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Glama

Fmcsa

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds specific behavioral details: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, and a 200K char cap with truncation flag. No contradiction.

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

Conciseness5/5

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

Concise with three well-structured sentences. Front-loaded with core purpose, then usage guidance, then technical details. No wasted words.

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?

No output schema, but description explicitly states return values: top-N passages with character offsets and similarity scores. Also mentions pairing with grounded tool, providing complete context.

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%. Description adds limited value beyond schema: for 'text' it adds context ('the text you already pulled'), for 'query' gives examples, and for 'limit' mentions default 5. Baseline 3 due to high coverage.

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?

The description clearly states 'Semantic search INSIDE a fetched record' with specific examples like SEC 10-K and articles. It distinguishes itself by explaining how it pairs with ask_pipeworx_grounded, a sibling tool, for grounding over passages.

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

Usage Guidelines4/5

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

Explicitly tells when to use: 'when the record is too big to cram into the prompt' and that it saves context. Provides pairing guidance with grounded tool, though does not explicitly state when not to use.

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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TDQS

A3.8/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is notable overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, as are bet_research and polymarket_edges. Detailed descriptions help, but an agent could still struggle to pick the right one.

Naming Consistency2/5

Naming conventions are mixed: snake_case (fmcsa_carrier_lookup), verb_noun (ask_pipeworx, recall), and phrases (suggest_questions, generate_llms_txt). No consistent pattern, making it harder for an agent to infer tool purpose from name alone.

Tool Count3/5

35 tools is high for a single server, but the server aggregates many domains. While each tool may serve a purpose, the count feels bloated and beyond typical scope (3-15). Some tools could be merged (e.g., the ask_pipeworx variants).

Completeness2/5

For the FMCSA domain, the four tools provide decent coverage. However, the server includes many tools for other domains (e.g., prediction markets, company profiles) without full lifecycle support (e.g., only lookup, no create/update). The set feels like a random collection rather than a coherent domain.

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