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Glama

Mime

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. Description adds significant behavioral details: 'BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).' No contradictions.

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?

A single dense paragraph of about 100 words, front-loaded with core purpose. Every sentence adds value: purpose, use case, pairing, technical details. No filler.

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 explains return values: 'top-N passages with character offsets and similarity scores'. Covers embedding model, chunking, character limit, and truncation behavior. Complete for a semantic search tool with good annotations.

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

Parameters5/5

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

Schema coverage is 100% with descriptions for each parameter. The description adds more context: 'Pass the text you already pulled', 'Max passages to return (1-20, default 5)', and natural-language query examples. This enriches the meaning beyond the schema.

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

Purpose4/5

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

The description clearly states it performs semantic search inside a fetched record, with examples like SEC 10-K body. It explains the output (passages with offsets and scores). Although it doesn't explicitly differentiate from all sibling tools, the context of searching within a specific text is clear.

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: 'when the record is too big to cram into the prompt'. Provides an alternative: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document.'

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

A4/5.0
Disambiguation3/5

Several tools have overlapping purposes, such as the ask_pipeworx family (standard, beta, grounded) and the multiple Polymarket analysis tools (edges, arbitrage, fill_risk, edge_tracker, kalshi_spread, bet_research). The detailed descriptions help differentiate them, but an agent could still misselect between deep_research vs ask_pipeworx or polymarket_edges vs polymarket_arbitrage.

Naming Consistency3/5

Naming patterns are mixed: many tools use verb_noun (discover_tools, validate_claim, compare_entities), but others are noun_noun (entity_profile, polymarket_edges), single verbs (remember, recall, forget), or unusual forms (extension_for, search_within, ask_pipeworx). The polymarket_ prefix and ask_pipeworx family provide some consistency, but overall the style is not uniform.

Tool Count2/5

33 tools is a large number for the server's scope. While it covers many domains (data querying, entity research, Polymarket analysis, subscriptions, memory, utilities), there is redundancy: three ask_pipeworx variants and six Polymarket-specific tools inflate the count. Several tools could be merged or dropped without losing functionality, making the set feel heavier than necessary.

Completeness4/5

The toolset covers its core domains well: data querying (ask_pipeworx, deep_research), entity resolution (resolve_entity, entity_profile, compare_entities), Polymarket analysis (research, arbitrage, risk, edges, tracking, cross-venue), subscriptions (subscribe/unsubscribe/list/alerts), memory (remember/recall/forget), and utilities (MIME lookup, dependency scan). Minor gaps exist, such as no direct fetch tool for pipeworx:// citation URIs and no update operation for subscriptions, but these are not critical.

Resources