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Glama

Medicare Coverage

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?

Annotations carry the safety profile (read-only, idempotent, non-destructive, open-world), so the bar is lower and the description clears it easily. It adds substantial behavioral context beyond the annotations: the fan-out across 8 named sources, the USPTO PatentsView API sunset soft-fail, expected-empty semantics for fda_products ('that is expected, not a failure'), sources_used/sources_failed interpretation, the GDELT→GNews fallback chain, and the resolved:false+notes behavior 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.

Conciseness3/5

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

Front-loading is strong — triggers and purpose come first — and every sentence earns its place with unique information. But the whole thing is one dense run-on paragraph with no section breaks; return-field catalog, failure semantics, and input formats are interleaved. For a mission-critical definition this structural sloppiness hurts skimmability, though an LLM agent can still parse it. It is dense-but-messy, not elegantly concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a high-complexity tool — multi-source fan-out, roughly 13 documented return sections, no output schema to lean on — and the description carries nearly the entire burden. It covers every return section by name, enumerates all data sources, explains failure modes (sources_failed, expected-empty, soft-fail), input variants, and the private-company edge case. Minor gaps: section shapes are only named rather than specified, and non-US company behavior is not explicitly addressed beyond 'US public company' in the purpose. Impressively complete given the constraints.

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 baseline is 3. The description adds genuine value by emphasizing that type accepts 'company' or 'ticker' interchangeably and that both accept the same value shapes, then enumerating those shapes (ticker, zero-padded CIK, company name). It also ties parameter behavior to output semantics (resolved_from/resolved_to when value was a name) and to the SEC EDGAR resolution flow. This exceeds what the schema alone conveys.

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 trigger phrases ('Tell me about X', 'research Acme', 'brief me on Tesla') and then states the resource precisely: 'full cross-source profile of a US public company in ONE parallel call'. This is a specific verb-resource-scope combination. It also implicitly distinguishes from granular siblings by explicitly contrasting with 'chaining single-pack SEC/XBRL/news lookups', and the sibling list shows clearer alternatives exist.

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?

Provides an explicit preference rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view', with the condition stated. The trigger examples delimit the input family. However, it never states when NOT to use it explicitly — e.g., when the user wants only a single filing or one specific metric — and with 60+ siblings only one alternative is named; the exclusion is implied by 'holistic view' rather than stated.

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

A3.5/5.0
Disambiguation2/5

The server mixes Medicare-specific tools with many unrelated general-purpose tools (e.g., bet_research, polymarket_arbitrage, remember), and there are multiple similar ask_pipeworx variants. This makes it difficult for an agent to distinguish which tool is appropriate for a given task without confusion.

Naming Consistency2/5

Tool names follow no consistent pattern. Some use a medicare_ prefix with underscores, others use generic verbs like forget, recall, or compound names like ask_pipeworx, deep_research. There is no uniform verb_noun or noun_verb structure.

Tool Count2/5

57 tools is excessive for a server ostensibly focused on 'Medicare Coverage'. Many tools (e.g., bet_research, polymarket_edge_tracker, scan_dependency) are unrelated to Medicare and should be in separate servers, inflating the count and diluting focus.

Completeness4/5

The Medicare-specific tools cover a broad range: NCDs, LCDs, NCAs, enrollment, DME, Part D, hospital, outpatient, post-acute, and provider data. Minor gaps include Medicare Advantage (Part C) and Medicare Supplement, but the coverage is largely comprehensive.

Resources