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Glama
Strands Agents MCP Server

Strands Agents MCP Server

Official

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
search_docsA

Search the curated documentation catalog.

The catalog contains Strands Agents user guides, API reference pages, and examples. Ranking starts from document titles and also considers page content once a page has been fetched (hydrated), so both title-like keywords ("bedrock model", "MCP tools") and body terms ("guardrail", "temperature") work. The top results are fetched on demand to hydrate the index and provide content snippets.

Call fetch_doc with a result URL to read the page or one of its sections.

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: Unbounded weighted TF-IDF score. Higher is better within this result set; do not interpret it as a probability or compare it across different queries. - snippet: Contextual content preview

Returns an empty list when no document matches. If a result page
cannot be fetched, its title is used as the snippet.
fetch_docA

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

Errors are returned for unsupported URLs and failed page fetches. For
sectioned documents, unknown section IDs also return an error; `section`
is ignored when the full document is returned automatically.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A4.5/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: search_docs discovers relevant documentation URLs, while fetch_doc reads a specific page or section. The descriptions explicitly document the recommended search-then-fetch workflow, leaving little ambiguity.

Naming Consistency5/5

Both tool names follow a consistent verb_noun snake_case pattern: search_docs and fetch_doc. The slight singular/plural difference is natural and does not create confusion.

Tool Count4/5

Two tools is slightly below the typical 3-15 range, but for a focused documentation search-and-read server this is a clean, well-scoped surface. Each tool clearly earns its place.

Completeness5/5

The server covers the full retrieval lifecycle: search for documents, list catalog URLs, inspect document structure via TOC, and read specific sections or full small documents. No obvious documentation-access gaps remain.

Maintenance

ActivityActive
ResponsivenessWithin a week