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execute_mcp_client

Execute MCP client commands to offload AI research tasks, fetch data, and facilitate multi-agent interactions through task delegation.

Instructions

Offload certain tasks to AI. Used for research purposes, do not use for code editing or anything code related. Only used to fetch data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commandYesThe MCP client command to execute

Implementation Reference

  • Handler logic for the 'execute_mcp_client' tool. Parses the command argument, executes it via safeCommandPipe using the configured executable, and returns the stdout or stderr as text content, or an error response.
    case 'execute_mcp_client': {
      const args = request.params.arguments as { command: string };
      try {
        const { stdout, stderr } = await this.safeCommandPipe(args.command, this.executable);
        return {
          content: [
            {
              type: 'text',
              text: stdout || stderr,
            },
          ],
        };
      } catch (error: any) {
        return {
          content: [
            {
              type: 'text',
              text: `Error executing MCP client command: ${error?.message || 'Unknown error'}`,
            },
          ],
          isError: true,
        };
      }
    }
  • src/index.ts:207-220 (registration)
    Registration of the 'execute_mcp_client' tool in the ListTools response, including name, description, and input schema requiring a 'command' string.
    {
      name: 'execute_mcp_client',
      description: 'Offload certain tasks to AI. Used for research purposes, do not use for code editing or anything code related. Only used to fetch data.',
      inputSchema: {
        type: 'object',
        properties: {
          command: {
            type: 'string',
            description: 'The MCP client command to execute',
          },
        },
        required: ['command'],
      },
    },
  • Input schema definition for the 'execute_mcp_client' tool, specifying an object with a required 'command' string property.
    inputSchema: {
      type: 'object',
      properties: {
        command: {
          type: 'string',
          description: 'The MCP client command to execute',
        },
      },
      required: ['command'],
    },
  • Helper method that spawns the executable command process using spawn('/bin/bash'), pipes the input to stdin, captures stdout and stderr with debug logging, and resolves with output or rejects on error. Used by the tool handler.
    private async safeCommandPipe(input: string, command: string, forceJson: boolean = false): Promise<{stdout: string, stderr: string}> {
      return new Promise((resolve, reject) => {
        // Get the full path to the executable
        const executablePath = join(this.workingDirectory, this.executable);
        console.error(`[Debug] Executing: ${executablePath} in ${this.workingDirectory}`);
        
        const proc = spawn('/bin/bash', [executablePath], { 
          shell: false,
          env: process.env, // Pass through environment variables
          cwd: this.workingDirectory // Use configured working directory
        });
        let stdout = '';
        let stderr = '';
    
        proc.stdout.on('data', (data) => {
          const str = data.toString();
          console.error(`[Debug] stdout: ${str}`);
          stdout += str;
        });
    
        proc.stderr.on('data', (data) => {
          const str = data.toString();
          console.error(`[Debug] stderr: ${str}`);
          stderr += str;
        });
    
        proc.on('error', (err) => {
          console.error(`[Debug] Process error: ${err.message}`);
          reject(new Error(`Failed to start process: ${err.message}`));
        });
    
        proc.on('close', (code) => {
          console.error(`[Debug] Process exited with code ${code}`);
          if (code === 0) {
            resolve({ stdout, stderr });
          } else {
            reject(new Error(`Command failed with code ${code}. stderr: ${stderr}`));
          }
        });
    
        // Safely write input with newline and close stdin
        // If forceJson is true, append a directive to return JSON
        const inputWithDirective = forceJson ? input + ' [RESPOND IN JSON KEY-VALUE PAIRS]' : input;
        proc.stdin.write(Buffer.from(inputWithDirective + '\n'));
        proc.stdin.end();
      });
    }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions 'offload tasks to AI' and 'fetch data', but doesn't disclose behavioral traits such as what types of tasks are supported, how data is fetched, potential side effects, error handling, or performance characteristics. This leaves significant gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three short sentences with no wasted words, making it efficient. However, it could be more front-loaded by stating the core purpose first, and some phrases like 'Used for research purposes' are a bit vague, but overall it's appropriately sized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no annotations, no output schema, and a single parameter with good schema coverage, the description is incomplete. It lacks details on what the tool actually does beyond 'fetch data', how it interacts with AI, what results to expect, or how it differs from siblings. For a tool named 'execute_mcp_client', this leaves too much ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 1 parameter with 100% description coverage, so the schema already documents the 'command' parameter. The description doesn't add any meaning beyond the schema, such as examples of valid commands or how they relate to 'offloading tasks'. Baseline 3 is appropriate since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'offload certain tasks to AI' and 'fetch data', which gives a general purpose but lacks specificity about what 'tasks' or 'data' means. It distinguishes from siblings by mentioning 'do not use for code editing', but doesn't clearly define what it does do versus what siblings do. The purpose is vague rather than specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on when to use ('for research purposes', 'fetch data') and explicit exclusions ('do not use for code editing or anything code related'). However, it doesn't mention alternatives or compare with sibling tools like execute_map_reduce_mcp_client or execute_parallel_mcp_client, so it's not fully explicit about when to choose this over others.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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