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datasets_list

List and search uploaded datasets using fuzzy matching to find specific data files by name, description, or tags.

Instructions

List and search uploaded datasets with fuzzy matching.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchNoSearch by name, description, or tags
limitNoMax results

Implementation Reference

  • The `datasets_list` tool (like all other tools) is handled by this `CallToolRequestSchema` handler, which proxies the request to a remote MCP server via `remoteClient.callTool`. The tool catalog is dynamically fetched from the remote server on startup.
    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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context about 'fuzzy matching' not present in the schema. However, it fails to disclose pagination behavior, what fields are searched (the schema clarifies name/description/tags, but the description doesn't), or the response format/structure.

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 no waste. It front-loads the core action ('List and search'), specifies the resource ('uploaded datasets'), and ends with the distinguishing mechanism ('fuzzy matching'). Every word earns its place.

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

Completeness3/5

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

Given the tool has only 2 simple parameters and no output schema, the description is minimally adequate. However, given the rich sibling ecosystem (datasets_read, datasets_update), it should clarify that this returns a catalog/list of datasets (not content) to guide the agent toward the proper tool sequence. It also omits that all parameters are optional.

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%, establishing a baseline of 3. The description adds the 'fuzzy matching' semantic for the search parameter, which helps understand the matching behavior. However, it does not elaborate on the interaction between search and limit parameters or provide examples of search syntax.

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 tool 'List[s] and search[es] uploaded datasets' with specific mechanism 'fuzzy matching'. This distinguishes it from siblings like datasets_read (content retrieval) and datasets_upload (creation). However, it could explicitly clarify that this returns metadata/catalog entries rather than dataset contents.

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 explicit guidance on when to use this tool versus alternatives like datasets_read (which likely requires a specific dataset ID obtained from this tool) or datasets_search (if it existed). It omits that both parameters are optional for full listing versus filtered search.

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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