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pokemon

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

A4.6/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior, and the description adds substantial context beyond that: fan-out across specific government and corporate databases, soft-failure of the USPTO PatentsView API, GDELT-to-GNews fallback, empty sections meaning genuine no-data, and resolved:false for private companies. This is far more than annotations alone provide.

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 informative and front-loaded with intent examples and the key preference rule. Nearly every sentence carries operational meaning, though some trimming of the return-structure enumeration would improve readability without losing essential guidance.

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 exhaustively: sources_used/sources_failed semantics, each returned section with caveats, source-specific limitations, fallback chains, and resolution failures are all covered. The tool's complexity is matched by a complete, decision-ready description.

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. The description still adds value by showing accepted shapes (ticker, zero-padded CIK, or company name), clarifying that 'company' and 'ticker' are interchangeable, and explaining name resolution behavior and the private-company edge case.

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-intent examples and then states the core function: a 'full cross-source profile of a US public company in ONE parallel call.' It names the verb, resource, and scope, and differentiates itself from chaining single-source lookups.

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?

The description explicitly instructs to 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' and it notes the private-company case where resolution fails. This gives clear usage context, though it does not name specific sibling tools like compare_entities or resolve_entity as exclusions.

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

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta (explicitly described as identical), ask_pipeworx_grounded, and deep_research all handle natural-language data queries, while six polymarket_* tools cover prediction-market analysis with blurry boundaries. The four legitimate Pokemon tools are drowned out by dozens of unrelated Pipeworx utilities, making tool selection confusing.

Naming Consistency2/5

Tool names mix verb_noun (get_pokemon, ask_pipeworx), noun phrases (entity_profile, recent_changes), and bare verbs (forget, recall, subscribe) with no consistent pattern. Even the Pipeworx-related tools alternate between ask_pipeworx*, pipeworx_*, polymarket_*, and descriptive names, so the naming is a hodgepodge rather than a predictable system.

Tool Count2/5

At 35 tools, this exceeds the 25+ threshold for a coherent toolkit. The 'pokemon' server name implies a small, domain-specific set, yet 31 of the tools are unrelated Pipeworx data-research, prediction-market, and memory utilities, making the count wildly inappropriate for the apparent purpose.

Completeness2/5

For a Pokemon domain, the surface is skeletal: only get_pokemon, get_ability, get_type, and get_evolution_chain exist, with no moves, items, locations, or search/list capabilities. The extensive Pipeworx tools clearly belong to a different server altogether, creating a massive coherence gap for the stated 'pokemon' purpose.

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