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Glama

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

The description goes well beyond the readOnly/openWorld/idempotent annotations by detailing fan-out behavior, source-specific failure modes (e.g., USPTO patent sunset soft-fail), the meaning of empty sections, and the resolved:false path for private companies. This gives the agent accurate expectations about partial results and genuine no-data cases.

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-structured: trigger examples and the 'ALWAYS PREFER' rule are front-loaded, followed by a scannable source/output breakdown. It is longer than average, but for a tool with this many return sections and edge cases, nearly every sentence earns its place; minor redundancy with the schema keeps it from a 5.

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 present, the description fully compensates by enumerating each returned field, explaining source fallback behavior, and addressing edge cases like non-public companies, absent drug categories, and source failure indicators. An agent has everything needed to correctly invoke the tool and interpret its response.

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, but the description adds useful nuance by specifying zero-padded CIK format, ticker/CIK/name interchangeability, and the fact that type is effectively a passthrough. These details help agents format input correctly beyond what the schema already states.

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 clearly identifies the tool's core function: producing a full cross-source profile of a US public company in one call, supported by varied trigger examples. It distinguishes itself from sibling tools by explicitly stating it should be preferred over chaining single-pack SEC/XBRL/news lookups, and the output section list makes its scope concrete.

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?

The description gives explicit usage guidance: use this over chained single-source lookups whenever the user asks for a holistic company view. It also clarifies acceptable inputs, how private companies are handled, and what empty sections mean, leaving little ambiguity about when or why to invoke it.

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

A3.9/5.0
Disambiguation3/5

Most tools have distinct responsibilities, but several clusters blur together: ask_pipeworx/ask_pipeworx_beta/ask_peworx_grounded are near-identical entry points, the five Polymarket tools overlap heavily, and ai_visibility_check vs scan_competitor_ai_presence overlap in purpose. An agent would need to read long descriptions carefully to avoid misselection.

Naming Consistency4/5

Names are almost entirely snake_case and mostly follow a verb_noun pattern. Minor inconsistency exists in prefixes and verb styles (ask_pipeworx vs pipeworx_feedback vs polymarket_arbitrage vs bet_research), but the naming is generally predictable and readable.

Tool Count2/5

34 tools is well above the 25+ threshold for a cohesive set, and the count is not justified by the server's apparent Anilist scope: the majority of tools are unrelated Pipeworx data-research, prediction-market, memory, and npm-scanning utilities. Many meta-tools could be consolidated.

Completeness2/5

For a server named Anilist, the anime surface is severely incomplete: only search_anime, get_anime, and trending_anime exist, with no seasonal, top-rated, studio, character, staff, or recommendation operations. The Pipeworx data side is fairly complete, but that does not serve the stated domain.