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Glama

Youtube

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
Behavior4/5

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds technical details (BGE-base-en embeddings, cosine, 500-char windows, 200K char limit, truncation with flag) and explains output features (offsets, similarity scores).

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?

Four sentences, each with distinct value. First sentence states purpose, second gives usage guidance, third elaborates on benefits and pairing, fourth provides technical specifics. No 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, description fully explains what is returned (passages, character offsets, similarity scores), input constraints (200K char limit, truncation), and how it pairs with other tools. Comprehensive for decision-making.

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 has 100% coverage with descriptions. Description adds real-world examples for text and query, and explains the return format (top-N passages with offsets and scores), going beyond schema.

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, with specific examples (SEC 10-K, article, long tool result). It distinguishes from siblings by indicating when to use this over other tools ('use when the record is too big to cram into the prompt').

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 advises when to use (when record is too large for prompt) and pairs with ask_pipeworx_grounded for a recommended workflow. Provides strong guidance on context saving and passage retrieval.

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
Disambiguation3/5

There is meaningful overlap among the ask_pipeworx, deep_research, validate_claim, and polymarket_* tools, but the descriptions do draw fairly clear boundaries between them. The five yt_* tools are distinct and easy to tell apart, though the unrelated Pipeworx cluster makes the overall set feel muddier than it should.

Naming Consistency3/5

Most tools use readable snake_case, and there are coherent prefixes like yt_ and polymarket_, but the set mixes bare verbs (remember, recall, forget, subscribe), noun-style names (entity_profile, deep_research), and API-like names (ask_pipeworx, generate_llms_txt). The pattern is not chaotic, but it is inconsistent across the set.

Tool Count2/5

36 tools is well above the typical well-scoped range, and the vast majority are unrelated to the server's stated 'Youtube' identity. The actual YouTube surface is only five tools, while 31 tools belong to a different Pipeworx/Polymarket domain.

Completeness2/5

For a YouTube-focused server, the yt_* tools cover search, channel info, video details, and comments, but miss obvious surfaces like playlists, transcripts, subscriptions, uploads, and video updates. The large non-YouTube tool collection does not fill these gaps; it only makes the server feel mis-scoped.

Resources