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llm_status

Check connection status and list available models for OpenAI-compatible LLM servers to verify API accessibility and model options.

Instructions

Verifica el estado de conexión con el servidor LLM y lista los modelos disponibles

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseURLNoURL del servidor OpenAI-compatible (ej: http://localhost:1234/v1, http://localhost:11434/v1)
apiKeyNoAPI Key (requerida para OpenAI/Azure, opcional para servidores locales)

Implementation Reference

  • The handler function that executes the llm_status tool logic: connects to LLM server, checks status, lists models, and returns formatted response.
    async llm_status(args: z.infer<typeof ConnectionConfigSchema> = {}) {
      const client = getClient(args);
      const usedBaseURL = args.baseURL || defaultConfig.baseURL;
      const status = await client.getServerStatus();
      if (status.connected) {
        const models = await client.listModels();
        return {
          content: [
            {
              type: "text" as const,
              text: `✅ **LLM Server Conectado**\n\n` +
                `- URL: ${usedBaseURL}\n` +
                `- Modelos disponibles: ${status.models}\n\n` +
                `**Modelos:**\n${models.map(m => `- ${m.id}`).join("\n") || "Ninguno"}`,
            },
          ],
        };
      } else {
        return {
          content: [
            {
              type: "text" as const,
              text: `❌ **LLM Server No Conectado**\n\n` +
                `No se pudo conectar a ${usedBaseURL}\n\n` +
                `Verifica que:\n` +
                `1. El servidor LLM está ejecutándose\n` +
                `2. La URL es correcta\n` +
                `3. El puerto está accesible`,
            },
          ],
        };
      }
    },
  • Zod schema defining the input parameters for connection configuration, used in llm_status handler.
    export const ConnectionConfigSchema = z.object({
      baseURL: z.string().optional().describe("URL del servidor LM Studio (ej: http://localhost:1234/v1)"),
      apiKey: z.string().optional().describe("API Key opcional"),
    });
  • src/tools.ts:83-93 (registration)
    MCP tool registration entry defining name, description, and input schema for llm_status.
    {
      name: "llm_status",
      description: "Verifica el estado de conexión con el servidor LLM y lista los modelos disponibles",
      inputSchema: {
        type: "object" as const,
        properties: {
          ...connectionProperties,
        },
        required: [],
      },
    },
  • src/index.ts:55-56 (registration)
    Dispatch/registration of llm_status handler in the main server request handler switch statement.
    case "llm_status":
      return await toolHandlers.llm_status(args as any);
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions connection verification and model listing but fails to describe what 'estado de conexión' entails (e.g., response format, error handling), whether it's a read-only operation, or any rate limits. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 in Spanish that front-loads the core purpose without unnecessary details. Every word earns its place, making it appropriately sized and well-structured for quick comprehension.

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 moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on behavioral traits, usage context, or return values. Without annotations or output schema, it should do more to compensate, but it meets the bare minimum for a simple status-check tool.

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 already documents both parameters thoroughly. The description adds no additional meaning beyond what the schema provides about baseURL or apiKey. Baseline 3 is appropriate when the schema does the heavy lifting, though the description doesn't compensate with extra context.

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 with specific verbs ('Verifica el estado de conexión' and 'lista los modelos disponibles') and identifies the resource (servidor LLM). It distinguishes from siblings like llm_get_models by combining connection verification with model listing, though it doesn't explicitly contrast with them.

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 like llm_get_models or llm_test_capabilities. It lacks explicit when/when-not instructions or prerequisites, leaving usage context implied rather than stated.

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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