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openmetadata-mcp-server

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get-ml-model-service

Retrieve an ML model service by its fully qualified name. Access details, fields, and inclusion status of the service.

Instructions

Get ML model service by name

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fqnYesService fully qualified name
fieldsNo
includeNo

Implementation Reference

  • Handler function for get-ml-model-service: fetches an ML model service by its fully qualified name (fqn) from OpenMetadata API endpoint /services/mlmodelServices/name/{fqn}
    export async function getMlModelService(params: z.infer<typeof getMlModelServiceSchema>) {
      const { fqn, ...query } = params;
      return omClient.get(`/services/mlmodelServices/name/${encodeURIComponent(fqn)}`, query);
    }
  • Schema (getByNameParams) reused for getMlModelServiceSchema: defines input params with required fqn (fully qualified name) string, optional fields and include filters
    const getByNameParams = z.object({
      fqn: z.string().describe("Service fully qualified name"),
      fields: z.string().optional(),
      include: z.enum(["non-deleted", "deleted", "all"]).optional(),
    });
  • src/index.ts:236-236 (registration)
    Registration of the 'get-ml-model-service' tool with MCP server using schema shape and wrapped handler
    tool("get-ml-model-service", "Get ML model service by name", getMlModelServiceSchema.shape, wrapToolHandler(getMlModelService));
  • src/index.ts:45-47 (registration)
    Import of getMlModelServiceSchema and getMlModelService from ./tools/services.js into the main index.ts
      listMlModelServicesSchema, listMlModelServices, getMlModelServiceSchema, getMlModelService,
      listStorageServicesSchema, listStorageServices, getStorageServiceSchema, getStorageService,
    } from "./tools/services.js";
  • Schema alias: getMlModelServiceSchema is set to getByNameParams, which is defined on lines 19-23
    export const getMlModelServiceSchema = getByNameParams;
    export async function getMlModelService(params: z.infer<typeof getMlModelServiceSchema>) {
      const { fqn, ...query } = params;
      return omClient.get(`/services/mlmodelServices/name/${encodeURIComponent(fqn)}`, query);
    }
Behavior1/5

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

No annotations present, so description bears full responsibility. It fails to disclose any behavioral traits such as what happens if the service is not found, whether the response includes full details, or any side effects. Lacks essential context for a get operation.

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

Conciseness2/5

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

Extremely concise but at the cost of informativeness. A single sentence provides minimal value. It would be better to include more detail without being verbose, but current brevity reduces utility.

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 is a simple getter, the description is too sparse. It does not explain the response format, how to filter using 'include', or the purpose of 'fields'. With no output schema and many sibling tools, more context is needed for correct invocation.

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

Parameters2/5

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

Input schema has three parameters but only 'fqn' has a description. The description adds no additional meaning to 'fields' or 'include'. With schema coverage at 33% and no param elaboration, the agent lacks understanding of optional parameters.

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?

Description clearly states the action (Get) and resource (ML model service) with qualifier (by name). Distinguishes from siblings like 'get-ml-model' or 'get-ml-model-by-name' which target different entities. However, it could be more specific about what an ML model service entails.

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 on when to use this tool versus alternatives, no prerequisites, and no exclusion criteria. The description does not indicate whether this tool is preferred over other getter tools or under what conditions.

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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