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cloudflare-mcp

by pocc

get_ai_search_job

Retrieve details of a Cloudflare AI Search job by supplying the account, instance, and job IDs to check status and configuration.

Instructions

Get details of an AI Search job

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesThe job ID
account_idYesThe account ID
instance_idYesThe instance ID

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations present, the description carries the burden of behavioral disclosure. 'Get details' clearly indicates a read-only retrieval operation with no mutation or side effects. However, it does not mention authentication requirements, possible error conditions, or whether the job must be in a particular state, leaving some behavioral context unstated.

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 a single focused sentence with no filler or redundant phrasing. It front-loads the core operation and is appropriately minimal for a simple getter tool.

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 simple get-by-ID tool, this is minimally sufficient: the name, verb, and schema let an agent invoke it. However, there is no output schema and the description does not explain what 'details' are returned or how job_id relates to account_id and instance_id, so the agent has limited context about the expected response.

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?

All three parameters are documented in the schema, giving 100% schema coverage, so the baseline is 3. The description adds no parameter-level meaning beyond 'AI Search job', and the schema descriptions themselves are largely tautological ('The job ID', 'The account ID', 'The instance ID').

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 states a clear verb ('Get') and a specific resource ('an AI Search job'), so an agent can distinguish it from nearby sibling tools like list_ai_search_jobs and get_ai_search_instance. However, it does not explicitly describe that it retrieves a single job by ID, which keeps it from being a perfect 5.

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

Usage Guidelines2/5

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

The description gives no explicit guidance about when to use this tool instead of list_ai_search_jobs or get_ai_search_instance. It does not mention prerequisites such as having the account_id, instance_id, and job_id, nor does it say to use list_ai_search_jobs first to obtain a job ID. Usage is only implied by the name and verb.

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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