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Glama

Fred

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 mark readOnly/openWorld/idempotent, and the description adds non-obvious behaviors: parallel fan-out across many sources, soft-fail for the PatentsView API, 'empty section is a real no-data, not a bug', and resolved:false for private companies. 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?

Dense but justified for a multi-source tool with no output schema; all sections of the return payload are spelled out. The opening six synonymous example queries are redundant and could be trimmed, so not a 5.

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?

No output schema, so description compensates by enumerating every returned section (cik, recent_filings, fundamentals, patents, contracts, fda_products, hiring, news, LEI, sources_used/failed) and input edge cases. An agent has enough to call and interpret results 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 covers both params fully; description adds value with accepted shapes (ticker, zero-padded CIK, name), examples, and the note that `type` values are interchangeable. Slight redundancy with schema, hence not a 5.

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 precise verb+resource: full cross-source profile of a US public company in one call, with concrete inputs (ticker/CIK/name). The 'ALWAYS PREFER over chaining single-pack … lookups' line separates it from lower-level lookup tools, and the entity vs compare/resolve distinction is evident.

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 when to use it — 'ALWAYS PREFER … when the user asks for a holistic view' — and what not to do (chain single-pack SEC/XBRL/news lookups). It also gives fallback behavior for private companies, leaving no ambiguity about acceptable inputs.

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

Every tool has a clearly distinct purpose with detailed descriptions that differentiate even closely related tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded. The FRED and Polymarket tool sets are well-organized with unique responsibilities. No two tools appear to do the same thing.

Naming Consistency4/5

Most tools follow a consistent verb_noun or noun_verb pattern in snake_case (e.g., resolve_entity, compare_entities, list_subscriptions). However, a few tools like 'forget', 'remember', and 'recall' deviate by being single verbs, and 'pipeworx_feedback' uses a noun_verb format. Overall, the naming is predictable but has minor inconsistencies.

Tool Count4/5

With 37 tools covering a broad domain (economic data, prediction markets, company profiles, subscriptions, memory, etc.), the count is reasonable and justifiable. It is slightly above the typical sweet spot but not excessive, and each tool serves a specific purpose. The scope is broad enough to warrant this many tools.

Completeness5/5

The server provides a comprehensive surface for its domain, including CRUD-like operations for data querying (ask_pipeworx, deep_research), specialized tools for prediction markets (arbitrage, edges), and utilities (memory, subscriptions). Obvious operations like entity resolution, comparison, and change tracking are present. No critical gaps are apparent for the stated purpose of querying structured data and engaging with prediction markets.

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