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Glama

openalex

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

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, and the description adds substantial behavioral context: cross-source fanout, USPTO API-sunset soft-failure, sources_used/sources_failed semantics, empty sections meaning real 'no data,' and resolved:false behavior for private companies. This goes well beyond what the annotations alone convey.

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?

Although long, the description is densely informative and front-loaded with examples and the ALWAYS PREFER directive. Each sentence carries distinct operational detail, and the enumeration of response sections and failure modes is structured rather than rambling, so nothing feels redundant.

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 the return shape, and it does so by listing cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, and LEI. It also covers edge cases like empty FDA sections, USPTO soft-failure, and private-company notes, making the tool invocable and interpretable without further documentation.

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 adds practical examples ('AAPL', '0000320193', 'Moderna'), explains that `type` accepts 'company' or 'ticker' interchangeably, and states that `value` can be any of the three shapes. It also notes that company names resolve via SEC EDGAR, enriching the schema definitions with real usage context.

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 utterances like 'Tell me about X' and 'research Acme' and defines the tool as a 'full cross-source profile of a US public company in ONE parallel call,' giving a specific verb, resource, and scope. It also distinguishes itself from chained single-pack SEC/XBRL/news lookups, so an agent can tell it apart from narrower sibling tools.

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 explicitly instructs agents to 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' which directly states when to use this tool. It also clarifies accepted input forms and the expected behavior for private companies, leaving little ambiguity about routing.

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
Disambiguation1/5

Several tools are effectively indistinguishable or near-duplicates: ask_pipeworx_beta is explicitly an identical copy of ask_pipeworx with no active experimental changes, and ask_pipeworx_grounded is a variant of the same router. discover_tools, suggest_questions, deep_research, and the ask_pipeworx family also heavily overlap as discovery/answer surfaces, while bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_fill_risk create a dense prediction-market cluster with fuzzy boundaries.

Naming Consistency3/5

Everything is snake_case and many names follow a readable verb_noun pattern (search_works, resolve_entity, validate_claim, suggest_questions), but conventions are mixed: noun-first domain-prefixed names (polymarket_edges, pipeworx_feedback, pipeworx_trending), bare verbs (remember, forget, recall), and adjective_noun names (deep_research, recent_changes) coexist. The inconsistency is not chaotic, but it is not a unified scheme.

Tool Count2/5

36 tools is well over the 25-tool threshold for a heavy toolset, and the count is inflated by many tangential concerns: memory, subscriptions, feedback, trending, npm dependency scanning, AI visibility, and llms.txt generation. Only a small subset actually serves the stated OpenAlex/scholarly purpose, so the size feels bloated rather than well-scoped.

Completeness2/5

For a server named openalex, the scholarly surface is incomplete: works support search and fetch, but authors and institutions only support search with no get-by-ID, concepts support get but no search, and major OpenAlex resource types like sources, publishers, funders, and topics are absent. The many non-OpenAlex tools do not fill these gaps and instead dilute the domain coverage.

Resources