Delete Flow
neuron_delete_flowSoft-delete a flow. It stops running but can be restored. Running executions are not cancelled.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | UUID of the flow to delete. |
neuron_delete_flowSoft-delete a flow. It stops running but can be restored. Running executions are not cancelled.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | UUID of the flow to delete. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal destructiveHint=true, but the description adds crucial nuance: the delete is soft, the flow can be restored, and running executions are deliberately not cancelled. This prevents an agent from assuming a hard delete or that all execution stops, which is significant behavioral context beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, with the primary action front-loaded and no filler. Every phrase adds meaningful behavioral information: soft-delete, restorable, stops running, executions not cancelled.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter, low-complexity delete operation, the description fully covers the behavioral consequences that matter for invoking it correctly. The annotations handle the destructive profile, and the description covers the soft-delete semantics and execution behavior, so nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes the only parameter id as 'UUID of the flow to delete' with 100% coverage. The description adds no additional parameter-level meaning, so the baseline of 3 for high schema coverage is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with the specific action and resource, 'Soft-delete a flow', and immediately clarifies the semantics with 'It stops running but can be restored' and 'Running executions are not cancelled.' This distinguishes it from related tools like neuron_cancel_flow_run or neuron_toggle_flow without needing to inspect schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The use case is clearly implied: use this when you want to stop a flow but keep it restorable. However, the description does not explicitly name alternatives or state when not to use this tool, leaving the agent to infer routing from the behavior alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly separated by resource type, but there is meaningful overlap in messaging entry points (send_message, send_whatsapp, compose_message, bot_api_send) and contact ingestion/sync tools (import_contacts, populate_contacts, sync_whatsapp_contacts). The descriptions help disambiguate, but with 309 tools an agent will frequently need to read closely to pick the right one.
The overwhelming majority of tools follow a consistent verb_noun snake_case pattern: create_*, get_*, list_*, update_*, delete_*. Minor deviations like sales_stats, lead_stats, wallet_balance, and whoami break the pattern slightly, but overall naming is highly predictable.
309 tools is an extreme count for any MCP server, even a broad platform. This creates significant cognitive load and navigation overhead for agents, and far exceeds the well-scoped 3-15 tool range where coherence is strongest.
The tool surface is remarkably comprehensive across bots, contacts, campaigns, flows, knowledge bases, personas, marketplace, wallet, and products. Minor gaps exist — lead sources lack update/delete tools, and there is no single get_task or get_webhook alongside their list/update/delete counterparts — but these are workable gaps rather than dead ends.