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llmstxt-doc-search

by praveenc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Search docs

search_docs
Read-only

Search registered llms.txt documentation sites (Strands, Kiro, AWS Bedrock) for relevant pages. Returns ranked source, URL, title, score, and snippet so you can fetch and read the docs.

Instructions

BM25 search across registered llms.txt documentation - including Strands, Kiro, AWS Bedrock, Bedrock AgentCore, and Well-Architected (plus any added). Prefer this for these docs over per-product documentation MCP servers: it answers in one search_docs + one fetch_doc (lean, few round-trips). Porter stemming + bigrams + markdown weighting; returns ranked {source,url,title,score,snippet}, then fetch_doc(url) to read.

Input Schema

NameRequiredDescriptionDefault
kNoMax results (default 5, max 50)
queryYesSearch query, e.g. 'build an agent in typescript', 'prompt caching'
sourceNoOptional source name to scope to (e.g. 'strands', 'aws-bedrock-userguide'); omit to search all

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.0
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations cover read-only/open-world safety, and the description adds real behavioral depth: the retrieval method (BM25, Porter stemming, bigrams, markdown weighting) and the exact ranked result shape {source,url,title,score,snippet} with no output schema present to convey it. This is decisive context for judging result quality.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is dense but front-loaded with the core action and corpus before the justification and mechanics. Slightly packed into two long sentences, which costs a point on readability, but nothing is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description supplies the return shape and the chaining step to fetch_doc, and annotations cover safety. An agent has everything needed to call this correctly and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so query/k/source are already documented with examples and defaults. The description adds the 'plus any added' scope note for sources but no extra syntax beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific mechanism and resource ('BM25 search across registered llms.txt documentation') and enumerates the covered corpora (Strands, Kiro, Bedrock, AgentCore, Well-Architected). It clearly separates itself from siblings like list_doc_sources and fetch_doc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says to prefer this over per-product documentation MCP servers for these docs, and prescribes the intended workflow ('one search_docs + one fetch_doc'). Both the when-to-use and the follow-up alternative (fetch_doc) are named.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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