mcp__gemini__ai_chat
Engage in AI-driven conversations with customizable model selection to enhance communication and context understanding. Ideal for integrating intelligent dialogue into workflows.
Instructions
AI conversation with model selection
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Additional context | |
| message | Yes | Message for AI | |
| model | No | Model type | main |
Implementation Reference
- src/tools/registry.js:59-74 (registration)Registration of the 'mcp__gemini__ai_chat' tool including schema and inline handler function
this.registerTool( 'mcp__gemini__ai_chat', 'AI conversation with model selection', { message: { type: 'string', description: 'Message for AI', required: true }, model: { type: 'string', description: 'Model type', default: 'main' }, context: { type: 'string', description: 'Additional context' } }, async (args) => { const { message, model = 'main', context = '' } = args; validateString(message, 'message'); const response = await aiClient.call(message, model, { context }); return `🤖 **AI Response** (${model})\\n\\n${response}`; } ); - src/tools/registry.js:67-73 (handler)The handler function that destructures args, validates message, calls aiClient.call with message, model, and context, and returns a formatted AI response
async (args) => { const { message, model = 'main', context = '' } = args; validateString(message, 'message'); const response = await aiClient.call(message, model, { context }); return `🤖 **AI Response** (${model})\\n\\n${response}`; } - src/tools/registry.js:62-66 (schema)Input parameters schema defining message (required string), model (string with default 'main'), and context (string) for the tool.
{ message: { type: 'string', description: 'Message for AI', required: true }, model: { type: 'string', description: 'Model type', default: 'main' }, context: { type: 'string', description: 'Additional context' } },