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datasets_journalists_facets

Get distribution counts of journalists by outlet, vertical, topic, or contact type, using optional filters like query, outlet, vertical, topic, and contact availability.

Instructions

Facet the journalists dataset. Returns distribution counts over the journalists index (dataset id enum value journalists), honoring the same filters as search. Facet enum: outlet, vertical, topic, contact_type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text match on the journalist's name, title, and bio, max 256 characters
facetYesFacet enum: outlet, vertical, topic, contact_type
topicNoExact topic filter
outletNoExact outlet id filter
verticalNoExact beat-vertical filter. Enum: tech, crypto, marketing, consumer_tech, consumer_policy, cybersecurity, health, gaming, climate, business, entertainment, sports, legal, science, politics, real_estate, automotive, travel, food, education, design, film_tv, fashion, music, personal_finance, tech_independent, culture_independent, local_news, construction, banking, retail, aerospace_defense, energy, agriculture, local_business
contact_typeNoContact-availability filter. Enum: email, social, none
Behavior2/5

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

No annotations exist, so description must carry behavioral disclosure. Only states it returns distribution counts and honors search filters. Lacks details on behavior like read-only nature, limits, response structure, or multiple facet support.

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?

Three concise sentences, front-loaded with key information, no fluff. Efficient structure.

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

Completeness3/5

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

Adequate for a simple facet tool, but lacks output schema details and does not specify if multiple facets can be requested. For an agent, additional context on response format would be helpful.

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 coverage is 100%, baseline 3. Description adds that filters work like search tool and lists facet enums again, but no additional syntax or format details beyond schema.

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?

Clearly states verb (facet), resource (journalists dataset), and output (distribution counts). Distinguishes from search and item tools by specifying it returns aggregated counts honoring search filters. Lists valid facet enums.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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

Implies usage for aggregate counts rather than full search results, and mentions it honors same filters as search. However, does not explicitly state when not to use or compare to sibling facet tools for other datasets.

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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