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Meilisearch MCP Server

by devlimelabs

get-experimental-features

Check the activation status of experimental features in Meilisearch to determine which advanced capabilities are available for testing.

Instructions

Get the status of experimental features in Meilisearch

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Registration of the 'get-experimental-features' MCP tool, including its description, empty input schema, and inline handler function that retrieves the experimental features status from the Meilisearch API and returns the JSON response or an error.
    server.tool(
      "get-experimental-features",
      "Get the status of experimental features in Meilisearch",
      {},
      async () => {
        try {
          const response = await apiClient.get('/experimental-features');
          return {
            content: [{ type: "text", text: JSON.stringify(response.data, null, 2) }],
          };
        } catch (error) {
          return createErrorResponse(error);
        }
      }
    );
  • The core handler function for the 'get-experimental-features' tool. It makes a GET request to '/experimental-features' using apiClient, formats the response as text content, or handles errors using createErrorResponse.
    async () => {
      try {
        const response = await apiClient.get('/experimental-features');
        return {
          content: [{ type: "text", text: JSON.stringify(response.data, null, 2) }],
        };
      } catch (error) {
        return createErrorResponse(error);
      }
    }
  • src/index.ts:68-68 (registration)
    Top-level registration call that invokes registerVectorTools on the MCP server instance, thereby registering the 'get-experimental-features' tool along with other vector tools.
    registerVectorTools(server);
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states a read operation ('Get'), implying it's likely safe and non-destructive, but doesn't mention any behavioral traits such as authentication needs, rate limits, or what the output format might be. This leaves significant gaps for a tool with zero 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 a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It's front-loaded with the key action and resource, making it easy to parse and understand quickly.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the returned status includes (e.g., feature names, enabled/disabled states) or any behavioral context like error handling. For a tool with no structured data to supplement it, more detail is needed to be fully helpful.

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

Parameters4/5

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing on the tool's purpose instead. A baseline of 4 is applied since the schema fully handles parameters, and the description doesn't add unnecessary details.

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 ('Get') and resource ('status of experimental features in Meilisearch'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get-settings' or 'info', which might also provide feature-related information, so it falls short of a perfect score.

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. With siblings like 'get-settings' or 'info' that might overlap in functionality, there's no indication of context, prerequisites, or exclusions, leaving the agent to guess based on the tool name alone.

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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