यदि आप अपना स्वयं का LLM एप्लिकेशन बना रहे हैं, तो आप आधिकारिक MCP SDK का उपयोग करके सीधे Synapse MCP server से कनेक्ट कर सकते हैं। इससे आपके ऐप को सभी 79 Synapse टूल तक पहुँच मिलती है।
\n| भाषा | \nपैकेज | \n
|---|---|
| TypeScript/JavaScript | \n@modelcontextprotocol/sdk | \n
| Python | \nmcp | \n
npm install @modelcontextprotocol/sdkimport { Client } from "@modelcontextprotocol/sdk/client/index.js";\nimport { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";\n\nconst transport = new StdioClientTransport({\n command: "npx",\n args: ["-y", "synapse-mcp-api@latest"],\n env: {\n SYNAPSE_MIND_KEY: process.env.SYNAPSE_MIND_KEY!,\n SYNAPSE_URL: "https://synapse.schaefer.zone",\n },\n});\n\nconst client = new Client(\n { name: "my-app", version: "1.0.0" },\n { capabilities: {} }\n);\n\nawait client.connect(transport);\n\n// List all available tools\nconst { tools } = await client.listTools();\nconsole.log(`Available tools: ${tools.length}`);\nfor (const tool of tools) {\n console.log(`- ${tool.name}: ${tool.description}`);\n}\n\n// Call a tool\nconst result = await client.callTool({\n name: "memory_recall",\n arguments: {},\n});\nconsole.log(result.content);\n\n// Store a memory\nawait client.callTool({\n name: "memory_store",\n arguments: {\n category: "fact",\n key: "custom_client_test",\n content: "Built a custom MCP client",\n tags: ["test", "mcp"],\n priority: "normal",\n },\n});\n\nawait client.close();import { Client } from "@modelcontextprotocol/sdk/client/index.js";\nimport { SSEClientTransport } from "@modelcontextprotocol/sdk/client/sse.js";\n\nconst transport = new SSEClientTransport(\n new URL("https://synapse-mcp.schaefer.zone/sse"),\n {\n requestInit: {\n headers: {\n Authorization: `Bearer ${process.env.SYNAPSE_MIND_KEY}`,\n },\n },\n }\n);\n\nconst client = new Client(\n { name: "my-app", version: "1.0.0" },\n { capabilities: {} }\n);\n\nawait client.connect(transport);\n// ... use as abovepip install mcpfrom mcp import ClientSession, StdioServerParameters\nfrom mcp.client.stdio import stdio_client\n\nserver_params = StdioServerParameters(\n command="npx",\n args=["-y", "synapse-mcp-api@latest"],\n env={\n "SYNAPSE_MIND_KEY": "mk_YOUR_KEY",\n "SYNAPSE_URL": "https://synapse.schaefer.zone",\n },\n)\n\nasync with stdio_client(server_params) as (read, write):\n async with ClientSession(read, write) as session:\n await session.initialize()\n\n # List tools\n tools = await session.list_tools()\n print(f"Available tools: {len(tools.tools)}")\n\n # Call a tool\n result = await session.call_tool("memory_recall", {})\n print(result.content)\n\n # Store a memory\n await session.call_tool("memory_store", {\n "category": "fact",\n "key": "python_client_test",\n "content": "Built a Python MCP client",\n "tags": ["test", "mcp", "python"],\n "priority": "normal",\n })कनेक्ट करते समय, आप Mcp-Tool-Profile हेडर (HTTP/SSE) या MCP_PROFILE env var (stdio) के माध्यम से विशिष्ट टूल प्रोफाइल का अनुरोध कर सकते हैं:
// stdio: set env var\nenv: {\n SYNAPSE_MIND_KEY: "mk_...",\n MCP_PROFILE: "minimal", // 8 tools instead of 119\n}\n\n// HTTP/SSE: set header\nrequestInit: {\n headers: {\n Authorization: "Bearer mk_...",\n "Mcp-Tool-Profile": "minimal",\n },\n}try {\n const result = await client.callTool({ name: "memory_recall", arguments: {} });\n if (result.isError) {\n console.error("Tool error:", result.content);\n } else {\n console.log("Success:", result.content);\n }\n} catch (err) {\n console.error("MCP error:", err);\n}