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Run AI employee

run_employee
Destructive

Start an AI employee's 'Run now', the same as the Run button in Zenning. It runs in the background and is billed to your organisation's credits. Needs owner or editor access. Returns the runId when it is available; otherwise call get_employee_run with the employeeId to see the latest run.

Input Schema

NameRequiredDescriptionDefault
inputNoOptional sample input for this run, as a JSON object. The employee receives it as its test payload.
employeeIdYesThe AI employee's id.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, destructiveHint=true and openWorldHint=true; the description goes further by disclosing asynchronous background execution, credit billing to the organization, and the owner/editor auth requirement. The one gap is that it never explains why the operation is flagged destructive (what gets consumed or changed), but the billing and permission disclosures are meaningful value beyond 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?

Three tight sentences, front-loaded with the action and its UI analogue, then side effects (billing/access), then return behavior. No filler and each sentence carries distinct information.

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?

Although there is no output schema, the description covers return semantics (runId may not be available immediately; poll get_employee_run as fallback), the async nature, cost, and permission requirements. Nothing an agent needs to invoke and follow up correctly is missing.

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 description coverage is 100%, so both parameters are already documented in the schema; the description adds only that employeeId can be reused with get_employee_run. It says nothing about the optional 'input' test payload beyond what the schema states, so the baseline 3 applies.

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 ('Start an AI employee's Run now') and anchors it to a concrete UI equivalent (the Run button in Zenning), so the agent immediately knows this is a trigger/execution tool rather than a read or config tool. It is also clearly distinguished from the nearby sibling get_employee_run, which is named as the status-check counterpart.

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 names the alternative and the condition that selects it: call this to kick off a run, 'otherwise call get_employee_run with the employeeId to see the latest run.' Preconditions (owner or editor access) are stated up front, so an agent can judge eligibility before invoking.

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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