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Glama

Datacite

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. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Despite annotations (readOnlyHint, openWorldHint, idempotentHint), the description adds substantial behavioral detail: it fans out across nine sources, explains that empty sections indicate real absence of data (not errors), discloses the USPTO API sunset and soft-fail, and clarifies that FDA products only cover biologics. This goes far beyond what annotations imply and gives the agent correct expectations on failure modes and partial results.

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?

The description is long but every sentence earns its place. It front-loads the most critical information (trigger phrases and the mandate to prefer this tool), then systematically enumerates the sources and return fields. The use of semicolons and clear delineation of sections (recent_filings, fundamentals, patents, etc.) keeps it navigable. No fluff or redundancy.

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 must explain the return structure and edge cases, and it does so thoroughly: it lists each section of the response (cik, company_name, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI), explains the URI format, and covers failure modes (sources_failed, empty sections, private company). An agent has everything it needs to call the tool correctly and interpret results.

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 already provides 100% coverage for both parameters, so baseline is 3. The description adds value by clarifying that type accepts 'company' and 'ticker' interchangeably, and that value can be a ticker, CIK, or company name with resolution via EDGAR. It also notes private-company behavior. This is meaningful extra context, but it still leans on the schema for the basic parameter shapes, so a 4 is appropriate.

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 trigger phrases and states the core function: a full cross-source profile of a US public company in one parallel call. It explicitly contrasts with chaining single-pack lookups, clearly distinguishing it from sibling tools like resolve_entity or deep_research. Every sentence reinforces the purpose, making it unmistakable what this tool does.

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?

Provides explicit when-to-use guidance: 'when the user asks for a holistic view' and 'ALWAYS PREFER over chaining single-pack...' It also covers when not to expect data (private companies, FDA small-molecule only) and explains fallback behavior. The examples of natural-language triggers are practical and help an agent recognize the right context. This is a model of usage 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

A3.7/5.0
Disambiguation2/5

Several tools occupy overlapping roles: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical entry points (beta currently behaves exactly like stable), while entity_profile, recent_changes, and compare_entities all target company research. The prediction-market cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) is large enough that an agent could easily select the wrong one.

Naming Consistency2/5

Naming conventions are mixed throughout: verb_noun patterns (search_dois, list_repositories, validate_claim) coexist with noun phrases (entity_profile, recent_alerts, ai_visibility_check) and bare verbs (remember, recall, forget, subscribe). The only consistent thread is snake_case, but the grammatical style is unpredictable.

Tool Count2/5

At 34 tools, the server is overstuffed, especially given its apparent focus is DataCite but only 3 tools actually serve that domain (get_doi, search_dois, list_repositories). The rest are a grab bag of Pipeworx data routing, prediction-market analytics, memory, and subscription utilities that would be better split into separate focused servers.

Completeness2/5

The DataCite surface is incomplete (no DOI creation, update, or deletion despite DataCite supporting registration), while the Pipeworx surface has no direct fetch-by-URI tool for the pipeworx:// citations that other tools return. The presence of a duplicate beta router and a beta router with no active differences further muddies the coverage picture.

Resources