Agentled MCP Server

Instructions

Implementation Reference

• src/tools/chat.ts:34-64 (handler)

  The async handler function for the 'chat' tool that calls the agent API.

  async ({ message, session_id }, extra) => {
      const client = clientFactory(extra);
      try {
          const result = await client.chat(message, session_id);
  
          if (result.error) {
              return {
                  content: [{
                      type: 'text' as const,
                      text: `Chat error: ${result.error}`,
                  }],
                  isError: true,
              };
          }
  
          return {
              content: [{
                  type: 'text' as const,
                  text: result.response || JSON.stringify(result, null, 2),
              }],
          };
      } catch (error: any) {
          return {
              content: [{
                  type: 'text' as const,
                  text: `Chat request failed: ${error?.message || 'Unknown error'}`,
              }],
              isError: true,
          };
      }
  }

• src/tools/chat.ts:13-66 (registration)

  Registration function for the 'chat' tool using server.tool.

  export function registerChatTools(server: McpServer, clientFactory: ClientFactory) {
  
      server.tool(
          'chat',
          `Send a message to the AgentLed AI agent and get a response. The agent can reason, plan, and build workflows through natural language conversation — no need to construct pipeline JSON manually.
  
  Use this tool when you want to:
  - Build a workflow from a high-level description ("Create a lead enrichment workflow for SaaS companies")
  - Get recommendations on how to structure a workflow
  - Ask questions about available integrations or capabilities
  - Iterate on workflow design through conversation
  
  The agent has access to the same planning tools, workflow builder, and workspace context as the in-app chat.
  
  For multi-turn conversations, pass the session_id returned from the first message to maintain context across messages.
  
  Example: chat("Build me a workflow that takes a LinkedIn company URL, enriches the data, and scores it by ICP fit")`,
          {
              message: z.string().describe('The message to send to the AI agent'),
              session_id: z.string().optional().describe('Session ID for multi-turn conversations. Use the session_id from a previous response to continue the same conversation.'),
          },
          async ({ message, session_id }, extra) => {
              const client = clientFactory(extra);
              try {
                  const result = await client.chat(message, session_id);
  
                  if (result.error) {
                      return {
                          content: [{
                              type: 'text' as const,
                              text: `Chat error: ${result.error}`,
                          }],
                          isError: true,
                      };
                  }
  
                  return {
                      content: [{
                          type: 'text' as const,
                          text: result.response || JSON.stringify(result, null, 2),
                      }],
                  };
              } catch (error: any) {
                  return {
                      content: [{
                          type: 'text' as const,
                          text: `Chat request failed: ${error?.message || 'Unknown error'}`,
                      }],
                      isError: true,
                  };
              }
          }
      );
  }

• src/tools/chat.ts:30-33 (schema)

  Input validation schema for the 'chat' tool using Zod.

  {
      message: z.string().describe('The message to send to the AI agent'),
      session_id: z.string().optional().describe('Session ID for multi-turn conversations. Use the session_id from a previous response to continue the same conversation.'),
  },