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Glama

Data Mesa

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive, so the safety profile is covered. The description adds meaningful context beyond annotations: parallel fan-out across multiple sources, resolved_from/resolved_to behavior, empty-section semantics (real no data vs bug), and graceful handling of private companies. It doesn't document every source detail, but it covers the important behavioral surprises.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is information-dense and front-loaded with usage triggers, but it is largely a single long paragraph with many compressed clauses and parentheticals. Every sentence carries information, yet the density hurts skimmability; an agent parsing it quickly could miss details like the `sources_used`/`sources_failed` semantics. It earns a 3: no fluff, but structure is suboptimal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex fan-out tool with no output schema, the description is quite complete: it enumerates source domains, return sections, input formats, resolution behavior, private-company fallback, and error interpretation. It doesn't deeply describe pagination or the exact shape of `fundamentals`, but the `sources_failed`/`sources_used` disclosure compensates for much of the uncertainty an agent would otherwise have.

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 coverage is 100%, so the baseline is 3. The description, however, adds substantial semantic value: it clarifies that `type` is effectively redundant, that `value` accepts the same shapes regardless of `type`, and that company-name resolution goes through SEC EDGAR. It also hints at future person/place support. This goes beyond the schema's own descriptions.

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 names a specific verb+resource: generate a full cross-source profile of a US public company in one parallel call. It clearly distinguishes itself from targeted lookups (SEC/XBRL/news) and from sibling tools like resolve_entity by emphasizing the holistic, one-call aggregation.

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?

Explicitly says ALWAYS PREFER over chaining single-pack lookups when a holistic view is requested, which is a crisp selection rule. It also specifies accepted input shapes (ticker, CIK, company name), behavior for private companies, and what `type` values are interchangeable. No ambiguity remains about when or how to invoke it.

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

A3.6/5.0
Disambiguation3/5

Several tool groups have overlapping purposes (e.g., ask_pipeworx variants, polymarket research tools, visibility checks), despite detailed descriptions. An agent may struggle to choose between closely related options.

Naming Consistency2/5

Tool names mix snake_case verb_noun patterns (e.g., ai_visibility_check, compare_entities) with noun-heavy compound names (e.g., polymarket_arbitrage, deep_research) and single-word names (e.g., query, recall). No consistent convention.

Tool Count2/5

With 34 tools, the surface is heavy for a research/data server. Many tools are variants of core capabilities (ask_pipeworx, polymarket edges), suggesting consolidation would improve usability.

Completeness3/5

The server covers a broad range: data research, prediction markets, entity profiles, monitoring. However, the abundance of specialized variants and gaps in unified workflows (e.g., needing separate tools for simple vs grounded queries) reduce completeness.

Resources