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datasets_read

Preview dataset contents including rows, columns, and data types to understand structure before analysis.

Instructions

Read dataset contents — preview rows, columns, and types.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uuidYesDataset UUID
secretNoDataset secret key
rowsNoNumber of rows to preview

Implementation Reference

  • The tool "datasets_read" is not implemented locally. Instead, this proxy server forwards all tool calls, including "datasets_read", to a remote MCP server via the `remoteClient.callTool` method.
    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,
        };
      }
    });
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It successfully discloses the preview nature (limited rows) and return structure (rows, columns, types), but omits safety confirmations, error handling behavior, or whether the secret parameter triggers different access permissions.

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 front-loads the primary action ('Read dataset contents') and uses an em-dash to append the specific return details. No words are wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of an output schema, the description compensates well by listing what is returned (rows, columns, types). It appropriately covers the tool's scope for a simple read operation, though it could mention authentication patterns or privacy implications of the secret parameter.

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?

With 100% schema description coverage, the baseline is 3. The description adds conceptual context by framing the operation as a 'preview', which explains the intent behind the 'rows' parameter, but does not elaborate on the relationship between uuid and secret or parameter syntax beyond the schema.

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

Purpose5/5

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

The description uses specific verbs ('Read', 'preview') and identifies the resource ('dataset contents'). The term 'preview' effectively distinguishes this from sibling tools like datasets_download (full extraction) and datasets_list (metadata only), while 'Read' contrasts with datasets_update/write operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The term 'preview' implies an exploration use case (sampling data before full download), suggesting when to use this tool. However, it lacks explicit guidance such as 'Use datasets_download for full extraction' or prerequisites like when the secret key is required.

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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