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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
Behavior5/5

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

The description discloses multiple behavioral details beyond annotations: it fans out to SEC EDGAR, GDELT/GNews with fallback logic, USPTO with soft-fail due to PatentsView sunset, and returns changes[] grouped by source with total count and citation URIs. This is rich contextual information not inferable from annotations alone.

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 long but densely informative, with opening query examples that aid intent matching. It is front-loaded with the core purpose, and each subsequent sentence adds meaningful detail. The abundance of examples is slightly redundant but still earns its place for matching user phrasing.

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, the description explains the return shape (changes[] grouped by source, total_changes, pipeworx:// citation URIs), the data sources, fallback behavior, and date format. For a 3-required-parameter tool with these annotations, this is comprehensive.

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

Parameters3/5

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

Schema description coverage is 100%, and the description largely mirrors the schema's parameter explanations (ISO/relative formats for since, ticker/CIK for value, company-only for type). It adds limited new param semantics beyond what the schema already provides.

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 defines the tool as a change feed for a company over a time window, with a specific verb ('change feed') and resource ('company'). It also distinguishes itself from entity_profile, which is the static profile alternative.

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 directs users to entity_profile when a static profile is needed, and provides typical usage guidance ('Use "30d" or "1m" for typical monitoring'). It also frames the tool for natural-language queries like 'What's new with X'.

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

Most tools have distinct purposes, but there is some overlap among closely related ones (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research; multiple Polymarket tools). Descriptions are detailed enough to differentiate, but an agent might still misselect on subtle differences.

Naming Consistency4/5

Names consistently use lowercase with underscores, but no strong verb_noun pattern. Some are noun-based (airquality, nowcast), others verb-based (ask_pipeworx, compare_entities). This is readable but not perfectly predictable.

Tool Count4/5

35 tools is high but justified by the server's broad scope (data queries, betting analysis, weather, memory, subscriptions). The number feels appropriate given the comprehensive functionality described.

Completeness5/5

The tool set covers an impressively wide range of capabilities: data querying with multiple modes, entity profiling, comparisons, search, betting analysis, weather, memory, subscriptions, and feedback. Missing features (e.g., updating memories) are minor; the surface is remarkably complete.

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