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Glama

Data Toulouse

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

A4.6/5.0
Behavior5/5

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

The description richly discloses behavior beyond the readOnly/openWorld/idempotent annotations: it fans out across SEC EDGAR, XBRL, USPTO, USAspending, FDA Purple Book, DOL LCA, news, and GLEIF, and explains sources_used/sources_failed, that empty sections mean real 'no data', and that private companies return resolved:false. This gives the agent an accurate mental model of what happens inside the tool.

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 long but front-loaded with query examples and the key 'one parallel call' behavior before enumerating sources and returns. Nearly every sentence adds needed information, though the type-interchangeability note largely repeats the schema and the 'person/place coming soon' aside is low-value for current invocation.

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?

With no output schema present, the description carries the burden of explaining return shape and does so well: it enumerates main result sections (cik, company_name, recent_filings with URIs, fundamentals, patents, contracts, fda products, hiring, news, LEI) and clarifies sources_used/sources_failed and empty-section semantics. The agent can invoke and interpret the result without guessing.

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 described: type enumerates 'company'/'ticker' and value documents ticker, CIK, and company-name forms. The tool description mostly repeats this and adds some related behavior (name resolution, resolved_from/resolved_to, private-company notes), but it does not substantially enrich the meaning of the parameters 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 states a specific action and resource: 'full cross-source profile of a US public company' delivered in one parallel call. It also distinguishes itself from chaining single-pack SEC/XBRL/news lookups explicitly, so an agent can recognize when this tool is the intended holistic alternative.

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?

It lists concrete trigger phrases like 'Tell me about X,' 'research Acme,' and 'brief me on Tesla,' then gives an explicit preference rule: ALWAYS PREFER over chaining single-pack lookups when the user asks for a holistic view. It also covers the private-company case, telling the agent that resolved:false with a notes line is the expected outcome rather than a failure.

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

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the multiple ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread). Users will struggle to choose the right tool without deep reading.

Naming Consistency2/5

Naming conventions are mixed: some use snake_case (ai_visibility_check, bet_research), others camelCase (generate_llms_txt, list_subscriptions), and many are long phrases (scan_competitor_ai_presence, polymarket_kalshi_spread). No consistent pattern.

Tool Count3/5

34 tools is high but not extreme. The server covers diverse domains (company data, drugs, economics, prediction markets, open data, memory utilities), but many tools are very specific and could be consolidated, making the set feel bloated.

Completeness3/5

The server covers many data sources but has notable gaps: simple market listing tools are missing for Polymarket, and the Toulouse Open Data tools are limited to querying only (no create/update/delete). Memory and subscription tools seem ancillary to the core data mission.

Resources