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Glama

Paperswithcode

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

A4.7/5.0
Behavior5/5

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

With readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false all present, the safety profile is already clear. The description then goes well beyond annotations by disclosing fan-out behavior, soft-failure of USPTO PatentsView, the meaning of sources_used/sources_failed, empty-section semantics, private-company behavior, and name resolution. This is exactly the behavioral context an agent needs.

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 front-loaded with usage triggers, but it is long and somewhat run-on, with many semicolon-separated return-field explanations packed into one paragraph. Every sentence adds substance, yet the structure could be tightened through lists or clearer separation between invocation, return fields, and failure semantics.

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?

There is no output schema, so the description carries the full burden of explaining return structure. It enumerates every section (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI) plus sources_used/sources_failed and resolved:false behavior. It also covers edge cases like FDA no-data expectations and soft-failed sources. Nothing practically needed to invoke and 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?

Schema coverage is 100%, but the description meaningfully extends the schema by giving concrete example values, explaining zero-padded CIK format, stating that company names resolve via SEC EDGAR, and clarifying that 'company' and 'ticker' are interchangeable. This goes beyond restating the schema and helps an agent construct valid calls.

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 names a clear verb-resource pair ('full cross-source profile of a US public company'), gives concrete example queries, and distinguishes itself from matching siblings by saying it should be preferred over 'chaining single-pack SEC/XBRL/news lookups' for holistic questions. This lets an agent confidently route user intents like 'research Acme' here.

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 states when to use the tool ('ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view') and gives trigger-phrase examples. It does not, however, contrast with potentially overlapping siblings like deep_research or compare_entities, so the comparative guidance is incomplete.

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

ask_pipeworx and ask_pipeworx_beta are currently identical in behavior, creating direct overlap. Additionally, the five query/research tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim, bet_research) and six Polymarket tools have heavily overlapping boundaries that require reading long descriptions to disambiguate.

Naming Consistency3/5

Names are uniformly snake_case and mostly readable, with consistent domain prefixes (polymarket_*, pipeworx_*) and a verb_noun majority (get_paper, search_papers, resolve_entity). However, bare-verb memory tools (remember, recall, forget) and adjective-noun names (recent_alerts, trending_papers, deep_research) break the dominant convention, making the set mixed though not chaotic.

Tool Count2/5

At 35 tools, this exceeds the 25+ threshold for 'too many,' and several are near-duplicates or overlapping query modes (ask_pipeworx vs ask_pipeworx_beta vs ask_pipeworx_grounded). The server bundles paper search, a universal data router, prediction-market analytics, memory, subscriptions, dependency scanning, and AI visibility into one surface, which feels over-scoped.

Completeness4/5

The surface is quite thorough for its broad domain: querying has grounded/deep/meta variants, companies have profile/compare/change/claim tools, prediction markets have research/edges/arbitrage/fill-risk/spread tracking, and subscriptions/memory have full lifecycles. Minor gaps exist (no full pack catalog listing, no direct fetch-by-URI tool, no paper leaderboards), but agents can work around them via discover_tools/ask_pipeworx.

Resources