3 | ## Adding Tools to Agents
4 |
5 | Tools are passed to agents during initialization or at runtime, making them available for use throughout the agent's lifecycle. Once loaded, the agent can use these tools in response to user requests:
6 |
7 | ```python
8 | from strands import Agent
9 | from strands_tools import calculator, file_read, shell
10 |
11 | # Add tools to our agent
12 | agent = Agent(
13 | tools=[calculator, file_read, shell]
14 | )
15 |
16 | # Agent will automatically determine when to use the calculator tool
17 | agent("What is 42 ^ 9")
18 |
19 | # Agent will use the shell and file reader tool when appropriate
20 | agent("Show me the contents of a single file in this directory")
21 | ```
22 |
23 | ## Building & Loading Tools
24 |
25 | ### 1. Python Tools
26 |
27 | Build your own Python tools using the Strands SDK's tool interfaces.
28 |
29 | Function decorated tools can be placed anywhere in your codebase and imported in to your agent's list of tools. Define any Python function as a tool by using the [`@tool`](../../../api-reference/tools.md#strands.tools.decorator.tool) decorator.
30 |
31 | ```python
32 | from strands import Agent, tool
33 |
34 | @tool
35 | def get_user_location() -> str:
36 | """Get the user's location
37 | """
38 |
39 | # Implement user location lookup logic here
40 | return "Seattle, USA"
41 |
42 | @tool
43 | def weather(location: str) -> str:
44 | """Get weather information for a location
45 |
46 | Args:
47 | location: City or location name
48 | """
49 |
50 | # Implement weather lookup logic here
51 | return f"Weather for {location}: Sunny, 72°F"
52 |
53 | agent = Agent(tools=[get_user_location, weather])
54 |
55 | # Use the agent with the custom tools
56 | agent("What is the weather like in my location?")
57 | ```
58 |
59 | ### 2. Model Context Protocol (MCP) Tools
60 |
61 | The [Model Context Protocol (MCP)](https://modelcontextprotocol.io) provides a standardized way to expose and consume tools across different systems. This approach is ideal for creating reusable tool collections that can be shared across multiple agents or applications.
62 |
63 | ```python
64 | from mcp import stdio_client, StdioServerParameters
65 | from strands import Agent
66 | from strands.tools.mcp import MCPClient
67 |
68 | # Connect to an MCP server using stdio transport
69 | stdio_mcp_client = MCPClient(lambda: stdio_client(
70 | StdioServerParameters(command="uvx", args=["awslabs.aws-documentation-mcp-server@latest"])
71 | ))
72 |
73 | # Create an agent with MCP tools
74 | with stdio_mcp_client:
75 | # Get the tools from the MCP server
76 | tools = stdio_mcp_client.list_tools_sync()
77 |
78 | # Create an agent with these tools
79 | agent = Agent(tools=tools)
80 | ```
81 |
82 | ### 3. Example Built-in Tools
83 |
84 | Strands offers an optional example tools package `strands-agents-tools` which includes pre-built tools to get started quickly experimenting with agents and tools during development.
85 |
86 | Install the `strands-agents-tools` package by running:
87 |
88 | ```bash
89 | pip install strands-agents-tools
90 | ```
91 |
92 | ## Available Built-In Strands Tools
93 |
94 | #### RAG & Memory
95 |
96 | - `retrieve`: Semantically retrieve data from Amazon Bedrock Knowledge Bases for RAG, memory, and other purposes
97 |
98 | #### File Operations
99 |
100 | - `editor`: Advanced file editing operations
101 | - `file_read`: Read and parse files
102 | - `file_write`: Create and modify files
103 |
104 | #### Shell & System
105 |
106 | - `environment`: Manage environment variables
107 | - `shell`: Execute shell commands
108 |
109 | #### Code Interpretation
110 |
111 | - `python_repl`: Run Python code
112 |
113 | #### Web & Network
114 |
115 | - `http_request`: Make API calls, fetch web data, and call local HTTP servers
116 |
117 | #### Multi-modal
118 |
119 | - `image_reader`: Process and analyze images
120 | - `generate_image`: Create AI generated images with Amazon Bedrock
121 | - `nova_reels`: Create AI generated videos with Nova Reels on Amazon Bedrock
122 |
123 | #### AWS Services
124 |
125 | - `use_aws`: Interact with AWS services
126 |
127 | #### Utilities
128 |
129 | - `calculator`: Perform mathematical operations
130 | - `current_time`: Get the current date and time
131 | - `load_tool`: Dynamically load more tools at runtime
132 |
133 | #### Agents & Workflows
134 |
135 | - `agent_graph`: Create and manage graphs of agents
136 | - `journal`: Create structured tasks and logs for agents to manage and work from
137 | - `swarm`: Coordinate multiple AI agents in a swarm / network of agents
138 | - `stop`: Force stop the agent event loop
139 | - `think`: Perform deep thinking by creating parallel branches of agentic reasoning
140 | - `use_llm`: Run a new AI event loop with custom prompts
141 | - `workflow`: Orchestrate sequenced workflows
142 |
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/src/strands_mcp_server/server.py:
--------------------------------------------------------------------------------
1 | from importlib import resources
2 |
3 | from mcp.server.fastmcp import FastMCP
4 |
5 | pkg_resources = resources.files("strands_mcp_server")
6 |
7 | mcp = FastMCP(
8 | "strands-agents-mcp-server",
9 | instructions="""
10 | # Strands Agents MCP Server
11 |
12 | This server provides tools to access Strands Agents documentation.
13 | Strands Agents is a Python SDK for building AI agents.
14 | It may also be referred to as simply 'Strands'.
15 |
16 | The full documentation can be found at https://strandsagents.com.
17 | """,
18 | )
19 |
20 |
21 | @mcp.tool()
22 | async def quickstart() -> str:
23 | """Quickstart documentation for Strands Agents SDK."""
24 | return pkg_resources.joinpath("content", "quickstart.md").read_text(
25 | encoding="utf-8"
26 | )
27 |
28 |
29 | @mcp.tool()
30 | async def model_providers() -> str:
31 | """Documentation on using different model providers in Strands Agents."""
32 | return pkg_resources.joinpath("content", "model_providers.md").read_text(
33 | encoding="utf-8"
34 | )
35 |
36 |
37 | @mcp.tool()
38 | async def agent_tools() -> str:
39 | """Documentation on adding tools to agents using Strands Agents."""
40 | return pkg_resources.joinpath("content", "tools.md").read_text(encoding="utf-8")
41 |
42 |
43 | def main():
44 | mcp.run()
45 |
--------------------------------------------------------------------------------