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TsvetanG2

cognigy-ai-mcp-management-server

update_node

Idempotent

Partially update a Cognigy.AI flow node by modifying only specified fields. Dry run validates changes before applying.

Instructions

Updates an existing node in a Cognigy.AI flow. MUTATING: This modifies the node. Use dryRun=true (default) to validate first. Only provided fields are updated; others remain unchanged.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelNoNew display label for the node
configNoNode-specific configuration to update. Structure depends on node type. Only provided fields are updated.
dryRunNoIf true (default), validates the operation without updating. Set to false to actually update.
flowIdYesThe flow ID containing the node
nodeIdYesThe ID of the node to update
commentNoNew developer comment/note
localeIdNoLocale ID if updating locale-specific content
isDisabledNoWhether the node is disabled (skipped during execution)
analyticsLabelNoLabel used in analytics reporting
Behavior4/5

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

Explicitly states 'MUTATING: This modifies the node' and recommends dryRun. Adds context about partial updates and config structure. Annotations already indicate idempotent and non-destructive, no contradiction.

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?

Two sentences, highly efficient. First sentence states purpose, second adds validation guidance and update semantics. No unnecessary words.

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

Completeness4/5

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

Covers validation, partial update, and config dependency. No output schema needed. Lacks error conditions or return value details, but sufficient for a mutation tool with annotations.

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 parameters are well-documented. Description adds minimal extra meaning beyond the schema, mainly reinforcing partial update behavior. Baseline 3 is appropriate.

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?

Clear verb and resource: 'Updates an existing node in a Cognigy.AI flow.' Distinguishes from siblings like create_node and delete_node.

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?

Provides explicit instruction: 'Use dryRun=true (default) to validate first.' Also explains partial update behavior. Does not explicitly list alternatives but context is clear.

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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