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Glama

Cdc

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, idempotentHint, and non-destructive. The description adds significant behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), handling of off-calendar fiscal years, sorting by primary metric, and inclusion of citation URIs.

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 lengthy but every sentence serves a purpose. It is front-loaded with trigger phrases and structured logically. Minor opportunity to trim redundancy (e.g., 'off-calendar fiscal years handled correctly' could be more concise).

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 lacking an output schema, the description explains return format (paired data + citation URIs) and behavior (sorted by primary metric). For a comparison tool, all necessary context is provided, including efficiency gains (replaces 8-15 sequential lookups).

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?

Schema covers 100% of parameters. The description enriches the 'type' and 'values' parameters by specifying exact data retrieved per type (revenue, net income, cash, debt for companies; adverse events, approvals, trials for drugs) and constraints (2-5 items, tickers vs names).

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 in a single call, distinguishing it from sequential lookups. Specific trigger phrases like 'compare X and Y' and 'head to head' are provided, making the purpose unmistakable.

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?

Explicit instructions are given: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Examples of usage ('which is bigger / better / larger / more profitable' / 'rank these companies') further clarify when to invoke this tool.

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

A3.9/5.0
Disambiguation3/5

Tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research serve similar querying purposes, and there is overlap among prediction market tools (e.g., polymarket_arbitrage, polymarket_edges). However, descriptions help differentiate them, so agents can usually select the correct one.

Naming Consistency3/5

Most tools use snake_case with verbs (ask_, resolve_, validate_), but there are noun-style exceptions (entity_profile, recent_changes, pipeworx_trending) and mixed naming among Polymarket tools. The pattern is readable but not fully consistent.

Tool Count2/5

With 32 tools, the server feels bloated, especially given the name 'Cdc' implies a focus on CDC data, yet many tools cover unrelated domains like prediction markets and company profiles. Several tools could be consolidated or removed to align with a narrower scope.

Completeness3/5

The CDC domain is thinly covered with only search and get for datasets, lacking upload or advanced filtering. Company financials are limited to basic fundamentals from 10-Ks. Prediction markets are well-covered with arbitrage, edges, and fill risk. The server has notable gaps in its core domain.

Resources