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particle_entity_resolve

Read-only

Resolve any named thing — person, company, place, or other entity — by free-text name in one union search. Each candidate carries a type and the canonical slug for that type:

  • person: the canonical person slug. Feed it into particle_person_get, every person_slug parameter (particle_podcast_find_mentions, particle_podcast_search_transcripts, particle_podcast_list_episodes), or particle_podcast_get_guest's guest_slug.

  • company: the canonical company slug. Feed it into particle_company_get and every company_slug parameter.

  • place/other: a bare entity slug. Feed it into the entity_slug parameter on particle_podcast_find_mentions, particle_podcast_search_transcripts, and particle_podcast_list_episodes to filter by that entity.

Use this first whenever you only have a name and don't know what kind of thing it names. If you already know it's a person, particle_person_resolve ranks people only; for companies with a known ticker, domain, CIK, or QID, particle_company_resolve has more identifier surface.

For bulk resolution, pass a comma-separated query (e.g. "sam altman, nvidia, davos") — each name is resolved independently in a single call and limit applies per query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum candidates per query (1-10, default 5).
queryYesFree-text name(s) of a person, organization, place, or company to resolve (e.g. 'sam altman', 'nvidia'). Case-insensitive. Comma-separated for bulk lookup (e.g. 'sam altman, kara swisher, marc andreessen') — each query is resolved independently and grouped in the response.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, and the description goes well beyond that by disclosing the return behavior: each candidate carries a type and canonical slug, slugs route into specific downstream tool parameters, and bulk queries are resolved independently with limit applying per query. No contradiction exists with 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.

Conciseness5/5

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

The description is front-loaded with a clear purpose, uses compact bullets for slug routing, and includes only decision-relevant guidance. Every section earns its place, with no fluff or repetition.

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?

Despite having no output schema, the description defines the essential contract: candidates expose type and canonical slug, and it maps each slug to the exact parameters and sibling tools that consume it. Combined with usage guidance and bulk behavior, the agent has everything needed to select and invoke the tool correctly.

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?

The input schema already provides 100% coverage of both parameters, including examples, case-insensitivity, default limit, and grouping behavior. The description's bulk and per-query-limit statements largely restate this, adding emphasis but little new semantic value.

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 immediately states a specific action and resource: resolving any named thing by free-text name in one union search. It clearly distinguishes itself from sibling resolve tools by covering person, company, place, and other entities, and by explaining that candidates carry a type and canonical slug.

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 explicitly tells the agent to use this tool first when only a name is known and the entity kind is unknown. It also names the alternatives for known-person and known-company cases, and gives concrete bulk-resolution usage guidance, making the selection criteria unambiguous.

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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