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run_keyword_research

Avvia una ricerca keyword e ritorna un job_id: richiama lo stesso tool passando job_id per avere i risultati (volume, difficulty, CPC), di solito pronti entro un paio di minuti. mode: related (da una seed), list (volumi di una lista), domain (keyword di un dominio), gap (keyword del competitor non coperte dal dominio). Con project_id marca le keyword già tracciate. Il ritiro con job_id non consuma quota. AZIONE A PAGAMENTO: consuma 1 unità della quota mensile del piano e 1 azione MCP (tetto dedicato). Usa get_usage per i crediti residui.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo
seedNokeyword di partenza (mode=related)
domainNodominio (mode=domain/gap)
job_idNoritira una ricerca già avviata
keywordsNolista keyword (mode=list)
competitorNodominio competitor (mode=gap)
project_idNo
language_codeNodefault 'it'
location_codeNodefault 2380 (Italia)
organization_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior5/5

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

The description discloses the paid nature of the action (1 quota unit and 1 MCP action), the asynchronous job flow, the side effect of marking keywords as tracked when project_id is provided, and that retrieval with job_id does not consume quota. Since no annotations are present, this carries the full burden and does so thoroughly.

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 dense but well organized: it front-loads the core flow and job_id retrieval, then enumerates modes and the quota warning. It could be slightly more structured with bullets, but every sentence contributes useful information.

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?

For a 10-parameter tool with no output schema and no annotations, the description covers the main usage, costs, and modes, but it omits the exact response structure, error conditions, and whether mode is required for a new search (since required is empty). This leaves agents to guess some invocation details.

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 description adds meaningful semantics for mode (related/list/domain/gap), job_id retrieval, and project_id tracking. However, it does not clarify the purpose of organization_id or the default behavior when no mode is provided, which are not covered in the schema either.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool launches a keyword research job and returns a job_id, listing four distinct modes. It does not explicitly differentiate it from siblings like get_keyword_gap or get_keywords, but the asynchronous job-based behavior is evident from the text.

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

Usage Guidelines3/5

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

The description tells the agent to call the same tool with job_id to retrieve results and to use get_usage for remaining credits. It does not provide explicit guidance on when to choose this tool over get_keywords or get_keyword_gap for direct data access, leaving the selection partially 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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