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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?

Annotations already declare readOnlyHint=true, openWorldHint=true, etc. The description adds specific behavioral details: data sources (SEC EDGAR/XBRL, FAERS), correct fiscal year handling, sorted results, 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 front-loaded with example queries and imperative guidance. Every sentence adds value. Despite length, no wasted words. Structure is logical: trigger examples, then behavior per type, then output format.

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?

No output schema, but description adequately describes return format (paired data + citation URIs). With full schema coverage, annotations, and detailed parameter behavior, the description is complete for a tool of this complexity. Nothing essential is missing.

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?

Schema coverage is 100% (2 params described). The description adds meaning beyond schema: for 'company' type it lists pulled metrics (revenue, net income, cash, debt), for 'drug' type it lists adverse events, approval counts, trials. Greatly aids selection and invocation.

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 a specific verb+resource: compares 2-5 entities in one call. It distinguishes from sequential lookups and lists example queries users might type. The purpose is immediately evident and differentiates from siblings.

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' and gives a list of trigger phrases. It tells when to use (comparison) and implies when not to (single entity). No ambiguity.

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
Disambiguation3/5

Tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are very similar, causing potential confusion. The open_payments_* tools are clearly differentiated, but the mix of generic Pipeworx tools with domain-specific ones creates overlapping purposes.

Naming Consistency2/5

Tool names mix conventions: some use snake_case (ask_pipeworx, open_payments_company), while others use less consistent patterns (deep_research, generate_llms_txt). The open_payments_* tools have a consistent prefix, but the overall set lacks a unified naming scheme.

Tool Count2/5

With 41 tools, the server is heavily overloaded for a domain focused on CMS Open Payments. The majority of tools are general-purpose Pipeworx tools unrelated to the server's name, making the count feel excessive and unfocused.

Completeness3/5

The open_payments_* tools cover the CMS Open Payments domain well (search, company, physician, history, etc.). However, the inclusion of many unrelated Pipeworx tools means the server as a whole is not cohesive, and the completeness of the named domain is overshadowed.