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Color Pizza

by io.github.pipeworx-io

Entity Profile

entity_profile
Read-onlyIdempotent

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF and returns: cik + company_name; recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today; person/place coming soon.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193"). Names not supported — use resolve_entity first if you only have a name.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations declare read-only, idempotent, safe. Description adds detailed behavior: fans out across multiple sources, returns specific fields, USPTO sunset May 2025 soft-fail, and exact output structure. No contradiction.

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?

Long but every sentence adds value; front-loaded with examples and usage priority. Could be slightly more concise but effectively structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, so description carries full burden. It fully explains returned fields, data sources, and potential failures. A slight gap in specifying exact output structure is acceptable given free-text.

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. The description reinforces parameter usage with examples and adds crucial context about name resolution, going beyond the schema's minimal definition.

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 starts with example user queries, specifies the verb ('profile' implied), and the resource ('US public company'). It explicitly contrasts with chaining single-pack lookups, clearly distinguishing its purpose from sibling tools.

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?

Directly states when to use ('holistic view') and when not (if only a name, use resolve_entity first). Provides clear alternatives and context for invocation.

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