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Glama

Grants Gov

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations indicate readOnlyHint and idempotentHint, which the description does not contradict. The description adds significant behavioral details: uses BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, and a 200K character cap with truncation flag. This goes well beyond the annotations.

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?

The description is a single, dense paragraph that front-loads the core purpose and then efficiently covers usage, output, constraints, and internal details. No superfluous words; every sentence adds value.

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?

Despite no output schema, the description fully explains the return value (passages with character offsets and similarity scores). It covers input constraints (200K chars, truncation), pairing with a sibling, and the embedding model. For a moderately complex tool, this is comprehensive.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. Description adds value by clarifying the 'text' parameter's maximum length (200K chars) and providing example queries for the 'query' parameter. It also states default for 'limit' (5), which is not in the schema description.

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' and gives concrete examples (SEC filing, article, tool result). It specifies the action (search), resource (a record's text), and the output (top-N passages with offsets and scores). This differentiates it from siblings, especially by mentioning pairing with ask_pipeworx_grounded.

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 states when to use: 'Use when the record is too big to cram into the prompt'. It explains the benefits (saves context, returns only relevant passages, provides offsets for verification). It also guides on pairing with ask_pipeworx_grounded, giving clear usage context.

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

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes: ask_pipeworx/ask_pipeworx_grounded/deep_research all handle broad queries; multiple Polymarket tools exist for edge finding; entity_profile/compare_entities/recent_changes/resolve_entity overlap on company data. An agent would struggle to pick the right tool.

Naming Consistency2/5

Naming patterns are mixed: some use verb_noun (search_opportunities, get_opportunity), some are phrases (ask_pipeworx_grounded, polymarket_edge_tracker), and some are vague (recall, forget). No consistent convention across the set.

Tool Count2/5

32 tools is high, but the core issue is that the server name 'Grants Gov' implies a narrow focus, yet only 2 tools are about grants. The sheer number of unrelated tools makes the set feel bloated and unfocused.

Completeness3/5

For the actual domain of general data querying and prediction markets, the tool surface is fairly complete, covering many sources. However, for 'Grants Gov' it is severely incomplete (missing all but opportunities). Overall, the scope is broad but lacks depth in any one area.

Resources