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Glama

Uuid

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

A4.4/5.0
Behavior5/5

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

The description goes far beyond the annotations (readOnlyHint, openWorldHint, idempotentHint) by detailing the multi-source fanout, specific returned sections, the sources_used/sources_failed fields, the meaning of empty sections ('a real "no data", not a bug'), and the private-company resolved:false behavior. It also discloses the GDELT→GNews fallback and expected absence for small-molecule drugs, which is rich, honest behavioral context.

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: example triggers up front, then fanout and return sections, then parameter acceptance rules. It earns most sentences, though the type-interchangeability sentence partially duplicates the already-complete schema. For a tool of this complexity, the length is justified and it front-loads the most important usage signal ('ALWAYS PREFER').

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?

Even without an output schema, the description fully prepares the agent: it lists every data section (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI), explains how to interpret empty results, specifies input shapes, and warns about private companies. Nothing a calling agent needs to know to select and invoke the tool is missing.

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?

The schema already provides 100% description coverage for both parameters, including accepted value shapes and the interchangeability of type values. The description largely restates this ('Pass a ticker... zero-padded CIK... or a company name', 'type accepts "company" or "ticker" interchangeably'), adding little beyond the schema. The name-resolution and private-company behavior are useful, but they are result semantics rather than parameter meaning.

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 verb ('profile', 'brief', 'research') and resource ('US public company') and positions the tool as the single-call cross-source profile lookup. It clearly distinguishes itself from single-pack SEC/XBRL/news lookups and from siblings like resolve_entity and compare_entities by emphasizing the 'holistic view' and 'ONE parallel call' behavior.

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?

The description explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view' and gives concrete example queries that trigger this tool. It does not offer explicit 'when not to use' guidance versus broader research tools like deep_research or compare_entities, but the supported query patterns and the emphasis on comprehensive profiles provide strong context.

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
Disambiguation3/5

Most tools have distinct purposes, but several overlap heavily: ask_pipeworx_beta explicitly matches ask_pipeworx exactly, ai_visibility_check is a single-entity version of scan_competitor_ai_presence, and multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk) share fuzzy boundaries. The lengthy descriptions help, but an agent could easily select the wrong tool.

Naming Consistency4/5

The overwhelming majority follow a clear verb_noun or noun_phrase pattern (ask_pipeworx, resolve_entity, validate_claim, generate_uuid, discover_tools). The ask_pipeworx_beta/ask_pipeworx_grounded variants and brand-name tools like pipeworx_trending are minor deviations, but the overall convention is remarkably consistent across 33 tools.

Tool Count2/5

33 tools is heavy for a single MCP server, especially one named 'Uuid' where only 2 tools (generate_uuid, validate_uuid) relate to the apparent name. The remaining 31 tools constitute a sprawling data-research platform that would normally be its own server. The count stretches beyond what is typically coherent.

Completeness3/5

As a data-research platform, the surface is quite complete: lookup, grounded answers, deep research, entity resolution, comparisons, claim validation, subscriptions, memory, and feedback are all covered. However, for the nominal 'Uuid' domain, only generation and validation exist — missing anything like UUID namespace generation, timestamp extraction from v1/v7, or bulk operations — creating a glaring mismatch between server name and actual tool scope.

Resources