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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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior. The description adds rich context: data sources (SEC EDGAR/XBRL, FAERS), specific metrics pulled, handling of off-calendar fiscal years, sorting behavior, and citation URIs. This goes well beyond annotation coverage.

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 somewhat long due to enumerated trigger phrases, but every sentence contributes functional or behavioral information. It front-loads usage patterns and keeps the structure logical, making the length acceptable.

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?

Given the tool's simplicity (2 params, no output schema) and the presence of annotations, the description adequately covers expected data, sorting, and return format. It does not mention error cases or limitations, but that is not a critical gap for a comparison tool.

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 descriptions cover both parameters, but the description reinforces semantics with concrete examples (AAPL, MSFT, ozempic) and explains what each type returns. This adds value over the schema by linking parameter values to their data implications.

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 identifies the tool as performing side-by-side comparisons of 2-5 companies or drugs, with explicit trigger phrases and a specific resource. It distinguishes itself from sequential lookups by asserting 'ALWAYS PREFER over sequential single-pack lookups,' making its scope unmistakable.

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?

It states when to use the tool ('when comparing entities') and explicitly instructs preferring it over sequential single-pack lookups. However, it does not name specific alternative tools (e.g., entity_profile) or state when not to use it, such as for single-entity queries.

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

A3.6/5.0
Disambiguation4/5

Tools are generally distinct in purpose, but some overlap exists between ask_pipeworx, ask_pipeworx_grounded, and deep_research. Descriptions clearly differentiate them, but an agent might still need to carefully choose.

Naming Consistency4/5

Most tool names follow a consistent snake_case pattern with clear prefixes (e.g., pipeworx_*, polymarket_*). A few names like compare_entities and validate_claim break the pattern, but overall it is predictable.

Tool Count2/5

With 40 tools, the server is overly large for a single coherent set. Many tools are meta or auxiliary, but the count exceeds the 'too many' threshold, making it hard for agents to navigate.

Completeness4/5

The tool set covers data retrieval, Chilean open data, Polymarket, memory, and monitoring comprehensively. Minor gaps exist (e.g., no direct trading for Polymarket), but the surface is complete for the stated purpose.

Resources