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Glama

Omdb

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?

Discloses embedding model (BGE-base-en), similarity metric (cosine), window size (500-char overlapping), character cap (200K) with truncation and flagging, and output details (offsets, similarity scores). Adds value beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Information-dense single paragraph with no fluff, but could benefit from structured bullet points for easier scanning. Still very concise relative to 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?

Given 3 parameters, no output schema, the description fully explains output structure and technical details, leaving no gaps for an agent to operate.

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?

With 100% schema coverage, the description adds examples for query, clarifies max chars for text, and gives default and range for limit, enhancing understanding 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 the tool performs semantic search inside a fetched record, with concrete examples (SEC 10-K, article) and distinguishes from siblings 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 says 'Use when the record is too big to cram into the prompt' and recommends pairing with ask_pipeworx_grounded, providing clear when-to-use and alternatives.

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, such as ask_pipeworx, ask_pipeworx_grounded, and deep_research all answering questions, and bet_research overlapping with polymarket_edges/arbitrage. The diverse tool set lacks clear boundaries.

Naming Consistency3/5

All names use snake_case, but verb patterns are inconsistent: some start with verbs (ask_pipeworx, remember) while others start with nouns (polymarket_arbitrage, entity_profile). Names vary widely in length and descriptiveness.

Tool Count2/5

33 tools is excessive for a server named 'Omdb', which implies a movie database. Most tools are unrelated to movies, indicating poor scoping for the server's purpose.

Completeness2/5

For the OMDb domain, only search and retrieval tools exist, with no create/update/delete operations. Overall, the tool set feels like a random collection with significant gaps relative to any coherent domain.

Resources