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Glama

Bcb Br

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?

Adds non-obvious implementation details: BGE-base-en embeddings, 500-char overlapping windows, 200K char cap with truncation flag. These complement the readOnlyHint since the operation is non-destructive and idempotent, but the description goes beyond annotations by explaining the mechanics and edge case.

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?

Multiple sentences but each carries distinct information: purpose, input/output, use case, pairing, and technical constraints. Front-loaded with the core function, 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?

For a tool with no output schema, the description clearly states the return type: top-N passages with offsets and similarity scores. It also covers the truncation behavior and the rationale for using it. This is sufficient for an agent to invoke correctly.

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 already provides full coverage of all three parameters. The description adds useful contextual examples (e.g., 'supply-chain risk') and clarifies that text is the fetched record, but doesn't introduce new parameter meaning beyond the schema; still earns a 4 for enhancing usability.

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 it performs semantic search inside a fetched record, using examples like SEC 10-K and articles. It differentiates from sibling search tools by emphasizing it searches within a provided text rather than external sources.

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 'Use when the record is too big to cram into the prompt' and frames it as a context-saving measure. Names the complementary tool ask_pipeworx_grounded, showing when to pair with it.

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

A4.4/5.0
Disambiguation5/5

Each tool has a distinct purpose; even similar tools like ask_pipeworx and ask_pipeworx_grounded are clearly differentiated by grounding behavior. Polymarket tools are separated by specific angles (arbitrage, edges, tracking, fill risk, cross-venue).

Naming Consistency5/5

All tool names use consistent snake_case with descriptive verbs (ask_, compare_, discover_, generate_, list_, recall_, etc.). No mixing of camelCase or other conventions.

Tool Count4/5

35 tools is on the higher end but justified by the breadth of functionality: Brazilian economics, Pipeworx data querying, company analysis, Polymarket betting, memory, subscriptions, etc. Each tool seems necessary, though a few could potentially be consolidated.

Completeness4/5

The tool set covers major CRUD operations and data retrieval across multiple domains. Minor gaps exist (e.g., no tool to edit subscriptions directly, but unsubscribe/resubscribe works). Overall, the surface is well-rounded for the stated purposes.

Resources