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mcp_ollama_rm

Remove Ollama models by name within the Ontology MCP server to manage and optimize AI model storage and performance for ontology data interactions.

Instructions

Ollama 모델을 삭제합니다

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes삭제할 모델 이름

Implementation Reference

  • Core handler implementation that sends DELETE request to Ollama API /api/delete to remove the specified model.
    async removeModel(args: { name: string }): Promise<string> {
      try {
        const response = await axios.delete(
          this.getApiUrl('delete'),
          {
            data: { name: 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 삭제에 실패했습니다: ${formatError(error)}`);
      }
    }
  • Tool registration including name, description, input schema, and thin wrapper handler that delegates to ollamaService.removeModel.
    {
      name: 'mcp_ollama_rm',
      description: 'Ollama 모델을 삭제합니다',
      inputSchema: {
        type: 'object',
        properties: {
          name: {
            type: 'string',
            description: '삭제할 모델 이름'
          }
        },
        required: ['name']
      },
      async handler(args: any): Promise<ToolResponse> {
        const result = await ollamaService.removeModel(args);
        return {
          content: [
            {
              type: 'text' as const,
              text: result
            }
          ]
        };
      }
  • src/index.ts:35-35 (registration)
    MCP server capabilities declaration enabling the mcp_ollama_rm tool.
    mcp_ollama_rm: true,
  • Input schema defining the required 'name' parameter for the tool.
    inputSchema: {
      type: 'object',
      properties: {
        name: {
          type: 'string',
          description: '삭제할 모델 이름'
        }
      },
      required: ['name']
    },
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states the tool deletes a model but lacks critical behavioral details: whether deletion is permanent, requires specific permissions, affects other resources, or has side effects (e.g., freeing disk space). For a destructive operation with zero annotation coverage, this is a significant gap in transparency.

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, clear sentence in Korean that directly states the tool's purpose without unnecessary words. It is front-loaded and efficiently conveys the core action, making it easy to understand at a glance. No extraneous information is included.

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's destructive nature, lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permanence or permissions, return values (e.g., success confirmation or error details), or error handling. For a delete operation, more context is needed to ensure safe and correct usage.

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 parameter 'name' documented as '삭제할 모델 이름' (name of the model to delete). The description adds no additional meaning beyond this, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema adequately defines the parameter.

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 'Ollama 모델을 삭제합니다' (Deletes an Ollama model) clearly states the action (삭제합니다/delete) and resource (Ollama 모델/Ollama model). It distinguishes from siblings like mcp_ollama_list, mcp_ollama_pull, and mcp_ollama_run, which perform different operations. However, it doesn't specify if it's for local or remote models, which could enhance differentiation.

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 (e.g., needing an existing model), exclusions, or comparisons to siblings like mcp_ollama_list (to check available models) or mcp_ollama_pull (to add models). Usage is implied only by the verb 'delete'.

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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