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Glama

Rxnorm

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations. It details data sources (SEC EDGAR/XBRL, FAERS, FDA, clinicaltrials), explains how off-calendar fiscal years are handled, and clarifies that results are sorted by primary metric. This complements the readOnlyHint and other annotations without contradiction.

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 front-loaded with natural language triggers and is well-structured. However, it is somewhat lengthy and could be slightly more concise without losing clarity. Still, every sentence adds value.

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?

The tool handles two distinct entity types and multiple data sources. The description covers all essential aspects: input constraints, data sources, handling of fiscal years, sorting order, and return format (paired data + citation URIs). No output schema exists, but the description sufficiently explains the output.

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?

Despite 100% schema coverage, the description adds essential meaning. It explains what data each type retrieves (e.g., 'LATEST 10-K revenue + net income + cash + long-term debt' for companies, 'FAERS adverse-event counts' for drugs). This goes well beyond the schema's minimal 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 performs side-by-side comparisons of 2-5 companies or drugs. It uses specific verbs like 'compare', 'rank', and includes examples of natural language triggers. It distinguishes from sibling tools by explicitly stating it replaces 8-15 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?

The description explicitly advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It provides concrete usage examples ('compare X and Y', 'which is bigger') and specifies the input constraints (2-5 entities). This clearly guides when to use this tool over alternatives.

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
Disambiguation2/5

Several tools blur together: ask_pipeworx_beta currently duplicates ask_pipeworx exactly, ask_pipeworx/ask_pipeworx_grounded/deep_research/validate_claim all route natural-language questions to data sources, and the prediction-market tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, bet_research) have fuzzy boundaries. The detailed descriptions help, but an agent can easily misselect among them.

Naming Consistency3/5

All names are lowercase snake_case and the rxnorm_, polymarket_, and pipeworx_ prefixes create readable groupings, but the set mixes imperative verb-object names (validate_claim, list_subscriptions), noun-phrase names (entity_profile, recent_alerts), and bare verbs (remember, forget). It is readable but not a predictable uniform naming convention.

Tool Count2/5

35 tools is in the too-many band for a coherent server, and the Rxnorm identity makes it worse: only 4 tools are actually RxNorm-specific while 31 are unrelated Pipeworx, prediction-market, memory, and utility tools. A focused RxNorm server would need a fraction of this surface.

Completeness3/5

The four rxnorm_* tools plus resolve_entity cover the main RxNorm lookup flow (search, properties, related, NDC), so the core is not broken. But the set lacks a reverse NDC-to-concept lookup and the 31 non-RxNorm tools do not complete any single coherent domain, leaving notable gaps relative to the server's stated purpose.

Resources