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Glama

Unpaywall

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description goes well beyond those by revealing parallel fan-out behavior, soft-failures (USPTO PatentsView sunset), fallback chains (GDELT→GNews), expected-empty sections for FDA products, and the semantics of sources_used/sources_failed. This gives an agent an accurate model of how the tool behaves in the real world.

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 every clause earns its place by defining a result section, a failure mode, or an input rule. It front-loads the usage trigger and the ALWAYS-PREFER directive before enumerating sources. The enumeration is dense but well-structured; a little more paragraphing would help, but there is no fluff.

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 stating what the tool returns, and it does so comprehensively: each named output field (cik, resolved_from/to, recent_filings, fundamentals, patents, contracts, fda_products, hiring, news, LEI, sources_used/failed). It even covers edge cases like private companies and empty FDA sections. Nothing an agent needs to call or interpret the tool is missing.

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?

Although schema coverage is 100%, the description adds meaning beyond the schema: it clarifies that type='company' and type='ticker' are interchangeable, that value accepts three distinct shapes, that names resolve via SEC EDGAR, and that zero-padding matters for CIKs. The schema only documents the parameter names; the description explains how to actually populate them.

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?

Purpose is immediately clear: one parallel call produces a comprehensive multi-source profile of a US public company. The trigger-phrase examples ('Tell me about X', 'research Acme', 'brief me on Tesla') make it obvious which user intents map to this tool, and the 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' sentence sharply distinguishes it from narrower retrieval 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?

The description explicitly says to prefer this tool over chaining single-source lookups for holistic company questions, and it spells out accepted input forms (ticker, CIK, name). It also tells the agent how to recognize out-of-scope cases like private companies ('resolved:false') so the tool is not misused. This is outstanding when-to-use guidance.

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
Disambiguation3/5

Several tools overlap in purpose, especially the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) which all answer factual questions but differ in grounding/depth. The beta version is explicitly identical to the stable one currently, creating confusion. The Polymarket analysis tools also have overlapping scopes, though their descriptions help differentiate them. Overall, most tools have distinct roles but the heavy overlap in the query router cluster makes misselection a real risk.

Naming Consistency3/5

Tool names mix several conventions: bare verbs (remember, subscribe, forget), verb_noun (compare_entities, resolve_entity), noun_phrase (entity_profile, pipeworx_feedback), and a brand-prefixed family (ask_pipeworx*, polymarket_*). While some prefixes are consistent, the overall pattern is inconsistent and not predictable. Names are readable but do not follow a single style.

Tool Count2/5

With 33 tools, the server is heavily over-scoped for its name 'Unpaywall', which implies a narrow open-access search utility. Even though the actual functionality is broad, the tool count is excessive and will overwhelm agents. Many tools (llms_txt generation, dependency scanning, memory) are unrelated to the core data-query function.

Completeness4/5

For the actual domain revealed by the descriptions—a comprehensive data research and prediction-market gateway—the tool surface is quite complete: it covers discovery, retrieval, grounding, entity resolution, comparison, validation, subscriptions, memory, and prediction-market analysis. Minor gaps exist (e.g., no subscription update tool, no direct tool to list all data packs), but agents can work around them. If the domain is strictly 'Unpaywall/open access', it's severely incomplete, but the descriptions clearly indicate a broader scope.

Resources