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Meilisearch MCP Server

by devlimelabs

update-faceting

Modify faceting settings for a Meilisearch index to customize how search results are categorized and filtered based on attributes.

Instructions

Update the faceting setting

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
indexUidYesUnique identifier of the index
valueYesJSON value for the setting

Implementation Reference

  • The handler function for the 'update-faceting' tool. It parses the 'value' parameter as JSON and performs a PUT request to `/indexes/{indexUid}/settings/faceting` via apiClient to update the faceting settings, returning the response or an error.
    async ({ indexUid, value }) => {
      try {
        // Parse the value string to ensure it's valid JSON
        const parsedValue = JSON.parse(value);
        
        const response = await apiClient.put(`/indexes/${indexUid}/settings/${endpoint}`, parsedValue);
        return {
          content: [{ type: "text", text: JSON.stringify(response.data, null, 2) }],
        };
      } catch (error) {
        return createErrorResponse(error);
      }
    }
  • Zod input schema for the 'update-faceting' tool, requiring 'indexUid' (string) and 'value' (string containing JSON for the faceting settings).
    {
      indexUid: z.string().describe("Unique identifier of the index"),
      value: z.string().describe("JSON value for the setting"),
    },
  • Configuration object within the updateSettingsTools array that defines the name, endpoint ('faceting'), and description for the 'update-faceting' tool used in dynamic registration.
    {
      name: "update-faceting",
      endpoint: "faceting",
      description: "Update the faceting setting",
    },
  • forEach loop that dynamically registers the 'update-faceting' tool (and others) by calling server.tool() with the configuration from updateSettingsTools, including schema and handler.
    // Create an update tool for each specific setting
    updateSettingsTools.forEach(({ name, endpoint, description }) => {
      server.tool(
        name,
        description,
        {
          indexUid: z.string().describe("Unique identifier of the index"),
          value: z.string().describe("JSON value for the setting"),
        },
        async ({ indexUid, value }) => {
          try {
            // Parse the value string to ensure it's valid JSON
            const parsedValue = JSON.parse(value);
            
            const response = await apiClient.put(`/indexes/${indexUid}/settings/${endpoint}`, parsedValue);
            return {
              content: [{ type: "text", text: JSON.stringify(response.data, null, 2) }],
            };
          } catch (error) {
            return createErrorResponse(error);
          }
        }
      );
    });
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 'update' implying a mutation, but doesn't disclose behavioral traits like required permissions, whether changes are reversible, rate limits, or what happens on success/failure. For a mutation tool with zero annotation coverage, this leaves critical gaps in understanding how it behaves.

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 front-loaded with the core action and target, making it easy to parse quickly. No unnecessary words or redundancy are present.

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 a mutation tool with no annotations, no output schema, and 2 parameters, the description is incomplete. It lacks context on what faceting is, how the update affects search behavior, error handling, or response format. This leaves the agent under-informed for 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%, with clear descriptions for both parameters (indexUid as unique identifier, value as JSON value). The description adds no additional meaning beyond the schema, such as explaining what 'faceting setting' entails or the expected JSON structure. Baseline is 3 since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Update the faceting setting' clearly states the action (update) and target (faceting setting), but it's vague about what 'faceting setting' entails. It distinguishes from siblings like 'get-faceting' and 'reset-faceting' by specifying 'update', but doesn't clarify what faceting is or its scope compared to similar tools like 'update-filterable-attributes'.

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. It doesn't mention prerequisites (e.g., needing an existing index), exclusions, or when to choose this over related tools like 'update-settings' or 'reset-faceting'. The agent must infer usage from the name and context 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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