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mcp_ollama_show

Display detailed information about a specific Ollama model by specifying its name, enabling users to retrieve metadata for better integration and utilization within the Ontology MCP server.

Instructions

Ollama 모델의 정보를 표시합니다

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes정보를 조회할 모델 이름

Implementation Reference

  • MCP tool handler function that calls ollamaService.showModel(args) and formats the response as ToolResponse.
    async handler(args: any): Promise<ToolResponse> {
      const result = await ollamaService.showModel(args);
      return {
        content: [
          {
            type: 'text' as const,
            text: result
          }
        ]
      };
    }
  • Input schema for the mcp_ollama_show tool, requiring a 'name' parameter.
    inputSchema: {
      type: 'object',
      properties: {
        name: {
          type: 'string',
          description: '정보를 조회할 모델 이름'
        }
      },
      required: ['name']
    },
  • Core implementation in OllamaService: makes GET request to Ollama /api/show endpoint and returns JSON string of model info.
    async showModel(args: { name: string }): Promise<string> {
      try {
        const response = await axios.get(
          this.getApiUrl(`show?name=${encodeURIComponent(args.name)}`)
        );
        return JSON.stringify(response.data, null, 2);
      } catch (error) {
        if (axios.isAxiosError(error)) {
          throw new McpError(
            ErrorCode.InternalError,
            `Ollama API 오류: ${error.response?.data?.error || error.message}`
          );
        }
        throw new McpError(ErrorCode.InternalError, `모델 정보를 가져오는데 실패했습니다: ${formatError(error)}`);
      }
    }
  • src/index.ts:25-54 (registration)
    MCP server capabilities registration declaring mcp_ollama_show: true
    tools: {
      mcp_sparql_execute_query: true,
      mcp_sparql_update: true,
      mcp_sparql_list_repositories: true,
      mcp_sparql_list_graphs: true,
      mcp_sparql_get_resource_info: true,
      mcp_ollama_run: true,
      mcp_ollama_show: true,
      mcp_ollama_pull: true,
      mcp_ollama_list: true,
      mcp_ollama_rm: true,
      mcp_ollama_chat_completion: true,
      mcp_ollama_status: true,
      mcp_http_request: true,
      mcp_openai_chat: true,
      mcp_openai_image: true,
      mcp_openai_tts: true,
      mcp_openai_transcribe: true,
      mcp_openai_embedding: true,
      mcp_gemini_generate_text: true,
      mcp_gemini_chat_completion: true,
      mcp_gemini_list_models: true,
      mcp_gemini_generate_images: false,
      mcp_gemini_generate_image: false,
      mcp_gemini_generate_videos: false,
      mcp_gemini_generate_multimodal_content: false,
      mcp_imagen_generate: false,
      mcp_gemini_create_image: false,
      mcp_gemini_edit_image: false
    },
  • src/index.ts:62-68 (registration)
    Handler for ListToolsRequestSchema that exposes all tools including mcp_ollama_show.
    server.setRequestHandler(ListToolsRequestSchema, async () => ({
      tools: tools.map(tool => ({
        name: tool.name,
        description: tool.description,
        inputSchema: tool.inputSchema
      }))
    }));
Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states what the tool does but doesn't disclose whether this is a read-only operation, what permissions are needed, what format the information is returned in, or any rate limits. The description is too basic for a tool with no annotation coverage.

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 extremely concise - a single Korean sentence that directly states the tool's purpose. There's zero waste or unnecessary elaboration, making it efficiently front-loaded and easy to parse.

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?

For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what information is returned about the model, the response format, error conditions, or how this differs from related tools. The agent would need to guess about the tool's behavior and output.

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% with the single parameter 'name' well-documented in the schema. The description doesn't add any additional parameter semantics beyond what's already in the schema ('정보를 조회할 모델 이름' - model name to retrieve information). Baseline score of 3 is appropriate when the schema does the heavy lifting.

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 verb ('표시합니다' - displays/shows) and resource ('Ollama 모델의 정보' - information of Ollama model), providing a specific purpose. However, it doesn't differentiate from sibling tools like 'mcp_ollama_list' or 'mcp_ollama_status', which might have overlapping functionality for model information retrieval.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context for usage, or how it differs from sibling tools like 'mcp_ollama_list' (which might list models) or 'mcp_ollama_status' (which might show system status).

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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