Skip to main content
Glama

Healthdata

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, but the description adds substantial context beyond that: the fan-out across SEC EDGAR, XBRL, USPTO, USAspending, Purple Book, DOL LCA, news, and GLEIF; the meaning of sources_used/sources_failed; the 'empty section is real no-data, not a bug' convention; and the resolved_from/resolved_to behavior. 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.

Conciseness3/5

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

The content is mostly necessary and front-loaded with trigger phrases and the one-call value proposition, but the long source/return list is written as a dense run-on sentence that mixes inputs, outputs, and caveats. Several details are repeated between description and schema, and the examples could be trimmed without losing value.

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 tool with no output schema, the description enumerates the output sections, source families, filing URI format, resolution behavior, private-company handling, and the sources_used/sources_failed signal. An agent has enough information to call the tool correctly and interpret empty or partial results.

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 both parameters are already documented. The description adds value by stating that type='company' and type='ticker' are interchangeable, that value can be a ticker, zero-padded CIK, or company name, and what happens when a name resolves or when the entity is private. It repeats some schema detail, which prevents 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?

The description states a specific action — 'full cross-source profile of a US public company in ONE parallel call' — and immediately distinguishes itself from chaining single-pack SEC/XBRL/news lookups. Trigger examples like 'Tell me about X' and 'company profile for Microsoft' make the resource and scope unmistakable, and siblings like compare_entities or resolve_entity are implicitly separated by the one-company-many-sources focus.

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?

Gives explicit trigger phrases and an explicit preference rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clarifies behavior for private companies, returning resolved:false with notes rather than a bare failure, so an agent knows this is ultimately a US public-company profile tool and how to interpret non-resolving input.

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

All Healthdata 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

A3.8/5.0
Disambiguation3/5

Most tools have clearly described distinct purposes, but a few near-duplicates exist: ask_pipeworx and ask_pipeworx_beta are functionally identical right now, and ai_visibility_check vs scan_competitor_ai_presence overlap. The polymarket sub-family also has multiple edge/fill tools that could be confused.

Naming Consistency3/5

The naming is varied but readable. Many tools follow verb_noun (ask_pipeworx, resolve_entity, search_within, subscribe), yet several are noun phrases (entity_profile, recent_changes, polymarket_edges, bet_research, pipeworx_feedback). There's no single coherent pattern, but the mixture is not chaotic.

Tool Count2/5

At 33 tools, the server is oversized for typical MCP coherence. The breadth is broad (prediction markets, healthcare datasets, memory, subscriptions), but such a large count forces agents to filter through many utilities (suggest_questions, discover_tools, generate_llms_txt) that could be consolidated or hidden.

Completeness4/5

The domain—data querying and analysis—is well covered: universal routing (ask_pipeworx), grounded verification, deep research, entity resolution, dataset metadata, subscriptions, prediction-market edge checks, and memory tools. Minor gaps exist (e.g., no direct health-care-specific analytics batch or file download), but no major dead ends for core workflows.

Resources