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Devdocs Io

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

A5/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description discloses specific data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, sorting by primary metric, and returning paired data with citation URIs. This gives the agent a comprehensive understanding of tool behavior without any 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?

The description is dense but every sentence adds value: trigger phrases, core behavior, data specifics for each type, sorting rule, and output format. It is front-loaded with user-friendly phrases for classification and maintains a logical flow. No wasted words.

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?

Given the absence of an output schema, the description explicitly states return values (paired data, citation URIs) and sorting behavior. It covers input requirements, entity types, data sources, and strategic advantages, making it fully self-contained for correct 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?

Although schema coverage is 100%, the description enriches parameter meaning substantially: type='company' specifically maps to financial metrics from 10-K filings, while type='drug' maps to FAERS/FDA/trial data. It also provides concrete examples for the values parameter, adding clarity beyond the schema's generic descriptions.

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 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call' and lists trigger phrases like 'X vs Y' and 'which is bigger'. It distinguishes itself from siblings like entity_profile (single-entity lookup) and deep_research by emphasizing parallel comparison and replacing 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', giving a clear directive on when to use this tool. It also provides a rich set of example queries and outlines what data each type pulls, enabling the agent to select it appropriately.

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

B3.1/5.0
Disambiguation3/5

There are many tools with overlapping purposes (e.g., multiple ways to ask questions, multiple Polymarket analysis tools, multiple company lookup tools). The detailed descriptions help distinguish them, but an agent could still easily select the wrong one.

Naming Consistency2/5

Tool names are inconsistent, mixing verb_noun patterns (ask_pipeworx, search_docs) with single words (db, docs) and compound names (polymarket_arbitrage, ai_visibility_check). No clear convention across the set.

Tool Count2/5

37 tools is excessive for a DevDocs documentation server; most tools are unrelated to documentation (Pipeworx data, memory, subscriptions). The core documentation functionality only requires about 7-8 tools, making the rest feel extraneous.

Completeness3/5

For the core DevDocs functionality, the tools cover listing, searching, and fetching documentation. However, the server includes many unrelated tools that are incomplete on their own (e.g., only some data lookups, no CRUD for prediction markets). Thus overall completeness is mediocre.

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