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TsvetanG2

cognigy-ai-mcp-management-server

run_playbook

Tests a Cognigy.AI flow by running a playbook scenario with assertions. Supports dry run validation before executing, returns pass/fail results.

Instructions

Runs a Cognigy.AI playbook test scenario against a flow. MUTATING: This executes test assertions. Use dryRun=true (default) to validate first. Returns pass/fail results with assertion details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dryRunNoIf true (default), validates without running. Set to false to actually run the playbook.
flowIdYesThe reference ID of the flow to test
localeIdYesThe reference ID of the locale
timeoutMsNoMaximum time to wait for playbook completion (5-300 seconds, default 120)
entrypointYesThe snapshot or project ID to run against
playbookIdYesThe playbook ID to run
pollIntervalMsNoHow often to check task status (1-10 seconds, default 3)
Behavior4/5

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

Annotations already indicate readOnlyHint=false, so the description reinforces that it is mutating. It adds behavioral context by explaining the dryRun parameter and that it executes assertions, which goes 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?

The description is concise with three sentences, front-loading the action and key behavior. Every sentence adds value without redundancy.

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 tool with 7 parameters, 4 required, and no output schema, the description covers the primary behavior and safety advice but lacks details on return structure, error handling, or prerequisites, leaving some gaps for an agent.

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%, so the baseline is 3. The description does not add significant meaning beyond what the schema already provides for each parameter, though it mentions dryRun and return values briefly.

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?

The description clearly states the tool runs a Cognigy.AI playbook test scenario against a flow, using a specific verb and resource. It distinguishes from sibling tools like list_playbooks or get_playbook by emphasizing execution.

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 suggests using dryRun=true to validate first, which is helpful for safe usage, but does not explicitly state when to use this tool versus alternatives (e.g., listing playbook runs). The guidance is implied rather than explicit.

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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