ollama_chat
Chat with local AI models using conversation messages, supporting system prompts, multi-turn dialogues, and tool calling for interactive AI applications.
Instructions
Chat with a model using conversation messages. Supports system messages, multi-turn conversations, tool calling, and generation options.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Name of the model to use | |
| messages | Yes | Array of chat messages | |
| tools | No | Tools that the model can call (optional). Provide as JSON array of tool objects. | |
| options | No | Generation options (optional). Provide as JSON object with settings like temperature, top_p, etc. | |
| format | No | json |
Implementation Reference
- src/tools/chat.ts:11-59 (handler)Core implementation of the chat functionality that calls the Ollama chat API, processes responses, handles tool calls, and formats output.export async function chatWithModel( ollama: Ollama, model: string, messages: ChatMessage[], options: GenerationOptions, format: ResponseFormat, tools?: Tool[] ): Promise<string> { // Determine format parameter for Ollama API let ollamaFormat: 'json' | undefined = undefined; if (format === ResponseFormat.JSON) { ollamaFormat = 'json'; } const response = await ollama.chat({ model, messages, tools, options, format: ollamaFormat, stream: false, }); // Extract content with fallback let content = response.message.content; if (!content) { content = ''; } const tool_calls = response.message.tool_calls; // If the response includes tool calls, include them in the output let hasToolCalls = false; if (tool_calls) { if (tool_calls.length > 0) { hasToolCalls = true; } } if (hasToolCalls) { const fullResponse = { content, tool_calls, }; return formatResponse(JSON.stringify(fullResponse), format); } return formatResponse(content, format); }
- src/tools/chat.ts:61-120 (registration)Tool definition export for 'ollama_chat' including name, description, input schema for MCP, and handler function. Automatically discovered and registered by the autoloader.export const toolDefinition: ToolDefinition = { name: 'ollama_chat', description: 'Chat with a model using conversation messages. Supports system messages, multi-turn conversations, tool calling, and generation options.', inputSchema: { type: 'object', properties: { model: { type: 'string', description: 'Name of the model to use', }, messages: { type: 'array', description: 'Array of chat messages', items: { type: 'object', properties: { role: { type: 'string', enum: ['system', 'user', 'assistant'], }, content: { type: 'string', }, images: { type: 'array', items: { type: 'string' }, }, }, required: ['role', 'content'], }, }, tools: { type: 'string', description: 'Tools that the model can call (optional). Provide as JSON array of tool objects.', }, options: { type: 'string', description: 'Generation options (optional). Provide as JSON object with settings like temperature, top_p, etc.', }, format: { type: 'string', enum: ['json', 'markdown'], default: 'json', }, }, required: ['model', 'messages'], }, handler: async (ollama: Ollama, args: Record<string, unknown>, format: ResponseFormat) => { const validated = ChatInputSchema.parse(args); return chatWithModel( ollama, validated.model, validated.messages, validated.options || {}, format, validated.tools.length > 0 ? validated.tools : undefined ); }, };
- src/schemas.ts:88-98 (schema)Zod input validation schema (ChatInputSchema) for the ollama_chat tool, used in the handler for parsing and validating arguments./** * Schema for ollama_chat tool */ export const ChatInputSchema = z.object({ model: z.string().min(1), messages: z.array(ChatMessageSchema).min(1), tools: parseJsonOrDefault([]).pipe(z.array(ToolSchema)), options: parseJsonOrDefault({}).pipe(GenerationOptionsSchema), format: ResponseFormatSchema.default('json'), stream: z.boolean().default(false), });