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Data Nj

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

A4.8/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, etc. The description adds detailed behavioral context: it fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), specifies return fields, and notes that the USPTO API will sunset May 2025 and will soft-fail. 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?

The description is somewhat long but well-structured: it front-loads example queries, then explains what the tool does, followed by return details and caveats. Every sentence adds value, though a slight trim could improve conciseness.

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?

Given the complexity and lack of output schema, the description covers inputs thoroughly and lists all returned fields (cik, company_name, recent_filings, fundamentals, patents, news, LEI). It also mentions the PatentsView sunset. Minor omission: no explicit error handling beyond soft-fail, but overall comprehensive.

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?

Schema coverage is 100%, but the description adds significant meaning: it clarifies that value can be a ticker or zero-padded CIK (with examples), explicitly states names are not supported, and confirms the type enum is currently limited to 'company'. This goes beyond the schema alone.

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 specific verbs like 'tell me about', 'research', 'brief me on' and clearly states it provides a 'full cross-source profile of a US public company'. It distinguishes from sibling tools like resolve_entity by explicitly saying names are not supported and to use that tool first if 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?

Explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also provides clear when-not guidance: 'names not supported (use resolve_entity first if you only have a name).'

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 distinct purposes, but some overlap exists between ask_pipeworx, ask_pipeworx_grounded, and deep_research, which all query Pipeworx data in different modes. However, their descriptions clearly differentiate them. Similarly, memory and subscription tools are separate. Overall, an agent can distinguish tools with moderate effort.

Naming Consistency4/5

Tool names mostly follow verb_noun pattern with snake_case, such as ask_pipeworx, compare_entities, generate_llms_txt. However, a few tools like 'datasets', 'metadata', and 'query' are single nouns, breaking the pattern. Overall, naming is consistent enough for an agent to predict behavior.

Tool Count3/5

33 tools is above the typical range for an MCP server, but the server covers a wide domain (SEC, FRED, FDA, prediction markets, etc.) with specialized tools. The count is borderline heavy but justifiable given the scope. Some tools like memory and subscription management add to the count but serve necessary auxiliary functions.

Completeness4/5

The tool surface is comprehensive for its intended domain of structured data querying and analysis. It covers data retrieval, entity resolution, comparison, news, subscriptions, and memory. Minor gaps include dependency scanning only for npm and lack of direct web search, but meta-tools like ask_pipeworx fill many needs. Overall, it supports common workflows well.

Resources