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tools_run

Execute statistical analysis tools to generate interactive HTML reports from business data sources like Shopify, Stripe, and Google Analytics.

Instructions

Execute an analysis tool. Returns a shareable interactive HTML report URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tool_nameYesName of the tool to execute
taskListYesContains inputs: dataset, userContext, column_mapping, module_parameters

Implementation Reference

  • The server.setRequestHandler(CallToolRequestSchema, ...) handler intercepts tool execution requests (including "tools_run" if that is a valid tool name in the remote catalog) and proxies them to the remote MCP server via the `remoteClient.callTool` call.
    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?

No annotations provided, so description carries full burden. It discloses the return format (shareable HTML URL) but omits critical behavioral details: whether execution creates persistent resources, consumes quotas, runs asynchronously, or modifies state.

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?

Two sentences with zero redundancy: first defines the action, second defines the return value. Front-loaded and appropriately brief for the complexity.

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?

For a tool that wraps other analysis tools with nested complex inputs, the description is minimally adequate. It mentions the output format, but lacks operational context (error handling, report lifecycle, required tool_name format) given the lack of annotations or output schema.

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 has 100% description coverage (taskList details its internal structure), so the description is not required to compensate. However, the description adds zero parameter context, so it earns the baseline score.

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?

States specific action ('Execute') and target ('analysis tool'), and distinguishes from metadata siblings (tools_info, tools_schema, discover_tools) by implying runtime execution vs. inspection. Lacks explicit differentiation statement.

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 on prerequisites (e.g., whether tool_name must be discovered via discover_tools first), no mention of when to use alternatives like module_request, and no exclusions or error conditions.

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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