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Glama

Npi Registry

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

A4.4/5.0
Behavior4/5

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

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds substantial detail: specific data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and citation URIs. It does not discuss error handling or response format but provides good transparency.

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 a single dense paragraph that front-loads with query examples. It contains no filler and every sentence adds value, though it could be slightly more structured (e.g., bullet points) for easier parsing. Still concise enough for an AI agent.

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 no output schema, the description covers essential aspects: entity types, data sources, sorting, citations, and efficiency gains. It lacks details on error scenarios and response structure but is complete for typical use.

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% with descriptions for both parameters. The description adds significant meaning: for type it explains what data each enum value retrieves, and for values it gives concrete examples (tickers for company, drug names for drug) and reinforces the 2-5 limit.

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 performs side-by-side comparison of 2-5 companies or drugs in a single call, with explicit example queries. It distinguishes from siblings by emphasizing preference over sequential lookups, and the sibling list includes entity_profile which is a single-entity tool.

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 clear guidance on when to use the tool (comparing 2-5 entities) and strongly recommends it over sequential lookups. It does not explicitly cover when NOT to use, such as limits beyond 5 entities, but the 2-5 range is stated.

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

A3.9/5.0
Disambiguation4/5

The tools are generally distinct, with clear purposes for NPI registry operations, but some overlap exists between ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded, which all route to the same underlying data but with different response modes. Additionally, bet_research and polymarket_edges both offer analysis of prediction markets, causing potential confusion.

Naming Consistency3/5

The naming is mixed: some tools follow a consistent verb_noun pattern (e.g., search, remember, forget), while others use descriptive but non-pattern names like ai_visibility_check or ask_pipeworx_grounded. There is also a mix of snake_case and camelCase (e.g., generate_llms_txt vs. ai_visibility_check).

Tool Count4/5

With 33 tools, the count is slightly high but still reasonable given the broad scope of the server, which covers NPI registry, company profiles, prediction markets, AI visibility, and more. Each tool serves a distinct purpose, though a few could potentially be consolidated.

Completeness3/5

The tool surface covers the NPI registry core (search, get by NPI) but lacks obvious CRUD operations like create, update, or delete for providers. For other domains like company profiles, it has good coverage, but the NPI-specific functionality feels incomplete without lifecycle management.

Resources