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datasets_update

Update dataset metadata including name, description, tags, and visibility settings for business analytics data from Shopify, Stripe, WooCommerce, and other sources.

Instructions

Update dataset metadata — name, description, tags, visibility.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uuidYesDataset UUID

Implementation Reference

  • The tool 'datasets_update' is not locally implemented. This proxy server forwards all tool calls (including 'datasets_update') to a remote MCP server via the `remoteClient.callTool` handler.
    server.setRequestHandler(CallToolRequestSchema, async (request) => {
      try {
        const result = await remoteClient.callTool({
          name: request.params.name,
          arguments: request.params.arguments || {},
        });
        return result;
      } catch (err) {
        return {
          content: [{ type: "text", text: `Error: ${err.message}` }],
          isError: true,
        };
      }
    });
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'update' implies mutation, it fails to clarify whether this is a partial update (PATCH) or full replacement, what permissions are required, or error conditions (e.g., invalid UUID). For a destructive-capable operation, this lack of safety context is inadequate.

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?

The description is appropriately concise at one sentence and front-loaded with the key verb. However, the high information density is undermined by the schema mismatch, making the brevity problematic rather than efficient.

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?

As a mutation tool with no output schema and zero annotations, the description should disclose success indicators, error handling, and update semantics. It provides none of these. The discrepancy between described fields and actual schema parameters further reduces completeness.

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?

Although the schema has 100% description coverage for the single 'uuid' parameter (baseline 3), the description confusingly references four additional fields (name, description, tags, visibility) that are not present in the input schema. This mismatch suggests either missing schema properties or misleading documentation, actively confusing the agent about what inputs are expected.

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 identifies the action (update) and target resource (dataset metadata) with specific field examples (name, description, tags, visibility). It distinguishes from siblings like datasets_read (read-only) and datasets_upload (create new). However, it loses a point because the fields mentioned don't appear in the provided schema, creating uncertainty about actual capabilities.

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 datasets_upload (which might create vs. update) or datasets_read. There are no prerequisites mentioned, such as needing to obtain the UUID from datasets_list first, nor warnings about mutation consequences.

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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