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Glama

Fas

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

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description adds critical details: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flagging, and that each passage carries a character offset. This fully discloses behavior.

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, well-structured paragraph that front-loads the core purpose and then adds important details about use cases, technical specifics, and pairing with siblings. Every sentence provides value without redundancy.

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?

Given no output schema, the description fully explains what the tool returns (top-N passages, character offsets, similarity scores). It covers the cap, truncation behavior, embedding model, and even how it pairs with ask_pipeworx_grounded. This is complete for the tool's complexity.

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%, and the description adds meaning beyond property names: it explains 'text' as document text you already pulled, 'query' as natural-language with examples, and 'limit' as max passages. It also describes the output (passages with offsets and scores), which enhances understanding.

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 (SEC 10-K, article), and explains the tool returns top-N passages with offsets and similarity scores. It distinguishes itself by noting it saves context and allows quote verification, which sets it apart from general search tools.

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?

The description explicitly says 'Use when the record is too big to cram into the prompt' and pairs the tool with ask_pipeworx_grounded for grounding over passages. While it doesn't list when not to use it, the positive guidance is clear and actionable.

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
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., ask_pipeworx vs. deep_research vs. validate_claim). However, a few pairs like ask_pipeworx_beta vs. ask_pipeworx and validate_claim vs. ask_pipeworx_grounded have overlapping roles, even though descriptions do differentiate them.

Naming Consistency4/5

All tool names use snake_case consistently, and many follow a verb_noun pattern (ask_pipeworx, compare_entities, subscribe). Some exceptions like entity_profile, recent_alerts, and pipeworx_trending break the strict verb_noun pattern but remain readable and stylistically uniform.

Tool Count3/5

With 35 tools, this is above the typical 'well-scoped' range and exceeds the 25-tool threshold for heavy servers. However, the server covers a very broad domain (financial data, prediction markets, agriculture, AI visibility, memory, subscriptions), which partially justifies the count, but it still feels bloated.

Completeness4/5

The tool surface covers all major workflows: data querying (ask_pipeworx, deep_research), entity resolution and comparison, prediction-market analysis, agricultural data (FAS tools), memory (remember/recall/forget), and subscription management (subscribe/unsubscribe/list). The only minor gap is lack of direct write/update operations for external data, but that's not expected for a read-heavy platform.

Resources