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Generate Content in Voice

neuron_generate_from_persona

Generate content written in the persona's voice for a topic/brief. Returns N variations. Stateless — nothing is persisted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesPersona UUID
countNoNumber of variations (1-10, default 1).
topicNoWhat to write about.
formatNoDesired format (post, reply, caption, thread, DM...).
platformNoTarget platform (instagram/x/tiktok/linkedin/facebook/whatsapp...).
extraInstructionsNo

TDQS

B3.4/5.0
Behavior1/5

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

The description claims 'Stateless — nothing is persisted,' which directly contradicts the annotation readOnlyHint=false (indicating the tool is not read-only). Per the rule, a description contradicting annotations earns a score of 1. This is a clear annotation 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?

The description is two sentences with no fluff. It front-loads the primary action, includes the key output behavior, and the stateless note. Every word contributes to understanding the tool.

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?

Given the moderate complexity (6 params, no output schema) and weak annotations, the description covers the main purpose, return behavior, and side-effect profile (stateless). It omits details like the required 'id' parameter usage or response structure, but these are partially covered by schema. The annotation contradiction slightly reduces completeness, but the description itself is fairly 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?

Schema description coverage is 83% (>80%), so baseline is 3. The description adds little beyond the schema: it uses 'topic/brief' echoing the 'topic' parameter and 'N variations' paraphrasing the 'count' parameter description. No significant new parameter meaning is provided.

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?

The description clearly states a specific action ('Generate content written in the persona's voice') and the output ('Returns N variations'), distinguishing it from siblings like neuron_compose_message or neuron_rewrite_text by its focus on persona-specific generation.

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

Usage Guidelines3/5

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

The description provides context ('for a topic/brief' and 'Stateless') but does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or recommended use cases. Usage is implied rather than explicitly guided.

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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