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OrionPotter

Meilisearch MCP Server

by OrionPotter

update-embedders

Configure embedders for vector search in Meilisearch indexes to enable semantic search capabilities.

Instructions

Configure embedders for vector search

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
indexUidYesUnique identifier of the index
embeddersYesJSON object containing embedder configurations

Implementation Reference

  • The handler function for the 'update-embedders' tool. It parses the embedders JSON string, validates it's an object, and sends a PATCH request to update the embedders configuration for the specified index using apiClient.
    async ({ indexUid, embedders }) => {
      try {
        // Parse the embedders string to ensure it's valid JSON
        const parsedEmbedders = JSON.parse(embedders);
        
        // Ensure embedders is an object
        if (typeof parsedEmbedders !== 'object' || parsedEmbedders === null || Array.isArray(parsedEmbedders)) {
          return {
            isError: true,
            content: [{ type: "text", text: "Embedders must be a JSON object" }],
          };
        }
        
        const response = await apiClient.patch(`/indexes/${indexUid}/settings/embedders`, parsedEmbedders);
        return {
          content: [{ type: "text", text: JSON.stringify(response.data, null, 2) }],
        };
      } catch (error) {
        return createErrorResponse(error);
      }
    }
  • Zod schema defining the input parameters for the 'update-embedders' tool: indexUid (string) and embedders (string representing JSON object).
    {
      indexUid: z.string().describe("Unique identifier of the index"),
      embedders: z.string().describe("JSON object containing embedder configurations"),
    },
  • Direct registration of the 'update-embedders' tool using server.tool(), including name, description, schema, and handler.
    server.tool(
      "update-embedders",
      "Configure embedders for vector search",
      {
        indexUid: z.string().describe("Unique identifier of the index"),
        embedders: z.string().describe("JSON object containing embedder configurations"),
      },
      async ({ indexUid, embedders }) => {
        try {
          // Parse the embedders string to ensure it's valid JSON
          const parsedEmbedders = JSON.parse(embedders);
          
          // Ensure embedders is an object
          if (typeof parsedEmbedders !== 'object' || parsedEmbedders === null || Array.isArray(parsedEmbedders)) {
            return {
              isError: true,
              content: [{ type: "text", text: "Embedders must be a JSON object" }],
            };
          }
          
          const response = await apiClient.patch(`/indexes/${indexUid}/settings/embedders`, parsedEmbedders);
          return {
            content: [{ type: "text", text: JSON.stringify(response.data, null, 2) }],
          };
        } catch (error) {
          return createErrorResponse(error);
        }
      }
    );
  • src/index.ts:68-68 (registration)
    Calls registerVectorTools(server) which registers the 'update-embedders' tool among 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 full burden. It mentions 'configure' which implies a mutation, but doesn't disclose behavioral traits like whether this requires specific permissions, if changes are reversible, what happens to existing configurations, or if it's idempotent. For a mutation tool with zero annotation coverage, this is a significant gap.

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 with zero waste. It's appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration.

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 this is a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'configure' involves, potential side effects, or return values. For a tool that modifies embedders, more context is needed to guide safe and effective use.

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%, so the schema already documents both parameters ('indexUid' and 'embedders'). The description doesn't add meaning beyond what the schema provides, such as explaining what 'embedders' configurations entail or providing examples. Baseline 3 is appropriate when 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 action ('Configure') and target resource ('embedders for vector search'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'reset-embedders' or 'get-embedders', which would require more specific language about what 'configure' entails versus 'reset' or 'get'.

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 like 'reset-embedders' or 'enable-vector-search'. The description lacks context about prerequisites, such as whether vector search must be enabled first, or when configuration is needed versus resetting to defaults.

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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