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by strands-agents • Uncategorized
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.
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.
Search curated documentation and return ranked results with snippets. This tool provides access to the complete Strands Agents documentation including: **User Guide Topics:** - Agent concepts (agent loop, conversation management, hooks, prompts, state) - Model providers (Amazon Bedrock, Anthropic, Cohere, LiteLLM, LlamaAPI, MistralAI, Ollama, OpenAI, SageMaker, Writer, Gemini) - Multi-agent patterns (Agent2Agent, Agents as Tools, Graph, Swarm, Workflow) - Tools (Python tools, MCP tools, community tools, executors) - Deployment guides (EC2, EKS, Fargate, Lambda, Bedrock AgentCore) - Observability & evaluation (logs, metrics, traces, evaluation) - Safety & security (guardrails, PII redaction, responsible AI) **API Reference:** - Complete API documentation for Agent, Models, Tools, Handlers, etc. **Examples:** - Code samples and implementation examples Use this to find relevant Strands Agents documentation for any development question. Args: query: Search query string (e.g., "bedrock model", "tell me about a2a", "how to use MCP tools") k: Maximum number of results to return (default: 5) Returns: List of dictionaries containing: - url: Document URL - title: Display title - score: Relevance score (0-1, higher is better) - snippet: Contextual content preview
Read documentation pages with smart sectioning for token efficiency. Two modes of operation: 1. **TOC mode** (omit section): Returns a table of contents with section IDs, titles, summaries, and the document's preamble so you can decide which part to read. 2. **Section mode** (provide section): Returns the full markdown content of one section, identified by the ID from the TOC (e.g., "3" or "3.2"). For small documents (under ~8KB), the full content is returned directly regardless of mode, since sectioning would add overhead without benefit. Recommended workflow: 1. search_docs("your query") - find relevant URLs 2. fetch_doc(uri="...") - see structure, preamble, and section summaries 3. fetch_doc(uri="...", section="3") - read the section you need Args: uri: Document URL (must be under https://strandsagents.com/). If empty, returns a catalog of all available document URLs with titles. section: Section ID from the TOC (e.g., "3" or "3.2"). Omit to get the table of contents. Returns: When section is omitted (TOC mode): - url, title: Document metadata - preamble: Introductory text between page title and first section - sections: List of sections with id, level, title, summary, children When section is provided: - url, title: Document metadata - section_id: Requested section ID - section_title: Section heading text - content: Full markdown content of the section For small documents (under ~8KB): - url, title: Document metadata - document_small: true - content: Full document content (returned automatically) On error: - error: Error description - url: Requested URL
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