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Lyrics Ovh

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 internal behavior: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation and flagging. This goes beyond annotations which already provide readOnlyHint, idempotentHint, etc. 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?

Four sentences, each essential. Front-loaded with core purpose, then usage, then technical details. No redundancy or fluff.

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?

The description covers functionality, use case, pairing, algorithm, limits, and output format, making it fully actionable despite no output schema.

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 description coverage is 100%, so baseline is 3. The description adds value by providing example queries for the 'query' parameter and clarifying the truncation behavior for 'text'. The limit parameter is mentioned in the output description.

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 verb 'search inside' a fetched record, specifies the resource (text), and describes the output (top-N passages with offsets and similarity scores). It distinguishes from siblings by explicitly 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 explains how it saves context. Also provides guidance on pairing with ask_pipeworx_grounded for grounding over relevant passages.

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
Disambiguation3/5

Several tools have overlapping purposes. ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route to the same 5,564 tools, differing only in grounding behavior. suggest and lyrics both look up music but in different ways; suggest is broader while lyrics is exact. The core tools are distinct, but the multiple pipeworx variants and music tools create ambiguity.

Naming Consistency2/5

Naming is highly inconsistent. Most tools use snake_case (ask_pipeworx, entity_profile, compare_entities), but several use verb phrases (generate_llms_txt, scan_competitor_ai_presence) and some use short nouns (lyrics, suggest). There's no consistent verb_noun pattern; 'ask_pipeworx' variants mix imperative with domain words, and 'recall'/'remember' are verbs without objects.

Tool Count4/5

With 33 tools, the server covers a wide domain (company research, prediction markets, news, weather, lyrics, memory, subscriptions, etc.). While this is many tools, each has a specific purpose and the variety matches the stated 'universal router' / 'thousands of data sources' value prop. It could be trimmed slightly, but the count is justified by the breadth.

Completeness5/5

The tool surface is remarkably complete for its stated purpose: unstructured lookup (ask_pipeworx), grounded verification (ask_pipeworx_grounded), deep multi-source research (deep_research), entity profiles, comparisons, change feeds, arbitrage scanning, memory, subscriptions, and even feedback/governance tools. It covers all common patterns in data retrieval and has distinct tools for edge cases, making it hard to find obvious gaps.

Resources