Skip to main content
Glama

guif

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?

The description adds substantial behavioral context beyond the readOnly/openWorld/idempotent annotations: it explains fan-out across sources, soft-failure for the PatentsView API sunset, empty sections as real 'no data' rather than bugs, and the resolved:false path for private companies. No contradiction with annotations exists.

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 densely informative, and the length is largely justified by the tool's many output sections and failure modes. It is front-loaded with user intents and the primary usage rule. It could be tightened with structured formatting, but every major sentence contributes operational guidance.

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?

There is no output schema, so the description carries the full burden of explaining return values. It does so thoroughly: cik/company_name, resolved_from/to, recent_filings, fundamentals, patents, contracts, FDA products, hiring, news, LEI, and sources_used/sources_failed. Edge cases like private companies and empty sections are explicitly covered, making the description complete for an agent to select and invoke the tool 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 coverage is 100%, so the baseline is 3, but the description adds meaningful semantics: both type values behave identically, value accepts ticker/CIK/name, and names resolve via EDGAR company-name match. This goes beyond the schema's own parameter descriptions, earning a 4.

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 clearly states this is a full cross-source profile tool for US public companies, with a specific verb ('brief', 'profile', 'research') and resource. It distinguishes itself from single-pack lookups and chaining by emphasizing ONE parallel call. Examples of user intents make its purpose immediately recognizable.

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 explicitly says to use this tool when the user asks for a holistic view and to ALWAYS PREFER it over chaining single-pack SEC/XBRL/news lookups. It also clarifies accepted input shapes and the expected behavior for private companies, giving the agent clear decision rules.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All guif toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.3/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose. The ask_pipeworx family is differentiated by grounded mode and beta status; prediction-market tools each target a specific analysis (arbitrage, edges, fill risk, cross-venue spread); species tools split search vs. detail vs. occurrences; memory and subscription tools are unambiguous. No two tools appear to do the same thing.

Naming Consistency5/5

Tool names follow consistent snake_case patterns grouped by domain: ask_pipeworx variants, polymarket_* tools, species tools (get_species, search_species, get_occurrences, occurrences_near), memory verbs (remember, recall, forget), subscription verbs (subscribe, unsubscribe, list_subscriptions), and descriptive nouns like entity_profile and deep_research. The style is uniform and predictable.

Tool Count3/5

At 35 tools, the count exceeds the 16-25 range that feels heavy, though it sits below the 50+ extreme. The server is a multi-domain data gateway covering entity research, prediction markets, species, AI visibility, and subscriptions, so the breadth is justified, but the sheer number borders on overwhelming and pushes the score down.

Completeness5/5

The tool surface covers the core CRUD lifecycle for each subdomain: subscriptions have create/list/delete and alert retrieval, memory has save/retrieve/delete, species has search/detail/occurrence lookup, and data queries offer multiple modes (universal, grounded, deep research, claim validation). No obvious dead ends—each workflow has the necessary follow-up tools (e.g., resolve_entity before lookups, search_within for large records).

Resources