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particle_company_get

Read-only

Return a bundled profile for one company: identifiers (slug, ticker, domain, CIK, QID, linked entity), name, and description.

Request optional sections via include: 'people' for current leadership and notable people (person slugs feed particle_person_get), 'products' for the three-level product hierarchy, 'competitors' for the competitor list, 'external_links' for the company's LinkedIn, social profiles, domain, Wikidata QID, SEC CIK and tickers. The default response is lean — include only what you need.

For sponsor/advertising analytics on this company, use particle_company_get_podcast_ad_presence instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
includeNoOptional response sections: 'people' (current leadership and notable people), 'products' (three-level product hierarchy), 'competitors' (competitor list), 'podcast_recommendations' (the ten podcasts the company could advertise on next, with the shows it already buys that led there; premium), 'external_links' (LinkedIn, social profiles, domain, Wikidata QID, SEC CIK and tickers). Default response is lean — request only what you need.
company_slugYesCompany identifier — accepts slug (e.g. 'nvidia'), domain (e.g. 'nvidia.com'), or canonical ID. If you already know the domain you can call this tool directly without first running particle_company_resolve.
product_statusNoComma-separated lifecycle filter for include=products (e.g. 'active' or 'active,announced'). Allowed values: active, announced, discontinued, rumored. Defaults to 'active'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / include / description
      Previous value: -"Optional response sections: 'people' (current leadership and notable people), 'products' (three-level product hierarchy), 'competitors' (competitor list), 'podcast_recommendations' (the ten podcasts the company could advertise on next, with the shows it already buys that led there; premium). Default response is lean — request only what you need."New value: +"Optional response sections: 'people' (current leadership and notable people), 'products' (three-level product hierarchy), 'competitors' (competitor list), 'podcast_recommendations' (the ten podcasts the company could advertise on next, with the shows it already buys that led there; premium), 'external_links' (LinkedIn, social profiles, domain, Wikidata QID, SEC CIK and tickers). Default response is lean — request only what you need."
    • changedInput schema / properties / include / items / enum
      Previous value: -[
-  "people",
-  "products",
-  "competitors",
-  "podcast_recommendations"
-]New value: +[
+  "people",
+  "products",
+  "competitors",
+  "podcast_recommendations",
+  "external_links"
+]
  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 carry readOnlyHint=true, openWorldHint=true, and destructiveHint=false, lowering the bar. The description adds genuine context beyond those: the lean-default response shape, what each include section returns, the 'premium' flag on podcast_recommendations, and the cross-tool pointer that person slugs feed particle_person_get. It doesn't cover failure modes or rate limits, but for a read-only fetch with annotations present this is a strong addition.

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 front-loaded and each sentence earns its place: purpose in sentence one, optional-section semantics in sentence two, sibling routing in sentence three. The length is proportionate to the five optional include sections it must document, with zero filler.

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?

With no output schema, the description carries the burden of explaining return values, and it does — default payload contents (identifiers, name, description) and each optional section's contents, including the ten-podcast premium recommendation list. The only gaps are edge-case behavior (unknown slug, empty results) and what 'premium' means for the caller (billing/access implications), which are minor for a read-only fetch tool.

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%, so the baseline is 3 — the schema already documents include, company_slug, and product_status with detailed descriptions. The description adds marginal workflow nuance ('If you already know the domain you can call this tool directly without first running particle_company_resolve') and reiterates the include options, but does not meaningfully explain anything the schema leaves out.

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 opening sentence names the verb ('Return'), the resource ('a bundled profile for one company'), and enumerates the contents (identifiers, name, description). It explicitly distinguishes itself from siblings by closing with 'use particle_company_get_podcast_ad_presence instead' for sponsor/advertising analytics, matching the highest bar for 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit routing: use particle_company_get_podcast_ad_presence for sponsor/advertising analytics, and skip particle_company_resolve when the domain is already known ('you can call this tool directly without first running...'). It also prescribes behavior — 'The default response is lean — include only what you need' — leaving nothing about when/why to call it to inference.

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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