MCP Ollama Consult Server

// Compatibility re-export for legacy imports. The real implementation lives in `src/handlers/legacy-handlers.ts`. export * from './handlers/legacy-handlers'; import defaultExport from './handlers/legacy-handlers'; export default defaultExport; import axios from 'axios'; import fs from 'fs/promises'; import path from 'path'; import os from 'os'; import { Client } from '@modelcontextprotocol/sdk/client/index.js'; import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js'; import { CallToolResultSchema } from '@modelcontextprotocol/sdk/types.js'; import { SequentialChainParams, SequentialChainResult, ChainStep, ConversationMessage, } from './conversationTypes.js'; const OLLAMA_BASE_URL = process.env.OLLAMA_BASE_URL || 'http://localhost:11434'; // Default timeout for Ollama calls (2 minutes) // Can be overridden per-consultant using the timeoutMs parameter axios.defaults.timeout = axios.defaults.timeout || 120_000; // Helper: determine whether a model name looks like a cloud model function looksLikeCloudModelName(n: string | undefined): boolean { if (!n) return false; // Accept names that contain ':cloud' or '-cloud' (covers common Ollama naming) return n.includes(':cloud') || n.includes('-cloud'); } // Helper: given a model object from /api/tags, decide if it's safe to use function modelObjIsSafe(m: any): boolean { if (!m) return false; const name = m.name || ''; if (looksLikeCloudModelName(name)) return true; // Only accept models that are actually installed locally (not just available for download) if (m.installed || m.local || m.downloaded) return true; return false; } // Helper: check availability for a given model name by querying tags. async function isModelAvailable(modelName: string): Promise<boolean> { if (!modelName) return false; // Allow opt-out for strict checking via env var. By default we are permissive to // avoid breaking existing users/tests. Set OLLAMA_STRICT_MODEL_CHECK=true to // enable strict availability checking (cloud or reported-installed only). const STRICT = process.env.OLLAMA_STRICT_MODEL_CHECK === 'true'; if (!STRICT) return true; if (looksLikeCloudModelName(modelName)) return true; try { const resp = await axios.get(`${OLLAMA_BASE_URL}/api/tags`); const models = resp.data.models || []; const found = models.find((m: any) => m && m.name === modelName); if (!found) return false; return modelObjIsSafe(found); } catch (_e) { void _e; // If we couldn't query Ollama, be conservative and return false so callers can handle gracefully return false; } } export function listTools() { return { tools: [ { name: 'consult_ollama', description: 'Consult an Ollama model with a prompt and get its response for reasoning from another viewpoint.', inputSchema: { type: 'object', properties: { model: { type: 'string' }, prompt: { type: 'string' }, system_prompt: { type: 'string' }, }, required: ['model', 'prompt'], }, }, { name: 'list_ollama_models', description: 'List all available Ollama models on the local instance.', inputSchema: { type: 'object', properties: {} }, }, { name: 'compare_ollama_models', description: 'Run the same prompt against multiple Ollama models and return their outputs side-by-side for comparison.', inputSchema: { type: 'object', properties: { models: { type: 'array', items: { type: 'string' } }, prompt: { type: 'string' }, system_prompt: { type: 'string' }, }, required: ['prompt'], }, }, { name: 'remember_consult', description: 'Store the result of a consult into a local memory store (or configured memory service).', inputSchema: { type: 'object', properties: { key: { type: 'string' }, prompt: { type: 'string' }, model: { type: 'string' }, response: { type: 'string' }, }, required: ['prompt'], }, }, { name: 'sequential_consultation_chain', description: 'Run a sequence of consultations where each consultant builds on previous responses, enabling complex multi-step reasoning.', inputSchema: { type: 'object', properties: { consultants: { type: 'array', items: { type: 'object', properties: { id: { type: 'string' }, model: { type: 'string' }, prompt: { type: 'string' }, systemPrompt: { type: 'string' }, temperature: { type: 'number' }, timeoutMs: { type: 'number' }, }, required: ['id', 'model', 'prompt'], }, }, context: { type: 'object', properties: { systemPrompt: { type: 'string' }, variables: { type: 'object' }, passThrough: { type: 'boolean' }, }, }, flowControl: { type: 'object', properties: { continueOnError: { type: 'boolean' }, maxRetries: { type: 'number' }, retryDelayMs: { type: 'number' }, }, }, memory: { type: 'object', properties: { storeConversation: { type: 'boolean' }, memoryKey: { type: 'string' }, }, }, }, required: ['consultants'], }, }, ], }; } // The handler expects the `params` object (i.e. the CallTool request params). export async function callToolHandler(params: { name: string; arguments?: any }): Promise<any> { const name = params.name; const args = params.arguments || {}; switch (name) { case 'consult_ollama': { const model = args?.model as string; const prompt = args?.prompt as string; const system_prompt = args?.system_prompt as string | undefined; try { // Sanity-check model availability to avoid 404s: accept cloud models or definitely-installed locals const available = await isModelAvailable(model); if (!available) { // Try to provide suggestions (cloud/installed models) let suggestions: string[] = []; try { const resp = await axios.get(`${OLLAMA_BASE_URL}/api/tags`); const raw = resp.data.models || []; suggestions = raw .filter((m: any) => modelObjIsSafe(m)) .map((m: any) => m.name) .slice(0, 5); } catch (_e) { void _e; // ignore } const suggestText = suggestions.length > 0 ? ` Available (cloud/installed): ${suggestions.join(', ')}` : ''; return { content: [ { type: 'text', text: `Model '${model}' is not available locally and is not a recognized cloud model. Use a model name that ends with ':cloud' or '-cloud', or install the model locally.${suggestText}`, }, ], isError: true, }; } const response = await axios.post(`${OLLAMA_BASE_URL}/api/generate`, { model, prompt, system: system_prompt, stream: false, }); return { content: [ { type: 'text', text: response.data.response, }, ], }; } catch (_error) { const error = _error; const message = error instanceof Error ? error.message : 'Unknown error'; return { content: [ { type: 'text', text: `Error consulting Ollama: ${message}`, }, ], isError: true, }; } } case 'list_ollama_models': { try { const response = await axios.get(`${OLLAMA_BASE_URL}/api/tags`); // Add defensive checks for response data if (!response || !response.data) { throw new Error('Empty response from Ollama API'); } const rawModels = response.data.models || []; if (!Array.isArray(rawModels)) { throw new Error(`Unexpected response format: models is not an array`); } const safe = rawModels.filter((m: any) => modelObjIsSafe(m)).map((m: any) => m.name); let modelsList: string[] = []; if (safe.length > 0) { modelsList = safe; } else { // Fallback: prefer cloud-like names if present, otherwise return raw list const cloudOnly = rawModels .filter((m: any) => m && looksLikeCloudModelName(m.name)) .map((m: any) => m.name); modelsList = cloudOnly.length > 0 ? cloudOnly : rawModels.map((m: any) => m.name); } const note = safe.length > 0 ? ' (cloud models and installed locals)' : ' (no installed locals detected; showing cloud-like or raw models)'; return { content: [ { type: 'text', text: `Available models${note}: ${modelsList.join(', ')}`, }, ], }; } catch (_error) { const error = _error; const message = error instanceof Error ? error.message : 'Unknown error'; const stack = error instanceof Error ? error.stack : ''; return { content: [ { type: 'text', text: `Error listing models: ${message}\n${stack ? `Stack: ${stack}` : ''}`, }, ], isError: true, }; } } case 'compare_ollama_models': { const modelsArg = args?.models as string[] | undefined; const prompt = args?.prompt as string; const system_prompt = args?.system_prompt as string | undefined; if (!prompt) { return { content: [ { type: 'text', text: 'Missing required argument: prompt', }, ], isError: true, }; } let modelsToUse: string[] = []; if (Array.isArray(modelsArg) && modelsArg.length > 0) { const ok: string[] = []; const bad: string[] = []; for (const m of modelsArg) { try { if (await isModelAvailable(m)) ok.push(m); else bad.push(m); } catch (_e) { void _e; bad.push(m); } } if (ok.length === 0) { return { content: [ { type: 'text', text: `None of the requested models are available (cloud or installed): ${bad.join(', ')}. Try using -cloud models or install the models locally.`, }, ], isError: true, }; } if (bad.length > 0) { // Inform about skipped models // We'll continue with the available ones } modelsToUse = ok; } else { try { const resp = await axios.get(`${OLLAMA_BASE_URL}/api/tags`); const candidates = (resp.data.models || []) .filter((m: any) => modelObjIsSafe(m)) .map((m: any) => m.name) .slice(0, 2); modelsToUse = candidates.length > 0 ? candidates : (resp.data.models || []).map((m: any) => m.name).slice(0, 2); } catch (_err) { void _err; modelsToUse = ['llama2']; } } const contents: any[] = []; for (const m of modelsToUse) { try { const gen = await axios.post(`${OLLAMA_BASE_URL}/api/generate`, { model: m, prompt, system: system_prompt, stream: false, }); contents.push({ type: 'text', text: `Model ${m}:\n${gen.data.response}` }); } catch (_e) { const e = _e; const message = e instanceof Error ? e.message : String(e); contents.push({ type: 'text', text: `Model ${m} failed: ${message}` }); } } return { content: contents }; } case 'remember_consult': { const key = args?.key as string | undefined; const prompt = args?.prompt as string | undefined; const model = args?.model as string | undefined; let responseText = args?.response as string | undefined; if (!prompt) { return { content: [ { type: 'text', text: 'Missing required argument: prompt', }, ], isError: true, }; } if (!responseText) { // generate using Ollama if a model was provided if (!model) { return { content: [ { type: 'text', text: "Missing 'response' and no 'model' provided to generate it." }, ], isError: true, }; } try { const gen = await axios.post(`${OLLAMA_BASE_URL}/api/generate`, { model, prompt, stream: false, }); responseText = gen.data.response; } catch (_e) { const e = _e; const message = e instanceof Error ? e.message : String(e); return { content: [{ type: 'text', text: `Failed to generate response: ${message}` }], isError: true, }; } } const payload = { key: key || `consult-${Date.now()}`, prompt, model: model || null, response: responseText, resource: { uri: `mcp-consult://consult/${Date.now()}`, text: responseText, }, }; // Build candidate servers list: env override, vscode mcp.json entries (prefer 'remember' then 'memory'), and MEMORY_MCP_CMD const tryServers: Array<any> = []; try { const envCfg = process.env.REMEMBER_MCP_CONFIG; if (envCfg) { try { const parsed = JSON.parse(envCfg); tryServers.push(parsed); } catch (_e) { void _e; const [cmd, ...argList] = envCfg.split(/\s+/); tryServers.push({ type: 'stdio', command: cmd, args: argList }); } } } catch (_e) { void _e; // ignore } try { const vscodeMcpPath = process.env.VSCODE_MCP_JSON || path.join(os.homedir(), 'Library', 'Application Support', 'Code', 'User', 'mcp.json'); const raw = await fs.readFile(vscodeMcpPath, 'utf-8').catch(() => ''); if (raw) { try { const cfg = JSON.parse(raw); const servers = cfg.servers || {}; const keys = Object.keys(servers || {}); const rememberKey = keys.find((k) => k.toLowerCase().includes('remember')); const memoryKey = keys.find((k) => k.toLowerCase().includes('memory')); if (rememberKey) tryServers.push(servers[rememberKey]); if (memoryKey) tryServers.push(servers[memoryKey]); } catch (_e) { void _e; // ignore parse errors } } } catch (_e) { void _e; // ignore } if (process.env.MEMORY_MCP_CMD) { const cmd = process.env.MEMORY_MCP_CMD; const argsList = process.env.MEMORY_MCP_ARGS ? JSON.parse(process.env.MEMORY_MCP_ARGS) : []; tryServers.push({ type: 'stdio', command: cmd, args: argsList }); } // Helper to attempt calling a memory MCP server via stdio transport async function tryCallMemoryServer(serverCfg: any): Promise<any> { if (!serverCfg || serverCfg.type !== 'stdio' || !serverCfg.command) throw new Error('unsupported server cfg'); const transport = new StdioClientTransport({ command: serverCfg.command, args: serverCfg.args || [], }); const client = new Client({ name: 'mcp-memory-client', version: '1.0.0', }); // limit connection time const conn = client.connect(transport); const timeoutMs = 10_000; await Promise.race([ conn, new Promise((_, rej) => setTimeout(() => rej(new Error('connect timeout')), timeoutMs)), ]); const toolsRes = await client.listTools(); const tools = toolsRes.tools || []; const patterns = [ 'remember', 'store', 'add', 'write', 'create', 'save', 'observe', 'put', 'set', 'append', ]; let chosen: any = null; for (const p of patterns) { const found = tools.find((t: any) => t.name && t.name.toLowerCase().includes(p)); if (found) { chosen = found; break; } } if (!chosen && tools.length > 0) { chosen = tools.find((t: any) => t.inputSchema && t.inputSchema.type === 'object') || tools[0]; } if (!chosen) { await client.close(); throw new Error('no suitable tool on memory server'); } const inputSchema = chosen.inputSchema || {}; const props = inputSchema.properties || {}; const toolArgs: any = {}; if ( props['response'] !== undefined || props['text'] !== undefined || props['value'] !== undefined || props['content'] !== undefined ) { if (props['response'] !== undefined) toolArgs.response = payload.response; if (props['text'] !== undefined) toolArgs.text = payload.response; if (props['value'] !== undefined) toolArgs.value = payload.response; if (props['content'] !== undefined) toolArgs.content = payload.response; } if (props['resource'] !== undefined || props['uri'] !== undefined) { toolArgs.resource = payload.resource; } if (props['key'] !== undefined) toolArgs.key = payload.key; if (props['prompt'] !== undefined) toolArgs.prompt = payload.prompt; if (props['model'] !== undefined) toolArgs.model = payload.model; if (Object.keys(toolArgs).length === 0) { toolArgs.key = payload.key; toolArgs.prompt = payload.prompt; toolArgs.response = payload.response; toolArgs.resource = payload.resource; } const callRes = await client .callTool({ name: chosen.name, arguments: toolArgs }, CallToolResultSchema) .catch((e) => ({ isError: true, error: e })); await client.close(); return callRes; } // Try candidate servers sequentially for (const s of tryServers) { try { const r = await tryCallMemoryServer(s); if (r && !r.isError) { return r; } } catch (_e) { void _e; // try next server } } // Fallback: persist to a simple local memory directory const memoryDir = process.env.MEMORY_DIR || path.join('/tmp', 'mcp-consult-memory'); try { await fs.mkdir(memoryDir, { recursive: true }); const id = `${Date.now()}-${Math.random().toString(36).slice(2, 9)}`; const filePath = path.join(memoryDir, `observation-${id}.json`); const payloadToWrite = { key: payload.key || null, prompt: payload.prompt, model: payload.model || null, response: payload.response, _meta: { createdAt: new Date().toISOString() }, }; await fs.writeFile(filePath, JSON.stringify(payloadToWrite, null, 2), 'utf-8'); return { content: [ { type: 'text', text: `Saved consult to ${filePath}`, }, { type: 'resource', resource: { uri: `file://${filePath}`, text: payload.response, }, }, ], }; } catch (_err) { const message = _err instanceof Error ? _err.message : String(_err); return { content: [{ type: 'text', text: `Failed to save memory: ${message}` }], isError: true, }; } } case 'sequential_consultation_chain': { const params = args as SequentialChainParams; const consultants = params.consultants || []; const context = params.context || {}; const flowControl = params.flowControl || {}; const memory = params.memory || {}; if (!consultants || consultants.length === 0) { return { content: [ { type: 'text', text: 'Error: No consultants provided for sequential chain', }, ], isError: true, }; } try { const conversationId = `chain-${Date.now()}`; const startTime = Date.now(); const steps: ChainStep[] = []; const messages: ConversationMessage[] = []; let conversationContext = ''; const consultantResponses: Record<string, string> = {}; // Add system prompt if provided if (context.systemPrompt) { conversationContext += `System: ${context.systemPrompt}\n\n`; } for (let i = 0; i < consultants.length; i++) { const consultant = consultants[i]; const stepStartTime = Date.now(); let retryCount = 0; let stepSuccess = false; let stepResponse = ''; let stepError: string | undefined; // Replace placeholders in prompt with previous responses let finalPrompt = consultant.prompt; for (const [id, response] of Object.entries(consultantResponses)) { finalPrompt = finalPrompt.replace(new RegExp(`\\{${id}\\}`, 'g'), response); } // Build prompt with conversation context if passThrough is enabled if (context.passThrough && conversationContext) { finalPrompt = `${conversationContext}${consultant.id}: ${finalPrompt}`; } while (!stepSuccess && retryCount <= (flowControl.maxRetries || 0)) { try { // Check model availability const available = await isModelAvailable(consultant.model); if (!available) { throw new Error(`Model '${consultant.model}' is not available`); } // Make the consultation call const response = await axios.post( `${OLLAMA_BASE_URL}/api/generate`, { model: consultant.model, prompt: finalPrompt, system: consultant.systemPrompt || context.systemPrompt, temperature: consultant.temperature, stream: false, }, { timeout: consultant.timeoutMs || 120000, } ); stepResponse = response.data.response; stepSuccess = true; // Store response for placeholder replacement consultantResponses[consultant.id] = stepResponse; // Add to conversation context for next consultant if (context.passThrough) { conversationContext += `${consultant.id}: ${consultant.prompt}\nResponse: ${stepResponse}\n\n`; } // Create message record const message: ConversationMessage = { consultantId: consultant.id, role: 'assistant', content: stepResponse, timestamp: new Date(), metadata: { model: consultant.model, temperature: consultant.temperature, duration: Date.now() - stepStartTime, }, }; messages.push(message); } catch (_error) { const error = _error; retryCount++; stepError = error instanceof Error ? error.message : 'Unknown error'; if (retryCount <= (flowControl.maxRetries || 0)) { // Wait before retry if (flowControl.retryDelayMs) { await new Promise((resolve) => setTimeout(resolve, flowControl.retryDelayMs)); } } else { // Max retries reached if (flowControl.continueOnError) { stepResponse = `[Error after ${retryCount} retries: ${stepError}]`; consultantResponses[consultant.id] = stepResponse; if (context.passThrough) { conversationContext += `${consultant.id}: ${consultant.prompt}\nError: ${stepError}\n\n`; } } else { // Fail the entire chain return { content: [ { type: 'text', text: `Sequential chain failed at step ${i + 1} (${consultant.id}): ${stepError}`, }, ], isError: true, }; } } } } // Record the step const step: ChainStep = { step: i + 1, consultantId: consultant.id, model: consultant.model, prompt: consultant.prompt, response: stepResponse, duration: Date.now() - stepStartTime, error: stepError, retryCount: retryCount, }; steps.push(step); } const totalDuration = Date.now() - startTime; const completedSteps = steps.filter((s) => !s.error).length; const result: SequentialChainResult = { conversationId, status: completedSteps === consultants.length ? 'completed' : 'partial', completedSteps, totalSteps: consultants.length, duration: totalDuration, steps, }; // Store in memory if requested if (memory.storeConversation) { try { // This would integrate with the remember_consult functionality const memoryKey = memory.memoryKey || `sequential_chain_${conversationId}`; const memoryData = { key: memoryKey, prompt: `Sequential consultation chain: ${consultants.map((c) => c.id).join(' → ')}`, response: JSON.stringify(result), model: 'sequential_chain', }; // Call remember_consult internally await callToolHandler({ name: 'remember_consult', arguments: memoryData, }); } catch (memError) { // Memory storage failed, but don't fail the main operation console.warn('Failed to store conversation in memory:', memError); } } // Format the output const formattedSteps = steps .map((step) => { const status = step.error ? `❌ Failed${step.retryCount ? ` (${step.retryCount} retries)` : ''}` : '✅ Success'; return ( `**Step ${step.step}: ${step.consultantId}** (${step.model}) ${status}\n` + `Duration: ${step.duration}ms\n` + `Prompt: ${step.prompt}\n` + `Response: ${step.response}${step.error ? `\nError: ${step.error}` : ''}\n` ); }) .join('\n---\n'); const summary = `# Sequential Consultation Chain Results\n\n` + `**Status**: ${result.status}\n` + `**Completed Steps**: ${completedSteps}/${consultants.length}\n` + `**Total Duration**: ${totalDuration}ms\n` + `**Conversation ID**: ${conversationId}\n\n` + `## Consultation Steps\n\n${formattedSteps}\n\n` + `## Final Context\n${conversationContext}`; return { content: [ { type: 'text', text: summary, }, ], }; } catch (_error) { const message = _error instanceof Error ? _error.message : 'Unknown error'; return { content: [ { type: 'text', text: `Error in sequential consultation chain: ${message}`, }, ], isError: true, }; } } default: throw new Error(`Unknown tool: ${name}`); } } export default { listTools, callToolHandler };