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Glama

Opencages

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

A4.1/5.0
Behavior5/5

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

Even with readOnly/openWorld/idempotent annotations present, the description adds substantial behavioral disclosure: it lists all fanned-out sources, explains that USPTO patents soft-fail after the May 2025 sunset, clarifies empty sections are real 'no data, not a bug,' and describes the resolved:false private-company path. It also documents fallbacks (GDELT→GNews) and the meaning of sources_used/sources_failed, going well beyond the 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 front-loaded with trigger phrases and the 'ALWAYS PREFER' directive, then moves through return fields and caveats. It is dense and every sentence contributes useful information, but it is one long semicolon-heavy paragraph that would benefit from bullets or headings; appropriate for the complexity, though not optimally structured.

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 thoroughly covers the return envelope: cik/company_name, recent_filings with URIs, fundamentals, patents, federal contracts, FDA products, hiring, news, LEI, sources_used/sources_failed, and resolved:false behavior. It even explains expected empty results (small-molecule-only companies get no FDA products), making it complete enough for an agent to correctly interpret both successes and non-failures.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%: the schema already explains that type accepts 'company' or 'ticker' interchangeably and that value can be a ticker, zero-padded CIK, or company name resolved via SEC EDGAR. The description restates the same examples and adds phrasing like 'names now resolve via SEC EDGAR's company-name match,' but this is effectively a duplicate of the schema rather than genuinely new parameter semantics.

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?

The description clearly specifies a verb-and-resource scope: produce 'a full cross-source profile of a US public company' in one parallel call, with trigger examples like 'Tell me about X' and 'company profile for Microsoft.' It is not a tautology and is distinguishable from generic lookups, but it does not explicitly differentiate itself from siblings such as deep_research or compare_entities, so clarity stops short of excellent sibling differentiation.

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?

It provides strong when-to-use guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' plus concrete query examples. It also sets expectations for edge cases (private companies, no FDA products), but it does not explicitly state when to choose a sibling like deep_research or compare_entities instead.

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 occupy adjacent territory—ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions, and the Polymarket family has five overlapping analysis tools. The descriptions do a decent job of differentiating them, but an agent could still plausibly select the wrong variant in a mixed workflow.

Naming Consistency3/5

Most names follow a readable lowercase snake_case style, and there are coherent families like ask_pipeworx_*, polymarket_*, and pipeworx_*. However, conventions are mixed across the set—some are verb_noun (list_subscriptions, resolve_entity), some are bare verbs (forget, recall, reverse), and some are noun-phrase-only (entity_profile, recent_alerts)—so no single predictable pattern governs the whole server.

Tool Count2/5

33 tools is well past the 25+ threshold for a heavy tool surface, even accounting for the broad data-domain ambitions of the server. Many of these tools are meta-tools or thin variants of one another, so the set feels larger than necessary and imposes meaningful selection cost on an agent.

Completeness4/5

The server covers its apparent domain thoroughly: querying, grounded verification, deep research, entity resolution, profiles, comparisons, change tracking, claim validation, memory, subscriptions, and prediction-market analytics are all represented. Minor gaps exist—such as no direct tool for retrieving a raw pipeworx:// citation record and no account/auth flow—but agents can generally complete core workflows without dead ends.

Resources