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reports_search

Search for reports using job IDs, tool names, or keywords to quickly find specific analysis results.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoSearch query
job_idsNoFilter by processing IDs

Implementation Reference

  • src/index.js:57-57 (registration)
    Tool 'reports_search' is registered as a static tool definition in the STATIC_TOOLS array with its name, description, and input schema.
    { name: "reports_search", description: "Search reports by job ID, tool name, or keyword.", inputSchema: { type: "object", properties: { query: { type: "string", description: "Search query" }, job_ids: { type: "array", items: { type: "string" }, description: "Filter by processing IDs" } } } },
  • The input schema for reports_search is defined in STATIC_TOOLS: properties include 'query' (string) and 'job_ids' (array of strings). No required fields.
    // Glama inspects servers by starting them in a container without valid credentials.
    // This static catalog lets inspection detect our tools even without API access.
    
    const STATIC_TOOLS = [
      { name: "about", description: "Get platform info, pricing, usage stats, or documentation.", inputSchema: { type: "object", properties: { topic: { type: "string", description: "Topic: platform, pricing, current_usage, manual, or a docs section" } }, required: ["topic"] } },
      { name: "discover_tools", description: "Find analysis tools matching your data or question. Semantic search across 50+ statistical and ML tools.", inputSchema: { type: "object", properties: { query: { type: "string", description: "Text query describing what you want to analyze" }, dataset: { type: "string", description: "Dataset UUID to match tools against" } } } },
      { name: "tools_schema", description: "Get JSON schema for a tool — column_mapping and module_parameters required before tools_run.", inputSchema: { type: "object", properties: { tool_name: { type: "string", description: "Name of the tool" } }, required: ["tool_name"] } },
      { name: "tools_run", description: "Execute an analysis tool. Returns a shareable interactive HTML report URL.", inputSchema: { type: "object", properties: { tool_name: { type: "string", description: "Name of the tool to execute" }, taskList: { type: "object", description: "Contains inputs: dataset, userContext, column_mapping, module_parameters" } }, required: ["tool_name", "taskList"] } },
      { name: "tools_info", description: "Get detailed information about a specific analysis tool — use cases, assumptions, data requirements.", inputSchema: { type: "object", properties: { tool_name: { type: "string", description: "Name of the tool" } }, required: ["tool_name"] } },
      { name: "datasets_upload", description: "Generate a secure upload token for CSV files. Returns UUID + curl command for the user.", inputSchema: { type: "object", properties: { expires_in: { type: "integer", description: "Token expiration in seconds", default: 300 } } } },
      { name: "datasets_list", description: "List and search uploaded datasets with fuzzy matching.", inputSchema: { type: "object", properties: { search: { type: "string", description: "Search by name, description, or tags" }, limit: { type: "integer", description: "Max results", default: 20 } } } },
      { name: "datasets_read", description: "Read dataset contents — preview rows, columns, and types.", inputSchema: { type: "object", properties: { uuid: { type: "string", description: "Dataset UUID" }, secret: { type: "string", description: "Dataset secret key" }, rows: { type: "integer", description: "Number of rows to preview", default: 10 } }, required: ["uuid"] } },
      { name: "datasets_download", description: "Generate a single-use download token for securely downloading datasets.", inputSchema: { type: "object", properties: { uuid: { type: "string", description: "Dataset UUID" } }, required: ["uuid"] } },
      { name: "datasets_update", description: "Update dataset metadata — name, description, tags, visibility.", inputSchema: { type: "object", properties: { uuid: { type: "string", description: "Dataset UUID" } }, required: ["uuid"] } },
      { name: "connectors_list", description: "List available data connectors — GA4, Google Search Console, and more.", inputSchema: { type: "object", properties: {} } },
      { name: "connectors_query", description: "Pull live data from a connected source using connector:// URIs.", inputSchema: { type: "object", properties: { uri: { type: "string", description: "Connector URI (e.g., connector://mcpanalytics_gsc/search_analytics?...)" } }, required: ["uri"] } },
      { name: "reports_list", description: "List analysis reports with metadata.", inputSchema: { type: "object", properties: { limit: { type: "integer", description: "Max results", default: 10 } } } },
      { name: "reports_search", description: "Search reports by job ID, tool name, or keyword.", inputSchema: { type: "object", properties: { query: { type: "string", description: "Search query" }, job_ids: { type: "array", items: { type: "string" }, description: "Filter by processing IDs" } } } },
      { name: "reports_view", description: "View a specific report by processing ID.", inputSchema: { type: "object", properties: { processing_id: { type: "string", description: "Processing ID from tools_run" } }, required: ["processing_id"] } },
      { name: "report_cards", description: "Get individual card data from a report for rendering.", inputSchema: { type: "object", properties: { processing_id: { type: "string" } }, required: ["processing_id"] } },
      { name: "agent_advisor", description: "Conversational AI that guides analysis and interprets results.", inputSchema: { type: "object", properties: { message: { type: "string", description: "Your question or request" } }, required: ["message"] } },
      { name: "billing", description: "Check credit balance, subscription status, or open billing portal.", inputSchema: { type: "object", properties: { action: { type: "string", enum: ["status", "portal", "usage"], description: "Billing action", default: "status" } } } },
      { name: "module_request", description: "Request a custom analysis module to be built for your use case.", inputSchema: { type: "object", properties: { description: { type: "string", description: "Describe the analysis you need" } }, required: ["description"] } },
    ];
  • The tool handler is a generic proxy — all tool calls (including reports_search) are forwarded to the remote MCP server via remoteClient.callTool(). There is no local handler specific to reports_search.
    server.setRequestHandler(CallToolRequestSchema, async (request) => {
      if (!remoteClient) {
        return {
          content: [
            {
              type: "text",
              text: "MCP Analytics API key required. Set MCP_ANALYTICS_API_KEY in your environment.\nGet a free key at https://app.mcpanalytics.ai",
            },
          ],
          isError: true,
        };
      }
    
      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,
        };
      }
    });
  • The toolCatalog variable is populated from the remote server (or falls back to STATIC_TOOLS). The actual reports_search logic lives on the remote server at api.mcpanalytics.ai.
    let remoteClient = null;
    let toolCatalog = STATIC_TOOLS;
Behavior2/5

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

Without annotations, the description carries the full burden but discloses no behavioral traits. It does not state whether the tool is read-only, what happens with an empty query, or any limitations such as pagination or authentication requirements.

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, front-loaded sentence with no unnecessary words. It efficiently conveys the core functionality.

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 two optional parameters and no output schema, the description is minimally adequate but fails to mention what the search returns (e.g., list of reports or details) or behavior when no filters are applied.

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?

Schema coverage is 100%, and the description adds value by hinting that the 'query' parameter can include tool names and that 'job_ids' correspond to processing IDs. This clarifies the intended use beyond the schema descriptions.

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 searches reports by job ID, tool name, or keyword, using a specific verb and resource. While it differentiates from siblings like 'reports_list' (list all) and 'reports_view' (view specific), the mention of 'tool name' is not explicitly represented in the input schema, causing slight ambiguity.

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 'reports_list' or 'reports_view'. The description does not specify contexts, exclusions, or prerequisites.

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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