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Glama

Hgnc

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

A4.5/5.0
Behavior5/5

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

Annotations cover the safety profile (readOnly, idempotent, openWorld, non-destructive), and the description adds rich behavioral context beyond them: the soft-fail for USPTO patents, the GDELT→GNews fallback, the expectation-setting that fda_products will legitimately be empty for small-molecule companies ('that is expected, not a failure'), and the 'empty section is a real no data, not a bug' semantics for sources_used/sources_failed. It also discloses the resolved:false + notes behavior for private companies instead of a bare failure. This is exemplary edge-case disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with trigger phrasings and the core purpose, and nearly every sentence carries information. However, the return-value enumeration is one massive run-on sentence that is hard to scan, and the eight example phrasings at the start are somewhat redundant. It is comprehensive but structurally dense; a list or shorter clauses would improve parseability without losing content.

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 complex multi-source fan-out tool with no output schema, the description carries the full burden of explaining return values, and it does so thoroughly: the source list, the return fields (cik, company_name, recent_filings with URIs, fundamentals, patents, contracts, fda_products, hiring, news, LEI), the sources_used/sources_failed semantics, and both edge cases (private companies, expected-empty sections). Nothing an agent needs to correctly set expectations is missing.

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; both parameters already have descriptive schema text. The description adds value beyond that: concrete accepted values ('AAPL', '0000320193', 'Moderna'), the explicit statement that type values are interchangeable, name-resolution behavior via SEC EDGAR, and the resolved_from/resolved_to output behavior when a name is passed. The examples and resolution semantics genuinely aid parameter selection.

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 ('Tell me about X', 'brief me on Tesla', 'everything you know about $TICKER') followed by a precise statement of function: 'full cross-source profile of a US public company in ONE parallel call'. It specifies the resource (US public company), the action (cross-source profile), and the scope (SEC, XBRL, patents, contracts, FDA, H-1B, news, GLEIF), making it unmistakable what this tool does and how it differs from a narrow single-source lookup.

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?

Gives explicit routing guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' This states the condition (holistic view) and what to prefer it over. However, it does not name specific sibling tools (e.g., resolve_entity for a bare CIK lookup, compare_entities for comparisons), so an agent must infer the full boundary against the 30+ siblings rather than being told.

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
Disambiguation3/5

Most tools have clearly distinct purposes, but the set is large and several boundaries are fuzzy: ask_pipeworx_beta is currently identical to ask_pipeworx, and the five polymarket_* tools plus ai_visibility_check/scan_competitor_ai_presence create selection ambiguity. The exhaustive descriptions mitigate confusion, but an agent could still easily pick the wrong variant.

Naming Consistency4/5

All tool names use snake_case and most follow a verb_noun pattern (get_gene, search_genes, validate_claim, subscribe/unsubscribe, compare_entities). The deviations are minor and internally consistent: noun-first family names like polymarket_edges/entity_profile and the ask_pipeworx_* variant group.

Tool Count2/5

34 tools is well above the comfortable range, and the server is named Hgnc while only 3 of its tools actually concern HGNC genes. The Pipeworx platform is bolted onto what should be a narrow gene-lookup surface, making the set feel bloated and mis-scoped.

Completeness4/5

For the HGNC domain, search_genes → get_gene → resolve_xref covers the core lookup lifecycle well. For the broader Pipeworx functionality the surface is remarkably thorough, with ask, grounded, deep research, compare, validate, subscribe, and memory tools leaving only minor gaps such as batch gene listing.

Resources