{"id":"strands-agents-mcp-server","name":"mcp-server","homepage":"https://strandsagents.com","repo_url":"https://github.com/strands-agents/mcp-server","category":"devtools","subcategories":[],"tags":["mcp","documentation","retrieval","rag","agentic-ai","python"],"what_it_does":"Provides an MCP server that exposes curated documentation (from llms.txt sources) to GenAI coding assistants. It enables searching relevant docs with relevance ranking, browsing document sections via a table-of-contents style interface, and fetching only the needed sections (plus contextual snippet generation).","use_cases":["RAG-style retrieval of documentation for agent-assisted coding","Letting IDE coding assistants query relevant tool/library docs via MCP","Token-efficient doc browsing by section and on-demand fetching","Generating short contextual snippets for faster agent decisions"],"not_for":["General-purpose knowledge base for arbitrary user content","Performing authenticated access to private resources (no auth mechanism described)","Replacing full documentation websites or complex multi-page navigation"],"best_when":"You want an MCP-compatible way to let agentic IDE tools find and retrieve the right documentation sections quickly, with lazy loading and doc-structure awareness.","avoid_when":"You need a fully-featured web crawler, strict guarantees about freshness of upstream docs, or require authenticated access control for private content.","alternatives":["Directly query documentation websites using a standard web search + fetch pipeline","Use a general-purpose RAG service (vector DB + ingestion pipeline) tailored to your docs","Custom MCP server exposing your own internal docs and retrieval logic","Use an MCP inspector/client integration with other doc-focused MCP servers"],"af_score":63.5,"security_score":32.8,"reliability_score":25.0,"package_type":"mcp_server","discovery_source":["github"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-30T13:34:56.950273+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":true,"mcp_server_url":null,"has_sdk":false,"sdk_languages":[],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":[],"oauth":false,"scopes":false,"notes":"No authentication/authorization requirements are described for using the MCP server."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"Pricing not described; appears to be distributed as an open-source Python package and run locally via MCP clients."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":63.5,"security_score":32.8,"reliability_score":25.0,"mcp_server_quality":75.0,"documentation_accuracy":70.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":100.0,"rate_limit_clarity":20.0,"tls_enforcement":60.0,"auth_strength":10.0,"scope_granularity":0.0,"dependency_hygiene":55.0,"secret_handling":50.0,"security_notes":"No authentication/authorization is described, so access control is effectively absent from the described interface. The README indicates support for HTTPS URLs when indexing, but does not discuss SSRF protections, URL allowlisting, or sandboxing. TLS usage is implied by HTTPS support for fetched URLs, but enforcement details for MCP communication are not specified. Dependency hygiene cannot be fully assessed from the provided manifest (only high-level deps: mcp, pydantic).","uptime_documented":0.0,"version_stability":40.0,"breaking_changes_history":30.0,"error_recovery":30.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["Behavior depends on the curated indexing sourced from llms.txt; if llms.txt inputs are incomplete, retrieval quality will be limited.","The server uses search/ranking and lazy loading; agents may need to call both search and fetch tools to obtain full section text."]}}