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Glama

Fas

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

A4.6/5.0
Behavior5/5

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

Even with readOnlyHint, openWorldHint, idempotentHint, and destructiveHint already set, the description adds substantial behavioral context: fan-out across sources, soft-fail on USPTO sunset, expected empty FDA sections, sources_used/sources_failed semantics, and private-company resolved:false behavior. There is 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 long, but the tool is complex and nearly every clause carries distinct behavioral or routing information. It is front-loaded with examples and the 'ALWAYS PREFER' directive; the rest is a structured enumeration of output sections and edge-case behavior. Some trimming of examples could improve conciseness, but it earns its length.

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, the description fully compensates by enumerating each return section, explaining fallbacks and failure modes, clarifying empty results, and specifying input resolution behavior. An agent has enough information to call this correctly across ticker, CIK, and company-name inputs.

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 baseline is 3. The description adds value beyond the schema by giving concrete examples ('AAPL', '0000320193', 'Moderna'), emphasizing zero-padded CIKs, explaining that type values are interchangeable, and detailing what happens for private companies. This is a meaningful enhancement over 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 opens with concrete user phrasings and states the core function: 'full cross-source profile of a US public company in ONE parallel call.' It clearly distinguishes this from 'chaining single-pack SEC/XBRL/news lookups' by explicitly telling agents to prefer it for holistic views.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an explicit when-to-use rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It does not name specific sibling tools like compare_entities or deep_research, nor state when not to use it, but the guidance is actionable for the holistic-research case.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., ask_pipeworx vs. deep_research vs. validate_claim). However, a few pairs like ask_pipeworx_beta vs. ask_pipeworx and validate_claim vs. ask_pipeworx_grounded have overlapping roles, even though descriptions do differentiate them.

Naming Consistency4/5

All tool names use snake_case consistently, and many follow a verb_noun pattern (ask_pipeworx, compare_entities, subscribe). Some exceptions like entity_profile, recent_alerts, and pipeworx_trending break the strict verb_noun pattern but remain readable and stylistically uniform.

Tool Count3/5

With 35 tools, this is above the typical 'well-scoped' range and exceeds the 25-tool threshold for heavy servers. However, the server covers a very broad domain (financial data, prediction markets, agriculture, AI visibility, memory, subscriptions), which partially justifies the count, but it still feels bloated.

Completeness4/5

The tool surface covers all major workflows: data querying (ask_pipeworx, deep_research), entity resolution and comparison, prediction-market analysis, agricultural data (FAS tools), memory (remember/recall/forget), and subscription management (subscribe/unsubscribe/list). The only minor gap is lack of direct write/update operations for external data, but that's not expected for a read-heavy platform.

Resources