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Datos Cl

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 patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when value was a 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); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. sources_used / sources_failed say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit notes line, not a bare failure. type accepts "company" or "ticker" interchangeably — both take the same value shapes above.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes"company" or "ticker" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon.
valueYesTicker (e.g., "AAPL"), zero-padded CIK (e.g., "0000320193"), or company name (e.g., "Moderna") — names resolve via SEC EDGAR company-name match.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. Only \"company\" supported today; person/place coming soon."New value: +"\"company\" or \"ticker\" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "ticker"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker (e.g., \"AAPL\") or zero-padded CIK (e.g., \"0000320193\"). Names not supported — use resolve_entity first if you only have a name."New value: +"Ticker (e.g., \"AAPL\"), zero-padded CIK (e.g., \"0000320193\"), or company name (e.g., \"Moderna\") — names resolve via SEC EDGAR company-name match."
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already signal read-only, non-destructive, open-world, and idempotent behavior. The description goes well beyond that by disclosing the fan-out across EDGAR, XBRL, patents, contracts, FDA, H-1B, news, and GLEIF; explaining sources_used/sources_failed semantics; noting that empty sections mean real 'no data'; and flagging the USPTO PatentsView sunset soft-fail. This is rich behavioral context an agent could not infer from the schema or annotations alone.

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?

Front-loaded with trigger phrases and the core purpose, then organized by return section. It is long, but the length is justified by the tool's breadth — every clause adds operational information rather than filler. The lack of visual structuring (bullets) costs a point, but the density is efficient.

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 a wide-ranging tool with no output schema, the description is exceptionally complete: it enumerates every data section, documents failure semantics, explains what happens for private companies, describes source fallback (GDELT→GNews), and makes the two-parameter contract fully self-explanatory. An agent has essentially everything needed to call it correctly.

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 description coverage is 100%, so the baseline is 3. The description adds meaningful value on top: both type values behave identically, value can be a ticker/CIK/name, CIKs are zero-padded, and names resolve via SEC EDGAR company-name match. This makes invocation less ambiguous without merely repeating 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?

States a specific action — producing a full cross-source profile of a US public company — with concrete trigger phrases and a clear distinction from chaining single-pack lookups. The resource and scope are unambiguous, and the description makes clear how entity_profile differs from more targeted 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?

Explicitly instructs to prefer this tool 'ALWAYS' over chaining single-pack SEC/XBRL/news lookups when a holistic view is requested. It also tells the agent when not to expect success (private companies return resolved:false) and that 'person/place' is not yet supported, covering both when-to-use and when-not-to-use.

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

A3.6/5.0
Disambiguation4/5

Tools are generally distinct in purpose, but some overlap exists between ask_pipeworx, ask_pipeworx_grounded, and deep_research. Descriptions clearly differentiate them, but an agent might still need to carefully choose.

Naming Consistency4/5

Most tool names follow a consistent snake_case pattern with clear prefixes (e.g., pipeworx_*, polymarket_*). A few names like compare_entities and validate_claim break the pattern, but overall it is predictable.

Tool Count2/5

With 40 tools, the server is overly large for a single coherent set. Many tools are meta or auxiliary, but the count exceeds the 'too many' threshold, making it hard for agents to navigate.

Completeness4/5

The tool set covers data retrieval, Chilean open data, Polymarket, memory, and monitoring comprehensively. Minor gaps exist (e.g., no direct trading for Polymarket), but the surface is complete for the stated purpose.

Resources