watson_get_model

Instructions

Implementation Reference

• src/tools/watson/index.ts:17-19 (handler)

  The tool handler for 'watson_get_model' — makes a GET request to the Watson ML API endpoint for foundation model specs, filtering by model_id. Uses safeTool for error handling and client.get to perform the authenticated API call.

  server.tool("watson_get_model", "Get details of a specific foundation model", {
    model_id: z.string().describe("Model ID (e.g. ibm/granite-13b-instruct-v2)"), region: z.string().optional(),
  }, async (p) => safeTool(() => client.get(`${ml(p.region||r)}/ml/v1/foundation_model_specs`, {version:ver,filters:`modelid_${p.model_id}`})));

• src/tools/watson/index.ts:17-19 (schema)

  The input schema for 'watson_get_model' defines two parameters: 'model_id' (required string, describes the model ID like ibm/granite-13b-instruct-v2) and 'region' (optional string).

  server.tool("watson_get_model", "Get details of a specific foundation model", {
    model_id: z.string().describe("Model ID (e.g. ibm/granite-13b-instruct-v2)"), region: z.string().optional(),
  }, async (p) => safeTool(() => client.get(`${ml(p.region||r)}/ml/v1/foundation_model_specs`, {version:ver,filters:`modelid_${p.model_id}`})));

• src/tools/watson/index.ts:7-68 (registration)

  Registration function 'registerWatsonTools' which registers all watson* tools including 'watson_get_model' on the MCP server via server.tool(). Called from src/server.ts line 68.

  export function registerWatsonTools(server: McpServer, client: IBMCloudAPIClient, config: ServerConfig) {
    const ml = (r: string) => IBM_ENDPOINTS.WATSON_ML(r);
    const w = () => assertWriteAllowed(config.allowWrite);
    const r = config.region;
    const ver = "2024-05-01";
  
    server.tool("watson_list_models", "List available foundation models on watsonx.ai", {
      region: z.string().optional(),
    }, async (p) => safeTool(() => client.get(`${ml(p.region||r)}/ml/v1/foundation_model_specs`, {version:ver,limit:200})));
  
    server.tool("watson_get_model", "Get details of a specific foundation model", {
      model_id: z.string().describe("Model ID (e.g. ibm/granite-13b-instruct-v2)"), region: z.string().optional(),
    }, async (p) => safeTool(() => client.get(`${ml(p.region||r)}/ml/v1/foundation_model_specs`, {version:ver,filters:`modelid_${p.model_id}`})));
  
    server.tool("watson_generate_text", "Generate text using a watsonx.ai foundation model", {
      model_id: z.string().describe("Model ID"), input: z.string().describe("Input prompt"),
      project_id: z.string().describe("watsonx project ID"),
      max_new_tokens: z.number().optional().describe("Max tokens to generate (default 200)"),
      temperature: z.number().optional().describe("Temperature 0-2 (default 0.7)"),
      top_p: z.number().optional(), top_k: z.number().optional(),
      region: z.string().optional(),
    }, async (p) => safeTool(async () => { w();
      return client.post(`${ml(p.region||r)}/ml/v1/text/generation`, {
        model_id:p.model_id, input:p.input, project_id:p.project_id,
        parameters:{max_new_tokens:p.max_new_tokens||200, temperature:p.temperature||0.7, top_p:p.top_p, top_k:p.top_k},
      }, {version:ver});
    }));
  
    server.tool("watson_list_deployments", "List model deployments in a space", {
      space_id: z.string().describe("Deployment space ID"), region: z.string().optional(),
    }, async (p) => safeTool(() => client.get(`${ml(p.region||r)}/ml/v4/deployments`, {version:ver,space_id:p.space_id})));
  
    server.tool("watson_deploy_model", "Deploy a model to a space", {
      name: z.string(), space_id: z.string(), asset_id: z.string().describe("Model asset ID"),
      online: z.boolean().optional().describe("Online inference (default true)"), region: z.string().optional(),
    }, async (p) => safeTool(async () => { w();
      return client.post(`${ml(p.region||r)}/ml/v4/deployments`, {
        name:p.name, space_id:p.space_id, asset:{id:p.asset_id},
        online:p.online!==false ? {} : undefined,
      }, {version:ver});
    }));
  
    server.tool("watson_delete_deployment", "Delete a model deployment", {
      deployment_id: z.string(), space_id: z.string(), region: z.string().optional(),
    }, async (p) => safeTool(async () => { w();
      await client.delete(`${ml(p.region||r)}/ml/v4/deployments/${p.deployment_id}`, {version:ver,space_id:p.space_id});
      return {message:"Deployment deleted"};
    }));
  
    server.tool("watson_list_spaces", "List Watson deployment spaces", {
      region: z.string().optional(),
    }, async (p) => safeTool(() => client.get(`${ml(p.region||r)}/v2/spaces`, {version:ver})));
  
    server.tool("watson_create_space", "Create a Watson deployment space", {
      name: z.string(), description: z.string().optional(),
      compute_name: z.string().optional().describe("WML instance CRN"), region: z.string().optional(),
    }, async (p) => safeTool(async () => { w();
      const body: Record<string,unknown> = {name:p.name, description:p.description};
      if(p.compute_name) body.compute=[{name:p.compute_name,type:"machine_learning"}];
      return client.post(`${ml(p.region||r)}/v2/spaces`, body, {version:ver});
    }));
  }

• src/server.ts:13-13 (registration)

  Import of registerWatsonTools from the watson tool module.

  import { registerWatsonTools } from "./tools/watson/index.js";

• src/server.ts:68-69 (registration)

  Invocation of registerWatsonTools to register all watson tools on the MCP server, including watson_get_model.

  registerWatsonTools(server, client, config);
  console.error(`  ✓ Watson AI (8 tools)`);