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reports_search

Search reports by job ID, tool name, or keyword to find specific analysis results within the MCP Analytics server.

Instructions

Search reports by job ID, tool name, or keyword.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoSearch query
job_idsNoFilter by processing IDs

Implementation Reference

  • The tool "reports_search" is not hardcoded in this codebase. Instead, this MCP proxy dynamically fetches available tools from a remote server (api.mcpanalytics.ai) and forwards all `tools/call` requests (including "reports_search") to that remote server using `remoteClient.callTool`.
    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 are provided, so the description carries full burden. It discloses the basic search capability but omits behavioral details like pagination, result limits, whether the search is case-sensitive, or what the return format contains.

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?

Single sentence, front-loaded with action and resource, zero waste. 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?

Adequate for a simple 2-parameter search tool with no output schema, but gaps remain. Without annotations or return type documentation, the description should ideally hint at result structure or volume, which it does not.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While the schema has 100% coverage with basic descriptions, the description adds valuable semantics by clarifying that the 'query' parameter accepts tool names and keywords, and that 'job_ids' filters by job ID—mapping abstract parameters to concrete search fields.

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 states the specific verb (search), resource (reports), and searchable fields (job ID, tool name, keyword). However, it does not explicitly differentiate from siblings like 'reports_list' or 'reports_view' to help the agent choose the correct tool.

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 provided on when to use this tool versus alternatives like 'reports_list' or 'reports_view'. There are no prerequisites, exclusions, or contextual triggers mentioned.

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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