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Lyrics Ovh

by io.github.pipeworx-io

Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable behavioral context: it pulls real-time SEC/FAERS data, handles fiscal years correctly, sorts results, and returns citation URIs. This goes 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?

The description is informative but slightly verbose. However, it front-loads key trigger phrases and usage guidance. Nearly every sentence adds value, but a minor trim could improve conciseness.

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?

Despite no output schema, the description explains the return format (paired data + citation URIs) and sorting behavior. Given the simple parameter structure and high schema coverage, the description is complete enough for correct agent invocation.

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 significant meaning: it explains the enum values (company vs drug), specifies how to format values (tickers/CIKs vs drug names), and limits array to 2-5 items. It also describes what data each type returns.

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 side-by-side comparison of 2-5 companies or drugs. It uses specific verbs like 'compare' and lists trigger phrases, distinguishing it from single-entity tools like entity_profile.

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 states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and provides trigger phrases. It also explains data sources per type, guiding when to use each variant.

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