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competlab

competlab-mcp-server

get_content_history

Retrieve paginated history of content monitoring runs with completion timestamps to compare competitor sitemap snapshots over time. Use runId to access detailed run data for specific analyses.

Instructions

Get paginated history of Content Intelligence monitoring runs with completion timestamps. Use this to compare content snapshots over time — each run captures sitemap analysis for all competitors. Retrieve specific run data with get_content_run_detail using the runId from this response. Read-only. Returns paginated JSON array with pagination.hasMore flag.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectIdYesProject ID (from list_projects)
pageNoPage number (1-indexed, default: 1)
limitNoItems per page (default: 20, max: 100)

Implementation Reference

  • Tool definition (schema) for 'get_content_history': defines name, description, parameters (projectId + pagination), API path (/v1/projects/{projectId}/content/history), and query params.
      name: "get_content_history",
      description:
        "Get paginated history of Content Intelligence monitoring runs with completion timestamps. Use this to compare content snapshots over time — each run captures sitemap analysis for all competitors. Retrieve specific run data with get_content_run_detail using the runId from this response. Read-only. Returns paginated JSON array with pagination.hasMore flag.",
      parameters: z.object({
        projectId: objectId("Project ID (from list_projects)"),
        ...pagination,
      }),
      path: (a) => `/v1/projects/${a.projectId}/content/history`,
      queryParams: ["page", "limit"],
    },
  • src/index.ts:16-25 (registration)
    Generic tool registration loop in index.ts — iterates all tools from tools.ts (including get_content_history) and registers them with the MCP server using server.tool(). The handler is a generic async function that calls apiGet() with the tool's path and queryParams.
    for (const tool of tools) {
      server.tool(tool.name, tool.description, tool.parameters.shape, async (args: Record<string, any>) => {
        const path = tool.path(args);
        const query: Record<string, any> = {};
        for (const key of tool.queryParams ?? []) {
          if (args[key] !== undefined) query[key] = args[key];
        }
        return apiGet(path, Object.keys(query).length ? query : undefined);
      });
    }
  • Handler logic for get_content_history (and all tools): calls tool.path(args) to build the API URL, extracts queryParams from args, and invokes apiGet() to make the HTTP request.
    server.tool(tool.name, tool.description, tool.parameters.shape, async (args: Record<string, any>) => {
      const path = tool.path(args);
      const query: Record<string, any> = {};
      for (const key of tool.queryParams ?? []) {
        if (args[key] !== undefined) query[key] = args[key];
      }
      return apiGet(path, Object.keys(query).length ? query : undefined);
    });
  • apiGet helper function: constructs a URL to api.competlab.com, reads COMPETLAB_API_KEY from env, makes a fetch call with the CL-API-Key header, and returns the response as MCP content.
    export async function apiGet(
      path: string,
      query?: Record<string, string | number>,
    ): Promise<{ content: Array<{ type: "text"; text: string }>; isError?: true }> {
      const apiKey = process.env.COMPETLAB_API_KEY;
      if (!apiKey) {
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify({
                error: "api_key_missing",
                message: "COMPETLAB_API_KEY environment variable is not set",
              }),
            },
          ],
          isError: true,
        };
      }
    
      const url = new URL(`${API_BASE}${path}`);
      if (query) {
        for (const [k, v] of Object.entries(query)) {
          if (v !== undefined) url.searchParams.set(k, String(v));
        }
      }
    
      try {
        const res = await fetch(url, {
          headers: { "CL-API-Key": apiKey },
        });
    
        const body = await res.text();
    
        if (!res.ok) {
          return { content: [{ type: "text", text: body }], isError: true };
        }
    
        return { content: [{ type: "text", text: body }] };
      } catch (err) {
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify({
                error: "api_unreachable",
                message:
                  err instanceof Error ? err.message : "Failed to reach CompetLab API",
                status: 503,
              }),
            },
          ],
          isError: true,
        };
      }
    }
Behavior4/5

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 explicitly states 'Read-only' and describes the return format: 'Returns paginated JSON array with pagination.hasMore flag.' This sufficiently discloses the operation's safety and output 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?

Three sentences, each adding value: purpose, usage context with sibling reference, and behavior/return format. No wasted words; front-loaded with the core function.

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 no output schema and no annotations, the description adequately covers purpose, pagination, and how to use results. It could benefit from listing key output fields, but the pagination hint partially compensates.

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 coverage is 100% (all parameters have descriptions). The description adds minimal context beyond the schema, e.g., 'projectId (from list_projects)' and hints about runId in the output. Baseline is 3 because schema already documents parameters well.

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 clearly states 'Get paginated history of Content Intelligence monitoring runs with completion timestamps.' It uses a specific verb (Get) and resource (history of monitoring runs), distinguishing it from sibling tools like get_content_run_detail which retrieves a specific run.

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

Usage Guidelines4/5

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

The description advises when to use this tool: 'Use this to compare content snapshots over time' and directs to retrieve specific run data with get_content_run_detail. It provides clear context and an alternative, though it doesn't explicitly state when not to use it.

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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