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workflow_delete_node

Destructive

Delete a node from a workflow. This will also remove any edges connected to the node. Use when editing a draft. Do not use to activate or run a candidate. Next step: validate, guard, save with workflow_save_version, then call workflow_test_run for a first version. Edits update one open draft; PII can be a warning, but new PII in agent or MCP saves blocks.

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": {
      "workflowId": "<workflowId>",
      "nodeId": "<nodeId>"
    },
    "name": "workflow_delete_node"
  }
}

Input Schema

NameRequiredDescriptionDefault
nodeIdYesThe ID of the node to delete
workflowIdYesThe workflow ID
versionLabelNoOptional label for this version (e.g. "Removed duplicate node"). If omitted, defaults to "Version N".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false, but the description adds real context beyond them: the cascading edge removal, the constraint that edits apply to a single open draft, and the PII gating on save. The PII sentence is terse and jargon-heavy ("agent or MCP saves blocks"), so the disclosure is valuable but not fully clear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose and the cascade caveat, then usage, then next steps — a sensible order with no filler sentences. The closing PII clause is compressed to the point of ambiguity and reads as an aside to this specific operation.

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?

For a destructive edit tool with a fully documented 3-parameter schema and annotations covering the safety profile, this fills the remaining gaps: cascade side effects, draft-only scope, and required follow-up calls. No output schema exists, and none is needed here.

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% and both required parameters plus versionLabel are documented in-schema, including the versionLabel default. The description adds no parameter-level meaning beyond that, 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 ("Delete a node from a workflow") and immediately scopes the behavior with "This will also remove any edges connected to the node," which separates it from workflow_delete (whole workflow) and workflow_delete_edge (edge only). An agent can tell it apart from siblings without opening any schema.

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?

Explicit when-to-use ("Use when editing a draft"), explicit when-not ("Do not use to activate or run a candidate"), and a named next-step chain: validate, guard, workflow_save_version, then workflow_test_run. Alternatives and sequencing are spelled out rather than inferred.

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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