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Open Contracting

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

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, etc. Description adds behavioral details: fans out across multiple sources, mentions USPTO patent sunset and soft-fail behavior, and lists return fields. No contradiction 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?

Single paragraph with example queries front-loaded for LLM recognition. Every sentence adds value, though slightly dense. Could be broken into bullet points for readability.

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?

For 2 required params and no output schema, the description covers inputs, outputs, data sources, fallbacks, usage priority, and limitations. Very complete for the tool's 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%, but description adds context: explains type enum limits, format of value (ticker or zero-padded CIK), and explicitly warns that names are not supported, referencing resolve_entity. This adds significant guidance 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?

The description clearly states it provides a 'full cross-source profile of a US public company' and lists exact return fields. It distinguishes from siblings like resolve_entity by noting names are not supported and directing to use resolve_entity first.

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 says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also provides when-not-to-use by stating names not supported and suggesting resolve_entity as an alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Open Contracting toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve query/discovery purposes; polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, and bet_research all target prediction-market opportunity detection. ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, making the distinction essentially invisible without deep description parsing.

Naming Consistency3/5

Names mix domain prefixes (oc_*, polymarket_*, pipeworx_*), action verbs (validate_claim, resolve_entity, generate_llms_txt), and plain nouns (entity_profile, recent_alerts, recent_changes). Most are readable snake_case, but there is no uniform verb_noun or domain-first convention, so the set feels stylistically fragmented rather than patterned.

Tool Count2/5

35 tools is a heavy surface for a server ostensibly named 'Open Contracting' — only 4 of the 35 tools actually relate to open contracting data. The rest sprawl across general data lookup, prediction markets, memory, subscriptions, npm scanning, and AI visibility, making the tool count feel bloated and unfocused relative to the stated purpose.

Completeness2/5

For the open-contracting domain implied by the server name, the surface is incomplete: there is coverage metadata, search, recent releases, and process history, but no direct retrieval of a single release by ID and no broader OCDS exploration tools. As a general data/Pipeworx toolkit the coverage is wide, but the severe mismatch between the server name and the actual tool set creates a significant gap between expectation and capability.

Resources