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Glama

Tmdb

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 (readOnly, idempotent, etc.), description adds details: BGE-base-en embeddings, 500-char overlapping windows, cosine similarity, 200K char cap with truncation flag. No contradiction.

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?

Single well-structured paragraph, front-loaded with main purpose, each sentence adds value. No unnecessary words.

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?

Covers embedding model, chunking, truncation, output content (passages, offsets, scores). No output schema needed given description completeness.

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%, but description adds value with context: text max, natural-language query examples, default limit. Exceeds baseline.

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?

Description clearly states it performs semantic search inside a fetched record, with specific examples (SEC 10-K, article) and a workflow pattern. Distinguishes from sibling 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use (record too big for prompt) and mentions pairing with ask_pipeworx_grounded. Could be more explicit about when not to use, but context is clear.

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

B3.4/5.0
Disambiguation2/5

The tool set blends two unrelated domains (TMDB and Pipeworx). Among Pipeworx tools, several overlap heavily (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, suggest_questions) with vague boundaries, making it hard for an agent to pick the right one. TMDB tools are distinct but the overall mixture creates confusion about which domain a request belongs to.

Naming Consistency3/5

All tools use snake_case, which is consistent. However, naming styles vary widely: TMDB tools use simple noun or verb-first names (movie, search_movie, discover_tv), while Pipeworx tools use longer descriptive phrases with prefixes (ask_pipeworx, polymarket_arbitrage, entity_profile). The pattern is not predictable across the set.

Tool Count2/5

50 tools is excessive for a server named 'Tmdb'. Only about 18 tools are actually TMDB-related; the remaining 32 belong to the Pipeworx ecosystem. This inflates the count and makes the server feel bloated and unfocused, far beyond a well-scoped TMDB server.

Completeness3/5

The TMDB portion is quite complete (search, discover, details, credits, recommendations, trending, genres, configuration). However, the server's overall scope is muddled—it tries to cover two disjoint domains, so no single domain feels fully fleshed out. There are also some missing TMDB features (e.g., upcoming/now playing) that would require extra discovery. The Pipeworx tools cover data broadly but overlap in coverage.

Resources