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Glama

Owid

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

A4.6/5.0
Behavior5/5

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

The description discloses significant behavioral traits: it makes one parallel call, handles off-calendar fiscal years correctly, sorts results by primary metric, and returns paired data with citation URIs. This adds rich context beyond annotations (readOnlyHint, openWorldHint, idempotentHint). No contradictions.

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?

The description is concise (around 8 sentences), front-loaded with purpose and usage triggers, and every sentence adds value. No redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 2 required parameters and no output schema, the description covers key aspects: data sources, sorting, and return format (paired data + citations). It could specify the output structure more, but it is sufficiently complete for the tool's complexity.

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 the description adds meaning by explaining that type='company' pulls specific financial data from SEC EDGAR/XBRL and type='drug' pulls FAERS, FDA, and trial data. It also provides example inputs for values. This enhances the schema's 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 explicitly states the tool performs side-by-side comparison of 2–5 companies or drugs in one parallel call. It provides synonyms such as 'X vs Y' and 'which is bigger,' and distinguishes from sibling tools like entity_profile by emphasizing preference over sequential lookups.

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?

The description gives clear when-to-use guidance with examples and explicitly advises to prefer this tool over sequential single-pack lookups. However, it does not mention when not to use it or alternative tools beyond the sequential lookup, missing some nuance.

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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the multiple 'ask' variants (ask_pipeworx, ask_pipeworx_grounded, deep_research) and discovery tools (discover_tools, suggest_questions) could cause confusion. Descriptions help differentiate, but the overlap is notable.

Naming Consistency4/5

All tool names use lowercase with underscores, but there is a mix of verb-first (e.g., ask_pipeworx, fetch_indicator) and noun-first (e.g., ai_visibility_check, polymarket_arbitrage) patterns. Consistent style but varied structure.

Tool Count2/5

33 tools is excessive for a coherent set. The server covers diverse domains (data lookup, betting, memory, subscriptions, web generation, package scanning) without a clear unifying theme, making it feel like a collection of utilities rather than a focused tool surface.

Completeness3/5

Core data retrieval and research capabilities are well-covered, but there are notable gaps such as lack of data update tools for OWID and no direct visualization. Additionally, the betting tools are extensive while other areas like entity editing are missing.

Resources