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Glama

NYC Open Data

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"]).

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate safe read operation (readOnlyHint, idempotentHint, non-destructive). Description adds valuable behavioral details: for company type, it pulls latest 10-K revenue/net income/cash/debt with correct fiscal year handling; for drug type, it pulls FAERS counts, FDA approvals, trial counts; results sorted by primary metric; returns paired data + citation URIs. No contradictions.

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 slightly verbose but well-structured and front-loaded with examples. Every sentence adds value, though a few could be combined for brevity. It efficiently conveys key information without redundancy.

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?

Despite having no output schema and moderate complexity (two entity types, multiple data sources), the description is remarkably complete. It covers what data is returned per entity type, sorting behavior, fiscal year handling, and citation URIs. No significant gaps.

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%. Description goes beyond schema: explains the 'type' enum values with what data each fetches (company vs drug). For 'values', it specifies business logic (tickers/CIKs for company, drug names for drug) and implicitly confirms min=2, max=5 through usage guidance.

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 that the tool performs side-by-side comparisons of 2-5 companies or drugs in a single call, with specific examples like 'Compare X and Y', 'X vs Y', etc. It distinguishes itself from sequential lookups by noting it replaces 8-15 sequential calls.

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 instructs to 'ALWAYS PREFER over sequential single-pack lookups when comparing entities'. Provides concrete use cases (e.g., ranking companies, head-to-head) and explains the efficiency gain (replaces 8-15 sequential lookups).

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

A3.7/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical routers, and the five polymarket_* tools all perform prediction-market analysis. Even with detailed descriptions, an agent could easily select the wrong one, especially since the server name 'NYC Open Data' does not hint at this focus.

Naming Consistency4/5

Most tools follow a consistent lowercase snake_case verb_noun pattern (e.g., list_subscriptions, generate_llms_txt, resolve_entity) and families share prefixes like polymarket_ and pipeworx_. Minor deviations exist with one-word names like datasets, metadata, and forget, but overall the naming is readable and predictable.

Tool Count2/5

34 tools is far too many for a server labeled 'NYC Open Data,' where only 3 tools (datasets, metadata, query) actually serve that purpose. The rest form a general-purpose data platform, but even then the count is high and includes many redundant meta-tools and overlapping Polymarket utilities, making the set feel bloated.

Completeness3/5

For the NYC Open Data domain, the three dedicated tools cover search, metadata, and query—adequate core functionality but missing export or dataset management capabilities. For the broader Pipeworx platform, coverage is strong (routing, grounded answers, deep research, entity profiles, subscriptions, memory), but the severe mismatch between the server name and actual scope leaves a major completeness gap.

Resources