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Glama

Oecd

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

A4.5/5.0
Behavior5/5

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

Annotations already mark the tool read-only, open-world, idempotent, and non-destructive, and the description adds substantial behavioral detail: source fan-out, sources_used/sources_failed semantics, USPTO sunset soft-fail, expected empty FDA results, GDELT→GNews fallback, and resolved:false notes 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.

Conciseness4/5

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

The description is dense and heavily front-loaded with trigger phrases followed by precise output-section semantics. It is long, but almost every clause carries information needed because there is no output schema; it would benefit from light structuring, so it is not 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?

Given the high complexity, sparse schema, and absent output schema, the description is remarkably complete: it enumerates all data sources, output sections, fallbacks, failure semantics, and input-resolution rules. An agent has enough context to call it correctly and interpret results.

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?

Even though schema coverage is 100%, the description adds meaning beyond it: ticker vs zero-padded CIK vs name examples, SEC EDGAR name resolution, type/value interchangeability, and what happens for private companies. This materially helps an agent construct correct calls.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description is explicit that the tool produces a full cross-source profile of a US public company and leads with natural-language triggers, so purpose is clear. It orients against 'chaining single-pack... lookups' but does not name sibling tools like resolve_entity or compare_entities, so differentiation is slightly indirect.

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?

Gives an explicit 'ALWAYS PREFER... when the user asks for a holistic view' rule and clarifies inputs and private-company behavior. It stops short of naming alternative tools for non-holistic or comparison/entity-resolution requests, so the when-not-to-use guidance is implied rather than explicit.

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
Disambiguation2/5

Several near-duplicate lookup and prediction-market tools make selection ambiguous: ask_pipeworx_beta deliberately mirrors ask_pipeworx, and the five polymarket_* tools plus bet_research all target the same general 'should I bet / where is the edge' use case. The descriptions are detailed, but at the set level an agent must read extensive disambiguation essays to avoid picking the wrong tool.

Naming Consistency3/5

The set is uniformly snake_case, and subfamilies like ask_pipeworx*, polymarket_*, and subscribe/unsubscribe are internally consistent. However, conventions vary widely: verb_noun (fetch_dataset, validate_claim), noun phrases (entity_profile, bet_research), bare verbs (remember, recall, forget), and prefix-branded meta tools (pipeworx_feedback, pipeworx_trending) all coexist without a single predictable pattern.

Tool Count2/5

34 tools for a server nominally called 'Oecd' vastly exceeds the scope implied by the name and crosses the 25+ too-many threshold. Many tools belong to unrelated domains such as Polymarket arbitrage, npm dependency scanning, llms.txt generation, and AI visibility audits, making the set feel like a broad dumping ground rather than a focused tool server.

Completeness4/5

Within its sprawling domains the tool surface is fairly complete: lookup, grounded verification, deep research, entity resolution/profile/comparison, memory, subscriptions, alerts, and OECD dataflow search/list/fetch are all represented. There are minor gaps such as lack of direct OECD metadata descriptions or deeper navigation of the 5,708 underlying tools, but most workflows can be completed without dead ends.

Resources