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Glama

Edgar

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds substantial behavioral context beyond that: it fans out across multiple sources, soft-fails on USPTO until reactivated, returns sources_used/sources_failed so empty sections are meaningful, resolves names via SEC EDGAR, and returns resolved:false with notes for private companies. This is exactly the kind of hidden behavior an agent needs to know before calling.

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 well-organized: trigger phrases first, then the core value proposition, then source list and output sections, then input semantics and edge cases. Every sentence carries information needed for correct invocation. It's longer than many tool descriptions, but the complexity of the tool justifies it; there is no filler or repetition. A small deduction because the length, while earned, could be trimmed slightly by moving some output-section detail into a more compact structure.

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 tool with 2 simple parameters, no output schema, and a complex fan-out behavior, this description is remarkably complete. It covers input formats, resolution behavior, expected empty results (FDA small-molecule), failure semantics (sources_failed, soft-fail), and the return structure in detail. An agent can confidently decide whether to call it and what to do with the result without needing any additional documentation.

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%, so the schema already documents both parameters in detail. The description adds value by clarifying that type accepts 'company' or 'ticker' interchangeably and both take the same value shapes, which is a non-obvious semantic that would otherwise be ambiguous. It also gives concrete examples for value (AAPL, 0000320193, Moderna) and explains the name-resolution behavior. The only minor gap: it doesn't enumerate every possible output field, but the output format is already thoroughly described in the main description.

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 opens with concrete natural-language triggers ("Tell me about X", "research Acme", "brief me on Tesla") and then names the exact resource — a full cross-source profile of a US public company — pulled in one parallel call. It explicitly lists the data sources (SEC EDGAR, XBRL, USPTO, USAspending, FDA Purple Book, DOL LCA, GDELT→GNews, GLEIF) and the output sections, which is far more specific than just saying 'entity profile'. It also distinguishes itself from sibling tools like edgar_company_filings or deep_research by framing itself as the holistic aggregation tool to use instead of chaining single-pack lookups.

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?

The description says ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view, which is an explicit usage directive with a clear alternative. It also enumerates accepted input shapes (ticker, zero-padded CIK, or company name) and states the expected behavior for private companies (resolved:false with notes), telling the agent when this tool is not appropriate. The schema and description together leave little ambiguity about when to invoke this tool versus its siblings.

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

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, with detailed descriptions that prevent confusion. Overlapping tools like ask_pipeworx vs ask_pipeworx_grounded are explicitly differentiated by use case (casual vs high-stakes) and refusal behavior.

Naming Consistency4/5

Names are mostly consistent using lowercase underscores, but there is a mix of verb-initial (ask_pipeworx, generate_llms_txt) and noun-initial (edgar_company_facts, polymarket_arbitrage) patterns, which slightly reduces predictability.

Tool Count3/5

37 tools is high, but the server covers a broad domain of authoritative data sources (SEC, FDA, FRED, prediction markets, etc.). While it exceeds the typical 15-tool threshold, each tool serves a distinct data need and the count feels justified for the scope.

Completeness5/5

The tool surface covers the full lifecycle of data retrieval and analysis: discovery (discover_tools), single queries (ask_pipeworx), grounded lookups (ask_pipeworx_grounded), multi-source research (deep_research), entity profiles, comparisons, historical data, and subscription monitoring. No major gaps are evident.

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