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Glama

Metabolights

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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorted results, and citation URIs. No contradiction with annotations.

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 with 4 sentences, each adding unique value. It is front-loaded with example queries, making it immediately actionable. No redundancy or irrelevant information.

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?

For a tool with no output schema, the description covers return format (paired data + citation URIs), data sources, sorting, and entity-specific behavior. It is complete enough for an AI agent to understand exactly what to expect.

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% with descriptions for both parameters. The description enriches semantics by explaining what each type's data includes (revenue, net income, etc. for companies; adverse-event counts for drugs) and how results are sorted, going beyond the bare schema.

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 uses specific verbs ('compare', 'side-by-side comparison') and resources ('companies or drugs'), includes example queries, and clearly distinguishes itself from sequential lookups, which are handled by sibling 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.' Provides concrete context for when to use (comparison queries) and describes the behavior for each type. No explicit exclusions, but the guidance is strong.

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

B3.3/5.0
Disambiguation1/5

The vast majority of tools (e.g., ask_pipeworx, ask_pipeworx_grounded, bet_research, polymarket_arbitrage, etc.) are unrelated to Metabolights and overlap heavily with each other in purpose. Only two tools (get_study and search_studies) are clearly distinct and relevant to the server's stated domain.

Naming Consistency2/5

Tool names are highly inconsistent, mixing snake_case (get_study, search_studies), camelCase (ask_pipeworx), and varied verb styles (e.g., validate_claim vs bet_research). No consistent pattern across the set.

Tool Count1/5

With 28 tools, the server is extremely overpopulated for its stated purpose (Metabolights). Only 2 tools actually pertain to metabolomics study access, while the remaining 26 are general-purpose or unrelated (e.g., bet_research, polymarket_edges), making the count highly inappropriate.

Completeness1/5

The Metabolights domain severely lacks completeness: only search_studies and get_study are provided, with no create, update, delete, or upload functionality. The server fails to cover essential operations for its supposed domain.

Resources