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Glama

Tvmaze

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

A5/5.0
Behavior5/5

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

Even with readOnly/openWorld/idempotent annotations, the description goes far beyond them: it discloses the parallel fan-out across SEC/XBRL/USPTO/federal contracts/FDA/H-1B/news/GLEIF, the GDELT→GNews fallback, USPTO soft-fail after sunset, and that empty sections are real 'no data' rather than errors. It also states private-company return behavior explicitly.

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

Conciseness5/5

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

Despite being long, every block earns its place: query examples, source list, return keys, expected empty behavior, and input forms are all necessary because there is no output schema. The text is front-loaded with purpose and examples, then structured by data domain. This is appropriately sized for the tool's complexity.

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?

With no output schema, the description carries the full burden of explaining return values, and it does so thoroughly: cik/company_name, recent_filings with URIs, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, plus sources_used/sources_failed. It even explains expected non-results so the agent won't misreport an empty section as a bug.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description still adds meaning: ticker, zero-padded CIK, or company name are explained with examples, along with SEC EDGAR name resolution and resolved_from/resolved_to behavior. It also clarifies that `type` values are interchangeable and take the same value shapes.

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 queries and defines the tool as 'full cross-source profile of a US public company in ONE parallel call,' naming the exact resource and scope. It clearly differentiates this from sibling tools like compare_entities or deep_research by focusing on single-entity profile assembly from many sources.

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?

Explicitly instructs to 'ALWAYS PREFER' this tool over chaining single-pack SEC/XBRL/news lookups when the user wants a holistic view, with example queries that map to the trigger. It also tells the agent what to expect for private companies (resolved:false plus notes), helping decide whether the tool applies.

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

A4.1/5.0
Disambiguation4/5

Tools cover a wide range of domains (TV, finance, betting, memory, etc.), each with detailed descriptions that help distinguish their purposes. However, some overlap exists between related tools like bet_research, polymarket_arbitrage, and polymarket_edges, though the descriptions clarify their distinct uses.

Naming Consistency4/5

Most tool names follow a consistent verb_noun pattern with underscores (e.g., get_show, list_episodes, resolve_entity). A few tools use single imperative verbs (remember, recall, forget), which is a minor deviation but still clear.

Tool Count3/5

The server has 31 tools, which is on the higher side. While each tool has a defined purpose, the scope seems too broad for a server named 'Tvmaze', as many tools are unrelated to TV (e.g., company analysis, betting, memory). A more focused set would improve coherence.

Completeness4/5

For a general-purpose data server, the tool set covers a wide range of tasks: TV show lookups, company research, betting analysis, memory management, and more. The inclusion of ask_pipeworx as a fallback for many queries compensates for missing specific tools. However, the TV-specific functionality is limited to search and schedule, missing details like cast or crew.

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