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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "flowId": "<flowId>",
      "nodeId": "<nodeId>"
    },
    "name": "update_node"
  }
}

Input Schema

Name	Required	Description
`label`	No	New display label for the node
`config`	No	Node-specific configuration to update. Structure depends on node type. Only provided fields are updated.
`dryRun`	No	If true (default), validates the operation without updating. Set to false to actually update.
`flowId`	Yes	The flow ID containing the node
`nodeId`	Yes	The ID of the node to update
`comment`	No	New developer comment/note
`localeId`	No	Locale ID if updating locale-specific content
`isDisabled`	No	Whether the node is disabled (skipped during execution)
`analyticsLabel`	No	Label used in analytics reporting

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.4Jul 27, 2026

TDQS

A4.2/5.0

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.

Deploy Server