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npm

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?

Annotations already declare read-only/idempotent behavior, but the description adds substantial context: it discloses data sources (SEC EDGAR/XBRL, FAERS), handles off-calendar fiscal years correctly, states results are sorted by primary metric, and mentions return of paired data with citation URIs. This goes well beyond the annotations.

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 and well-structured, front-loaded with trigger phrases and the key 'ALWAYS PREFER' guidance. While every sentence adds value, it is longer than strictly necessary, and the 'Replaces 8–15 sequential lookups' is a value prop rather than essential behavioral info.

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 no output schema, the description adequately explains the return structure ('paired data + pipeworx:// citation URIs'), sorting behavior, and the types of data retrieved for each entity type. It also covers quirks (off-calendar fiscal years) and performance benefits, making it complete for selection and invocation.

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?

Even with 100% schema coverage, the description enriches both parameters: it explains what 'type=company' vs 'type=drug' means in terms of data fields, and gives concrete examples for the 'values' array (tickers/CIKs, drug names). This adds meaning beyond the schema.

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 opens with concrete user query patterns ("Compare X and Y", "X vs Y", "which is bigger") and clearly states the tool performs side-by-side comparison of 2–5 entities. It distinguishes itself from sibling tools by explicitly preferring it over 'sequential single-pack 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 says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing a clear when-to-use directive and an alternative to avoid. It also details exactly what each entity type (company vs drug) pulls, guiding usage.

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

A3.8/5.0
Disambiguation4/5

Descriptions are exceptionally detailed and differentiate tools well; the ask_pipeworx family (stable/beta/grounded), polymarket tools, and npm lookup tools each have clear separation of intent. Minor overlap exists between polymarket_edges and polymarket_arbitrage (both surface opportunities) and between discover_tools, suggest_questions, and pipeworx_trending (all aid discovery), but descriptions mostly resolve the ambiguity.

Naming Consistency3/5

All names are snake_case and several families are consistent (get_*, list_*, search_*, ask_pipeworx, polymarket_*), but the convention is inconsistent: verb-first names (resolve_entity, validate_claim, scan_dependency) coexist with noun-first or noun-only names (entity_profile, deep_research, bet_research, recent_alerts, ai_visibility_check, pipeworx_trending, polymarket_arbitrage). No clear governing pattern beyond snake_case.

Tool Count2/5

36 tools is well over the 25-tool heavy threshold, and the majority (~29) are unrelated to the server's declared 'npm' identity — they are Pipeworx data-query, prediction-market, memory, and subscription tools. Only about 7 tools (search_packages, get_package, get_version_info, list_versions, get_downloads, scan_dependency, generate_llms_txt) actually pertain to npm. The scope is a kitchen-sink mismatch with the server name.

Completeness3/5

For npm, the read-side surface is reasonably complete: search, inspect package metadata, version listings, download counts, and a dependency-safety composite check. However, there are no lifecycle operations (publish, unpublish, deprecate, set versions/tags), leaving a notable gap, and the bulk of the server's functionality (data queries, prediction bets) belongs to an entirely different domain that can't be cohesively evaluated against the npm purpose.

Resources