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Glama

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

A4.8/5.0
Behavior5/5

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

With no output schema, the description carries the full burden and delivers: it enumerates every return section, flags the patents API sunset as a soft-fail, explains fda_products can legitimately be empty, and states that sources_used/sources_failed distinguish real no-data from bugs. This goes far beyond the readOnly/openWorld/idempotent 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 long, but it front-loads trigger phrases, the preferred-use directive, and the input contract before enumerating output sections. Given the tool's breadth and the absence of an output schema, the density is justified, though it partially restates schema-defined information.

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?

Given high complexity and no output schema, the description covers inputs, parallel fan-out, every return field, source availability, expected-empty cases, API fallback, and private-company failure behavior. An agent can predict both invocation outcomes and response interpretation correctly.

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?

The schema already covers both parameters at 100%, so the baseline is 3. The description adds value with concrete examples ('AAPL', '0000320193', 'Moderna'), explains that type and value are effectively interchangeable, and details how name resolution and private-company failures behave. It overlaps with the schema but adds operational meaning.

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 identifies a specific deliverable: 'full cross-source profile of a US public company in ONE parallel call.' It clearly distinguishes the tool from narrower single-source SEC/XBRL/news lookups and from other profile-style siblings by its breadth and single-call behavior.

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?

It explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view' and lists canonical query forms. It also clarifies accepted input shapes and the private-company fallback, so an agent knows when invocation will still produce a useful response.

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

A3.5/5.0
Disambiguation3/5

Most tools have distinct, well-described purposes, but clusters like ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded and the five polymarket_* tools have overlapping scopes that could cause misselection. The beta currently behaves identically to the stable router, and `recent` vs `recent_changes` vs `recent_alerts` are confusingly similar names for different domains.

Naming Consistency2/5

All names use lowercase underscores, but there is no consistent verb_noun pattern: some are verbs (ask, compare, generate), some are nouns (entity_profile, recent, user), and some are adjectives (deep_research). There are coherent subfamilies (subscribe/unsubscribe/list_subscriptions, remember/recall/forget), but the overall naming is a mix of conventions.

Tool Count2/5

33 tools is well above the 25+ threshold, making the surface heavy and hard to navigate. Many are highly specialized meta-tools (e.g., five Polymarket analyzers) that could be consolidated.

Completeness2/5

For a server named 'Codestats', only `recent` and `user` address coding stats, leaving major gaps in what that name implies. The broader data/research/betting capabilities are fairly rich, but the server's stated identity is under-served and there's no clear lifecycle coverage for any single domain.