Skip to main content
Glama
us-all

openmetadata-mcp-server

by us-all

list-ml-model-services

List machine learning model services with filtering by fields, pagination, and options to include deleted records.

Instructions

List ML model services

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNoComma-separated fields to include
limitNo
beforeNo
afterNo
includeNonon-deleted

Implementation Reference

  • The handler function that lists ML model services by calling the OpenMetadata API endpoint /services/mlmodelServices with pagination/field params.
    export async function listMlModelServices(params: z.infer<typeof listMlModelServicesSchema>) {
      return omClient.get("/services/mlmodelServices", params);
    }
  • The schema definition (listParams) used for list-ml-model-services; includes fields, limit, before, after, and include (non-deleted/deleted/all).
    const listParams = z.object({
      fields: z.string().optional().describe("Comma-separated fields to include"),
      limit: z.coerce.number().optional().default(10),
      before: z.string().optional(),
      after: z.string().optional(),
      include: z.enum(["non-deleted", "deleted", "all"]).optional().default("non-deleted"),
    });
  • src/index.ts:235-235 (registration)
    Registration of the 'list-ml-model-services' tool with the MCP server, binding the schema and handler.
    tool("list-ml-model-services", "List ML model services", listMlModelServicesSchema.shape, wrapToolHandler(listMlModelServices));
  • The wrapToolHandler utility that wraps tool handlers with error handling and redaction.
    export const wrapToolHandler = createWrapToolHandler({
      redactionPatterns: [/OPENMETADATA_TOKEN/i],
      errorExtractors: [
        {
          match: (error) => error instanceof WriteBlockedError,
          extract: (error) => ({
            kind: "passthrough",
            text: (error as WriteBlockedError).message,
          }),
        },
        {
          match: (error) => error instanceof OpenMetadataError,
          extract: (error) => {
Behavior2/5

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

No annotations provided. Description does not disclose pagination, ordering, or side effects. Only implies a read operation without further detail.

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

Conciseness3/5

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

Single sentence is concise but inadequate for a tool with 5 parameters and no output schema. It is front-loaded but lacks useful detail.

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 5 parameters and no output schema, description is severely incomplete. No info on return format, pagination, or filtering behavior.

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?

Schema description coverage is 20% (only 'fields' described). Description adds no param-level info. Parameters like 'before', 'after', 'include' remain unexplained.

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 'List ML model services' clearly states verb and resource, distinguishing from 'get-ml-model-service' but not from other list tools like 'list-ml-models'. It is specific enough.

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 vs alternatives (e.g., get-ml-model-service or list-ml-models). No context on filtering, pagination, or default behavior.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/us-all/openmetadata-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server