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Send Message

neuron_send_message

Send a message to an existing conversation. Supports text, image, and document message types with optional media attachments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesUnique identifier (UUID) of the conversation
contentYesMessage content to send
mediaUrlNoURL of media to attach (required for image/document message types)
messageTypeNoType of message: 'text' (default), 'image', or 'document'

TDQS

B3.1/5.0
Behavior2/5

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

Annotations declare readOnlyHint=false and destructiveHint=false, but the description adds little behavioral context. It does not disclose how the tool handles missing required fields (e.g., mediaUrl for image/document types), what the response contains, or any side effects. With no idempotentHint or openWorldHint, the description should compensate but does not.

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, concise and front-loaded. No redundant information. Every word adds value.

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

Completeness3/5

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

For a 4-parameter send tool with no output schema, the description is brief. It covers the core action and supported types but omits common expectations like return format (message ID, success status), error conditions (e.g., invalid conversation ID), and how this tool relates to other messaging siblings. Given the complexity, it is adequate but not fully complete.

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?

The schema covers all 4 parameters with descriptions (100% coverage). The description adds only that message types include text, image, document, and media attachments are optional. This aligns with schema but does not clarify defaults (e.g., messageType defaults to 'text') or provide further syntax guidance. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Send'), the resource ('message to an existing conversation'), and supported types (text, image, document with optional attachments). It is specific enough to differentiate from siblings like neuron_compose_message or neuron_send_broadcast, though it does not explicitly distinguish them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides minimal guidance: it says to use for existing conversations but does not explain when to prefer this over alternatives (e.g., neuron_compose_message for new conversations, neuron_send_broadcast for broadcasts). No prerequisites or exclusions are mentioned.

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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TDQS

B3.4/5.0
Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness4/5

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.

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