Skip to main content
Glama

neuron

by io.github.conquext
OverviewDiscussionsAdmin

Create Bot

neuron_create_bot

Create a new AI bot with specified name, model, and behavior instructions. Returns the created bot object.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesDisplay name for the bot
llmModelNoOpenRouter model ID (e.g., 'deepseek/deepseek-v4.1-flash' (default), 'google/gemini-2.5-flash')
metadataNoArbitrary metadata object stored with the bot. Known keys: 'communityExitFollowUp' (auto-DM users who leave watched groups — { enabled: boolean, groupJids: string[], message: string with {name} placeholder }), 'learningConfig' (self-learning from conversations — { enabled: boolean, folder: string, groupChats: string[], dmPhones: string[], useAdminPhones: boolean }), 'loadBalancing' (multi-channel round-robin — { enabled: boolean, strategy: string }), 'welcomeNewMembers' (fine-tune welcome behavior — { batchWindowSeconds: number, groupJids: string[] }). Additional custom keys are preserved as-is.
maxTokensNoMaximum number of tokens the bot can generate per response
assignmentNoOne-sentence role definition for the bot (max 2000 chars)
systemPromptYesSystem prompt that defines the bot's behavior, personality, and response guidelines
llmTemperatureNoTemperature parameter controlling response randomness (0 = deterministic, 2 = creative)
welcomeMessageNoWelcome message sent to new members joining groups the bot manages. Setting this field activates the batched welcome feature — new members are accumulated over a window (default 60s) and welcomed in a single message with @mentions. Set to null to disable.
fallbackMessageNoMessage sent when the bot cannot understand user input (max 2000 chars)
greetingMessageNoAutomatic greeting sent when a new conversation starts (max 2000 chars)
escalationPromptNoPrompt template used when escalating to a human agent (max 5000 chars)
responsibilitiesNoArray of responsibility descriptions defining what the bot handles
whatsappChannelIdNoUnique identifier (UUID) of the WhatsApp channel to associate with the bot

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / llmModel / description
      Previous value: -"OpenRouter model ID (e.g., 'google/gemini-2.5-flash-lite', 'openrouter/auto')"New value: +"OpenRouter model ID (e.g., 'deepseek/deepseek-v4.1-flash' (default), 'google/gemini-2.5-flash')"
  2. Changed2 schema fields changed
    • changedInput schema / properties / metadata / description
      Previous value: -"Arbitrary metadata object stored with the bot. Known keys: 'communityExitFollowUp' (auto-DM users who leave watched groups — { enabled: boolean, groupJids: string[], message: string with {name} placeholder }), 'learningConfig' (self-learning from conversations — { enabled: boolean, folder: string, groupChats: string[], dmPhones: string[], useAdminPhones: boolean }), 'loadBalancing' (multi-channel round-robin — { enabled: boolean, strategy: string }). Additional custom keys are preserved as-is."New value: +"Arbitrary metadata object stored with the bot. Known keys: 'communityExitFollowUp' (auto-DM users who leave watched groups — { enabled: boolean, groupJids: string[], message: string with {name} placeholder }), 'learningConfig' (self-learning from conversations — { enabled: boolean, folder: string, groupChats: string[], dmPhones: string[], useAdminPhones: boolean }), 'loadBalancing' (multi-channel round-robin — { enabled: boolean, strategy: string }), 'welcomeNewMembers' (fine-tune welcome behavior — { batchWindowSeconds: number, groupJids: string[] }). Additional custom keys are preserved as-is."
    • addedInput schema / properties / welcomeMessage
      Added value: +{
+  "description": "Welcome message sent to new members joining groups the bot manages. Setting this field activates the batched welcome feature — new members are accumulated over a window (default 60s) and welcomed in a single message with @mentions. Set to null to disable.",
+  "type": "string"
+}
  3. Changed1 schema field changed
    • addedInput schema / properties / metadata
      Added value: +{
+  "additionalProperties": {},
+  "description": "Arbitrary metadata object stored with the bot. Known keys: 'communityExitFollowUp' (auto-DM users who leave watched groups — { enabled: boolean, groupJids: string[], message: string with {name} placeholder }), 'learningConfig' (self-learning from conversations — { enabled: boolean, folder: string, groupChats: string[], dmPhones: string[], useAdminPhones: boolean }), 'loadBalancing' (multi-channel round-robin — { enabled: boolean, strategy: string }). Additional custom keys are preserved as-is.",
+  "type": "object"
+}
  4. Changed1 schema field changed
    • changedInput schema / properties / llmModel / description
      Previous value: -"OpenRouter model ID (e.g., 'google/gemini-2.5-flash-lite-preview-09-2025', 'openrouter/auto')"New value: +"OpenRouter model ID (e.g., 'google/gemini-2.5-flash-lite', 'openrouter/auto')"
  5. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare this as a non-read-only, non-destructive, non-idempotent, closed-world write, so the safety profile is covered. The description adds one useful behavioral fact not in the annotations: 'Returns the created bot object,' which matters because there is no output schema. It does not disclose defaults (e.g., llmModel fallback) or any side effects of activating welcome/learning features, so it stays at a baseline 3.

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?

Two short sentences, zero padding, with the operation front-loaded and the return value second. It is efficient, though the extreme brevity means it earns little beyond the schema.

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 13-parameter tool with a nested metadata object and no output schema, the description is thin: it confirms the return value (helpful) but says nothing about the metadata feature flags, defaults, or follow-up wiring needed to make the bot functional. The rich schema prevents this from being inadequate, but more context would clearly help.

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%, so the schema already documents all 13 parameters, including the rich nested 'metadata' object. The description only echoes three of them (name, model, behavior instructions) without adding syntax, defaults, or validation semantics. Baseline 3 is appropriate when the schema does the heavy lifting.

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 states a specific verb+resource ('Create a new AI bot') and names the key configurable inputs (name, model, behavior instructions), so the agent knows exactly what operation this performs. It does not distinguish itself from adjacent bot-management siblings such as neuron_update_bot, neuron_delete_bot, or neuron_build_persona, which is the only thing keeping it from a 5.

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?

There is no when-to-use guidance, no prerequisites (e.g., that a bot typically needs a channel assignment afterward via neuron_add_bot_channel), and no mention of alternatives for related workflows. The agent must infer usage entirely from the name and schema.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All neuron toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources