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Clear Chats

neuron_clear_chat
DestructiveIdempotent

Delete messages (and their media) from chats; the conversations stay, empty. Chats a human is currently handling, or that a flow is waiting on, are skipped. Only Neuron's copy — nothing is deleted from WhatsApp. DRY RUN by default. To clear without picking conversations, pass execute: true AND confirmChats equal to the number of chats from the preview. Irreversible.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
executeNoActually clear. Default false = preview only.
chatKindNoDMs, groups, or both (default both).
mediaTypesNoOnly these media types (media clears only).
confirmChatsNoRequired for workspace-wide clears: the 'conversations' number from the preview.
alsoBotMemoryNoAlso clear what the bot learned from these chats (insights, conversation summary).
olderThanDaysNoOnly messages older than this many days. Omit/null for all ages.
conversationIdsNoLimit to these conversations. Omit for the whole workspace.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare destructive=true, but the description adds the traits that matter most here: DRY RUN by default, irreversibility, the skip conditions, and the scope limit ('Only Neuron's copy — nothing is deleted from WhatsApp'). This is exactly the extra context annotations cannot carry.

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?

Five short sentences, front-loaded with the core action and the highest-risk facts (dry run, irreversible). Every sentence earns its place, though the execute/confirmChats sentence is dense and slightly mid-stream.

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?

No output schema exists, and the description references a 'preview' and a 'conversations' number that the agent will need to interpret. It is nearly complete for this destructive, high-stakes tool, though a word on the preview response shape would close the loop.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds the non-obvious coupling between execute and confirmChats (confirmChats must equal the preview's chat count) and the preview-before-execute workflow. That meaning goes beyond the field-level docs.

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 (delete) and resource (messages and their media) plus the crucial nuance that conversations remain empty, which distinguishes it from neuron_delete_message and neuron_clear_media. An agent can tell what this tool does without opening the 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?

Explicitly covers the dry-run default, which chats are skipped (human-handled or flow-blocked), the alternative path (conversationIds vs workspace-wide), and the exact condition for a workspace-wide execute (execute: true plus confirmChats matching the preview count). When-to-use and prerequisites are fully specified.

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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