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Glama

animequotes

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

The description goes far beyond the annotations. It details the fan-out across multiple sources, explains that empty sections mean 'no data' not a bug, discloses that the USPTO PatentsView API is sunsetting and will soft-fail, and explains the behavior for private companies (resolved:false with a notes line). It also describes the return structure including sources_used/sources_failed. This is rich behavioral context that annotations (readOnlyHint, openWorldHint, etc.) do not provide, and there is no contradiction.

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 long but densely packed with necessary information. It front-loads the purpose and usage with examples, then systematically lists the output components. Every sentence contributes to agent decision-making: input formats, source coverage, failure modes, and edge cases. While it could be tightened, it is not verbose or redundant; the length is justified by the tool's complexity. It earns a 4 for efficiency given the content volume.

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?

Given the tool's complexity (multiple sources, 2 parameters, no output schema), the description is remarkably complete. It covers all input variants, explains the output structure in detail (each source section), and explicitly addresses failure/empty cases and API deprecations. An agent has everything needed to call this tool correctly and interpret the results. Even without an output schema, the description specifies the return fields sufficiently.

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 schema already documents both parameters with examples. The description adds value by clarifying that 'company' and 'ticker' are interchangeable and that `value` can be any of ticker, CIK, or name, with names resolving via SEC EDGAR. This removes potential confusion about parameter semantics beyond the schema's enum and type definitions. It doesn't add syntax details that are missing from the schema, but it confirms and elaborates on the acceptance criteria, earning slightly above the baseline 3.

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 prompts and then states a specific purpose: 'full cross-source profile of a US public company in ONE parallel call.' It clearly names the resource (US public company) and the verb (profile/research), and distinguishes it from chaining single-pack lookups by explicitly saying 'ALWAYS PREFER over chaining...' This makes it unmistakable what the tool does and how it differs from siblings like deep_research or resolve_entity.

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?

It provides explicit when-to-use guidance ('when the user asks for a holistic view'), and explicitly names the alternative (single-pack SEC/XBRL/news lookups) that should be avoided in favor of this tool. It also gives specific input formats (ticker, CIK, or name) and explains the resolution process. This is comprehensive and leaves no ambiguity about when to invoke this tool versus others.

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

A3.8/5.0
Disambiguation2/5

Several tools occupy blurred boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to overlapping data pipelines, and the Polymarket family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) heavily overlaps in purpose. Individual descriptions are detailed, but an agent must read long text to avoid misselection, especially when the three anime-quote tools are surrounded by unrelated tool families.

Naming Consistency3/5

Most tools use snake_case and many begin with verbs (ask_, search_, resolve_, scan_, compare_, validate_), but several are noun-first or noun-phrase names like entity_profile, bet_research, random_quote, recent_alerts, recent_changes, and pipeworx_trending. There is no chaotic camelCase/snake_case mix, but the convention is not applied consistently enough for a predictable pattern.

Tool Count2/5

34 tools is heavy for any single-purpose server, and the vast majority have nothing to do with anime quotes—they are Pipeworx data tools, prediction-market tools, subscription tools, memory tools, and AI-audit tools. For a server named animequotes, only random_quote, search_by_anime, and search_by_character fit the stated purpose, making the count wildly disproportionate.

Completeness3/5

For the apparent anime-quote domain, random_quote, search_by_anime, and search_by_character cover basic lookup but leave notable gaps: no search by quote text, no quote-by-id fetch, no ability to list all series or characters, and no pagination or metadata browsing. The unrelated tools do not fill these gaps, so the anime-quote surface is functional but incomplete.

Resources