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Sec Events

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
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent/non-destructive, and the description adds valuable context: it fans out across multiple sources, discloses the USPTO PatentsView API sunset with soft-fail behavior, and mentions GDELT→GNews fallback. This goes beyond the annotations and helps the agent set expectations.

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 dense but front-loaded with example queries that make the intent immediately clear. While it is a long paragraph, each clause adds necessary detail about the multi-source output, fallbacks, and limitations. No redundancy.

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?

The description thoroughly explains what the tool returns (cik, recent_filings with URIs, fundamentals, patents, news, LEI), notes source dependencies and soft-fail behavior, and covers parameter format. With no output schema, the description carries full responsibility for return-value transparency, and it does so completely.

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 both parameters are already well-documented. The description primarily reiterates the same ticker/CIK examples ('AAPL', '0000320193') and the 'names not supported' rule, adding no new semantic information beyond the 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 uses a specific verb ('profile') and clearly states it returns a 'full cross-source profile of a US public company in ONE parallel call.' It distinguishes from siblings by explicitly preferring this tool over chaining single-pack SEC/XBRL/news lookups for holistic views, and also points to resolve_entity when only a name is available.

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?

Explicit guidance is provided: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also gives a clear when-not-to-use case: names are not supported, and the alternative (resolve_entity) is named.

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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap among similarly named tools like ask_pipeworx, deep_research, and bet_research, which could cause misselection. However, detailed descriptions help differentiate them.

Naming Consistency3/5

Tool names follow a mix of patterns (verb_noun, noun_noun, etc.) and use different prefixes (polymarket_, sec_8k_, pipeworx_), which is somewhat inconsistent but still readable and descriptive overall.

Tool Count3/5

34 tools is on the higher side, with several tools dedicated to specific subdomains (e.g., 6 Polymarket-related, 4 SEC 8-K tools). While each has a distinct role, the number feels slightly bloated for a single server.

Completeness4/5

The tool surface covers a broad range of data research and monitoring tasks, including filings, entity profiles, claims, and prediction markets. Minor gaps exist (e.g., no data writing tools), but core workflows are well-supported.

Resources