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Particle Expertise Get

particle_expertise_get
Read-only

Describe one occupation or field of research and, on request, who is booked under it: its parent, children and crosswalk by default; include: ["top_guests"] ranks the guests listed under it by podcast appearances in a window (default the last 30 days, at most 90), and include: ["top_podcasts"] ranks the shows that booked them. standing="established" ranks only credentialed or recognized experts — "the shows that book established finance experts" is field plus top_podcasts plus standing. A broad code covers everything below it.

Take codes from particle_expertise_resolve, which turns free text ("doctors", "finance") into them; on unresolved_reference or not_found, resolve and retry. Guest slugs work with particle_person_get (include: ["expertise"]) and particle_podcast_get_guest; podcast slugs with every podcast tool.

For a multi-criteria guest search (practicing, seniority, capacity, employer, all-time) use particle_podcast_list_guests; for one show's guest mix use particle_podcast_resolve with include: ["guest_expertise"].

Input Schema

NameRequiredDescriptionDefault
fieldNoA field of research's ANZSRC 2020 code, slug or title from particle_expertise_resolve (e.g. 'banking-finance-and-investment'). Pass exactly one of occupation or field.
limitNoRows per top_ section (1-25, default 10).
sinceNoWindow start for the top_ sections (YYYY-MM-DD). Defaults to 30 days before until; the window may span at most 90 days.
untilNoWindow end for the top_ sections (YYYY-MM-DD, inclusive). Defaults to now.
includeNoOptional sections: 'top_guests' (the guests in the code most booked in the window, with appearances, shows and last appearance) and 'top_podcasts' (the shows that booked the code's guests most). Default is the code with its parent, children and crosswalk.
standingNoRank only guests whose standing is recognized this way (e.g. 'established' for credentialed experts). Applies to the top_ sections.
min_guestsNotop_podcasts keeps shows that booked at least this many distinct guests in the code in the window (default 1).
occupationNoAn occupation's SOC 2018 or ISCO-08 code, slug or title from particle_expertise_resolve (e.g. 'cardiologists'). Pass exactly one of occupation or field.
min_podcastsNotop_guests keeps guests seen on at least this many distinct shows in the window (default 1); raise it to favor guests booked across the medium over a show's regulars.
output_formatNoOutput serialization. 'markdown' (default) returns the LLM-facing rendering. 'json' returns the structured payload as JSON text — use only for programmatic chaining where exact field extraction matters; the JSON shape is larger and noisier for an LLM to read.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only cover the read-only/open-world safety profile, and the description adds substantial operational context: the default vs. opt-in section behavior, the 30-day default window capped at 90 days, how standing filters ranking, and the markdown-vs-json tradeoff ('JSON shape is larger and noisier for an LLM to read').

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?

Front-loaded with the core purpose, then routing, then alternatives — each paragraph carries distinct information and nothing is filler. It is dense with parentheticals and slightly long, but every clause earns its place.

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?

For a 10-param, no-output-schema tool, the description covers defaults, constraints, opt-in return contents ('with appearances, shows and last appearance'), and disambiguation from four sibling tools. An agent has everything needed to call it correctly without opening the schema.

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 still adds real meaning on top of the schema, e.g. min_podcasts 'raise it to favor guests booked across the medium over a show's regulars' and the window cap semantics. It stops short of exhaustively explaining every param, but the value-add is genuine.

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?

States a specific verb and resource ('Describe one occupation or field of research') plus what the default response contains (parent, children, crosswalk) and what opt-in sections add. It is immediately distinguishable from siblings like particle_expertise_resolve (which produces codes) and particle_person_get (which consumes slugs).

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 routes the agent: take codes from particle_expertise_resolve, retry on unresolved_reference/not_found, use particle_podcast_list_guests for multi-criteria guest search, and particle_podcast_resolve with include:['guest_expertise'] for one show's guest mix. It even names the exact invocation pattern for a common query ('the shows that book established finance experts').

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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