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Glama

NYC Open Data

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds important behavioral details: parallel fan-out to multiple sources, fallback logic (GDELT→GNews), patent soft-fail, and structured return format with citation URIs. No contradictions 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense paragraph, but every sentence adds value. It front-loads examples and purpose, then flows into technical details. A slight structural improvement (e.g., bullet points) could enhance readability, but it remains efficient and focused.

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 the lack of an output schema, the description fully explains the return structure (changes[] grouped by source, total_changes, citation URIs). It covers edge cases (GDELT rate limits, patent API sunset) and provides alternative tool guidance, making it complete for a tool of this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all 3 parameters. The description adds value by explaining the `since` parameter's acceptance of ISO dates and relative shorthands (e.g., '7d', '30d'), and that `value` accepts tickers or CIK numbers. It enriches the schema without being redundant.

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 uses specific verbs ('change feed for a company') and names the covered sources (SEC, GDELT/GNews, USPTO). It includes example queries and explicitly distinguishes from the sibling tool 'entity_profile', making purpose unmistakable.

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 provides explicit usage context: 'What's new with X' queries and explains fallback behavior. It also states when NOT to use the tool: 'Use entity_profile instead when you want the static profile...' This leaves no ambiguity for an AI agent.

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