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Color Pizza

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

A4.9/5.0
Behavior5/5

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

Discloses underlying behavior: fans out to SEC EDGAR, GDELT/GNews fallback, USPTO with PatentsView sunset explanation, and soft-fail. This adds significant context beyond annotations (readOnlyHint, idempotentHint, etc.).

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?

Description is well-structured with clear front-loading of example queries and logical flow. However, it is somewhat verbose; each sentence is informative but could be slightly more concise.

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 no output schema, description explains return values (changes grouped by source, total_changes count, citation URIs). Covers fallback behavior, parameter nuances, and alternative tool. Very complete for a complex tool.

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?

Input schema has 100% description coverage. Description adds meaning: explains 'since' accepts ISO date or relative shorthand with examples, recommends '30d' for monitoring, and clarifies 'value' accepts ticker or CIK. Enhances understanding beyond 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?

Description clearly states 'change feed for a company in the last N days/weeks/months', providing a specific verb and resource. It distinguishes from sibling tool 'entity_profile' by noting when to use that instead for static profiles.

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 lists example queries ('What's new with X', 'latest on Y'), gives context for usage, and directly tells when not to use it ('Use entity_profile instead when you want the static profile').

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

A4.3/5.0
Disambiguation4/5

Most tools have distinct purposes, such as `entity_profile` for company profiles and `recent_changes` for updates. However, some overlap exists between `ask_pipeworx` and `ask_pipeworx_grounded` (both query data, one with hallucination resistance), and among Polymarket-related tools (`bet_research`, `polymarket_edges`, `polymarket_arbitrage`). Detailed descriptions help distinguish them.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern (e.g., `ai_visibility_check`, `ask_pipeworx`, `bet_research`, `compare_entities`). No mixing of camelCase or other conventions. The names are descriptive and predictable.

Tool Count3/5

At 29 tools, the server is on the heavy side. While each tool serves a specific function, the broad scope (color naming, Polymarket betting, company research, etc.) makes the count feel slightly high for a single server, though still manageable.

Completeness4/5

The tool set covers color naming, company research, Polymarket, AI visibility, memory, subscriptions, and data queries comprehensively. Minor gaps exist (e.g., no direct SEC filing retrieval without compound tools), but overall it provides a well-rounded surface for its diverse domain.

Resources