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raeseoklee

MCP Workbench MCP Server

by raeseoklee

inspect_server

Connect to an MCP server to inspect its capabilities, version, and supported features for testing and validation purposes.

Instructions

Connect to an MCP server and inspect its capabilities, version, and supported features.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
transportYesTransport type to use for connecting to the server
urlNoServer URL (required for streamable-http transport)
commandNoCommand to launch the server (required for stdio transport)
argsNoArguments to pass to the server command
headersNoHTTP headers to send (e.g. Authorization)
timeoutMsNoTimeout in milliseconds (default: 30000)

Implementation Reference

  • The core implementation of the inspect_server tool, which runs CLI commands to inspect an MCP server.
    export async function inspectServer(
      input: InspectServerInput,
    ): Promise<InspectServerOutput> {
      const args = buildArgs(input);
      const result = await runCli(args, { timeoutMs: input.timeoutMs });
    
      const session: NegotiatedSession = JSON.parse(result.stdout);
    
      const caps = session.serverCapabilities;
      const capabilities = {
        tools: "tools" in caps,
        resources: "resources" in caps,
        prompts: "prompts" in caps,
        completions: "completions" in caps,
        logging: "logging" in caps,
      };
    
      const structured: InspectServerOutput["structured"] = {
        serverName: session.serverInfo.name,
        serverVersion: session.serverInfo.version,
        protocolVersion: session.protocolVersion,
        capabilities,
      };
      if (session.serverInstructions) {
        structured.instructions = session.serverInstructions;
      }
    
      const capLines = (
        ["tools", "resources", "prompts", "completions", "logging"] as const
      )
        .map((c) => `  ${capabilities[c] ? "\u2713" : "\u25CB"} ${c}`)
        .join("\n");
    
      const text = [
        `Server: ${structured.serverName} v${structured.serverVersion}`,
        `Protocol: ${structured.protocolVersion}`,
        "",
        "Capabilities:",
        capLines,
      ].join("\n");
    
      return { text, structured };
    }
  • Input and output interface definitions for the inspect_server tool.
    export interface InspectServerInput {
      transport: "stdio" | "streamable-http";
      url?: string;
      command?: string;
      args?: string | string[];
      headers?: Record<string, string>;
      timeoutMs?: number;
    }
  • src/server.ts:221-228 (registration)
    The request handler registration where inspect_server is mapped to its execution function.
    case "inspect_server": {
      const output = await inspectServer(
        args as unknown as Parameters<typeof inspectServer>[0],
      );
      return {
        content: [{ type: "text" as const, text: output.text }],
      };
    }
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 of behavioral disclosure. It mentions connecting and inspecting but lacks details on authentication needs, rate limits, error handling, or what happens during connection failures. For a tool with 6 parameters and no annotations, this is insufficient to inform safe and effective use.

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

Conciseness5/5

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary details. Every word contributes to understanding the tool's function, making it appropriately sized and well-structured.

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

Completeness3/5

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

Given the tool's complexity (6 parameters, no annotations, no output schema), the description is minimally adequate. It states what the tool does but lacks details on behavior, output format, or integration with siblings. Without annotations or output schema, more context would help, but it's not completely inadequate.

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?

Schema description coverage is 100%, so the schema fully documents all 6 parameters. The description adds no specific parameter semantics beyond implying connection and inspection, which aligns with the schema but doesn't provide extra value. This meets the baseline for high schema coverage.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Connect to an MCP server and inspect its capabilities, version, and supported features.' It specifies the verb ('connect' and 'inspect') and resource ('MCP server'), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'explain_failure' or 'run_spec', which prevents a perfect score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for server inspection, or how it relates to sibling tools such as 'generate_spec' or 'run_spec'. Usage is implied but not explicitly stated, leaving gaps for an AI agent.

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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