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Glama

Spdx License

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?

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), fiscal year handling, sorting by primary metric, and return format (paired data + citation URIs). This is rich behavioral context that annotations do not provide.

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 dense with information and front-loads trigger phrases and the key preference instruction. While not ultra-brief, every sentence contributes meaningful details about usage, data sources, or output behavior, justifying its length.

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 tool's complexity (two entity types, distinct data sources, no output schema), the description covers purpose, input semantics, behavior, sorting, and return format. It is self-contained and leaves no significant gaps for an agent to invoke it correctly.

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 valuable semantics: explains the type enum variants, gives concrete examples for values (tickers/CIKs vs drug names), and clarifies the data pulled for each type. This goes beyond the schema's basic 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 the tool's function: side-by-side comparison of 2–5 companies or drugs in one parallel call. It uses specific verbs and resources, and explicitly distinguishes itself from sequential single-pack lookups, making it unambiguous.

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 provides explicit triggers ('Compare X and Y', 'X vs Y', etc.) and instructs to ALWAYS PREFER this tool over sequential lookups when comparing entities. It does not name specific sibling tools, but the guidance is clear and actionable.

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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple 'ask_pipeworx' variants, several company analysis tools, and multiple prediction market tools. The set is large and not well-disambiguated, leading to potential confusion.

Naming Consistency2/5

Naming conventions are mixed, with snake_case ('get_license'), camelCase ('ai_visibility_check'), and prefix-based ('pipeworx_*', 'polymarket_*'). No consistent pattern across the tool set.

Tool Count2/5

35 tools is excessive for a server named 'Spdx License', which implies a focused license management tool. The actual number is more appropriate for a general data platform, but mismatched with the server name.

Completeness1/5

For the implied SPDX license domain, only a few basic tools exist (list, get, search). Missing crucial features like create, update, delete, or compare licenses. The surface is severely incomplete for the stated purpose.

Resources