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web_data_tiktok_comments

Extract structured TikTok comments data from video URLs using reliable cache lookup instead of direct scraping.

Instructions

Quickly read structured Tiktok comments data. Requires a valid Tiktok video URL. This can be a cache lookup, so it can be more reliable than scraping

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

Implementation Reference

  • Defines the tool's metadata including ID, BrightData dataset_id, description, and input parameters (url). This configures the schema for web_data_tiktok_comments.
        id: 'tiktok_comments',
        dataset_id: 'gd_lkf2st302ap89utw5k',
        description: [
            'Quickly read structured Tiktok comments data.',
            'Requires a valid Tiktok video URL.',
            'This can be a cache lookup, so it can be more reliable than scraping',
        ].join('\n'),
        inputs: ['url'],
    }, {
  • server.js:683-744 (registration)
    Registers the tool named 'web_data_tiktok_comments' (via `web_data_${id}` where id='tiktok_comments') with the MCP server using addTool, including name, description, parameters schema, and execute handler.
    addTool({
        name: `web_data_${id}`,
        description,
        parameters: z.object(parameters),
        execute: tool_fn(`web_data_${id}`, async(data, ctx)=>{
            let trigger_response = await axios({
                url: 'https://api.brightdata.com/datasets/v3/trigger',
                params: {dataset_id, include_errors: true},
                method: 'POST',
                data: [data],
                headers: api_headers(),
            });
            if (!trigger_response.data?.snapshot_id)
                throw new Error('No snapshot ID returned from request');
            let snapshot_id = trigger_response.data.snapshot_id;
            console.error(`[web_data_${id}] triggered collection with `
                +`snapshot ID: ${snapshot_id}`);
            let max_attempts = 600;
            let attempts = 0;
            while (attempts < max_attempts)
            {
                try {
                    if (ctx && ctx.reportProgress)
                    {
                        await ctx.reportProgress({
                            progress: attempts,
                            total: max_attempts,
                            message: `Polling for data (attempt `
                                +`${attempts + 1}/${max_attempts})`,
                        });
                    }
                    let snapshot_response = await axios({
                        url: `https://api.brightdata.com/datasets/v3`
                            +`/snapshot/${snapshot_id}`,
                        params: {format: 'json'},
                        method: 'GET',
                        headers: api_headers(),
                    });
                    if (['running', 'building'].includes(snapshot_response.data?.status))
                    {
                        console.error(`[web_data_${id}] snapshot not ready, `
                            +`polling again (attempt `
                            +`${attempts + 1}/${max_attempts})`);
                        attempts++;
                        await new Promise(resolve=>setTimeout(resolve, 1000));
                        continue;
                    }
                    console.error(`[web_data_${id}] snapshot data received `
                        +`after ${attempts + 1} attempts`);
                    let result_data = JSON.stringify(snapshot_response.data);
                    return result_data;
                } catch(e){
                    console.error(`[web_data_${id}] polling error: `
                        +`${e.message}`);
                    attempts++;
                    await new Promise(resolve=>setTimeout(resolve, 1000));
                }
            }
            throw new Error(`Timeout after ${max_attempts} seconds waiting `
                +`for data`);
        }),
    });
  • The execute handler for the tool. Triggers a data collection job on BrightData using the specific dataset_id ('gd_lkf2st302ap89utw5k'), polls for completion (up to 600 attempts), and returns the JSON snapshot data.
    execute: tool_fn(`web_data_${id}`, async(data, ctx)=>{
        let trigger_response = await axios({
            url: 'https://api.brightdata.com/datasets/v3/trigger',
            params: {dataset_id, include_errors: true},
            method: 'POST',
            data: [data],
            headers: api_headers(),
        });
        if (!trigger_response.data?.snapshot_id)
            throw new Error('No snapshot ID returned from request');
        let snapshot_id = trigger_response.data.snapshot_id;
        console.error(`[web_data_${id}] triggered collection with `
            +`snapshot ID: ${snapshot_id}`);
        let max_attempts = 600;
        let attempts = 0;
        while (attempts < max_attempts)
        {
            try {
                if (ctx && ctx.reportProgress)
                {
                    await ctx.reportProgress({
                        progress: attempts,
                        total: max_attempts,
                        message: `Polling for data (attempt `
                            +`${attempts + 1}/${max_attempts})`,
                    });
                }
                let snapshot_response = await axios({
                    url: `https://api.brightdata.com/datasets/v3`
                        +`/snapshot/${snapshot_id}`,
                    params: {format: 'json'},
                    method: 'GET',
                    headers: api_headers(),
                });
                if (['running', 'building'].includes(snapshot_response.data?.status))
                {
                    console.error(`[web_data_${id}] snapshot not ready, `
                        +`polling again (attempt `
                        +`${attempts + 1}/${max_attempts})`);
                    attempts++;
                    await new Promise(resolve=>setTimeout(resolve, 1000));
                    continue;
                }
                console.error(`[web_data_${id}] snapshot data received `
                    +`after ${attempts + 1} attempts`);
                let result_data = JSON.stringify(snapshot_response.data);
                return result_data;
            } catch(e){
                console.error(`[web_data_${id}] polling error: `
                    +`${e.message}`);
                attempts++;
                await new Promise(resolve=>setTimeout(resolve, 1000));
            }
        }
        throw new Error(`Timeout after ${max_attempts} seconds waiting `
            +`for data`);
    }),
  • Helper function that wraps all tool execute functions, adding rate limiting, execution logging, error reporting with stack traces, timing, and statistics tracking.
    function tool_fn(name, fn){
        return async(data, ctx)=>{
            check_rate_limit();
            debug_stats.tool_calls[name] = debug_stats.tool_calls[name]||0;
            debug_stats.tool_calls[name]++;
            debug_stats.session_calls++;
            let ts = Date.now();
            console.error(`[%s] executing %s`, name, JSON.stringify(data));
            try { return await fn(data, ctx); }
            catch(e){
                if (e.response)
                {
                    console.error(`[%s] error %s %s: %s`, name, e.response.status,
                        e.response.statusText, e.response.data);
                    let message = e.response.data;
                    if (message?.length)
                        throw new Error(`HTTP ${e.response.status}: ${message}`);
                }
                else
                    console.error(`[%s] error %s`, name, e.stack);
                throw e;
            } finally {
                let dur = Date.now()-ts;
                console.error(`[%s] tool finished in %sms`, name, dur);
            }
        };
    }
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 useful context: 'Quickly' suggests performance characteristics, 'structured' describes output format, 'cache lookup' explains the data source method, and 'more reliable than scraping' compares reliability. However, it doesn't cover important aspects like rate limits, authentication needs, error conditions, or what 'structured data' specifically entails (e.g., JSON format, fields included).

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 extremely concise and well-structured: three short sentences that each add distinct value (purpose, requirement, behavioral context). It's front-loaded with the core purpose, has zero wasted words, and efficiently communicates key information without unnecessary elaboration.

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's moderate complexity (data retrieval with reliability considerations), no annotations, no output schema, and minimal parameter documentation, the description is somewhat complete but has gaps. It covers the core purpose, basic requirement, and key behavioral advantage (cache reliability), but lacks details about output format, error handling, limitations, and comprehensive parameter guidance that would be helpful for an AI agent.

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?

The input schema has 1 parameter with 0% description coverage (no schema descriptions). The description adds some semantic value: 'Requires a valid Tiktok video URL' clarifies that the 'url' parameter must be a Tiktok video URL (not just any URI). However, it doesn't specify URL format requirements (e.g., must include video ID), validation rules, or examples. Given the low schema coverage, the description provides basic but incomplete parameter guidance.

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's purpose: 'Quickly read structured Tiktok comments data' specifies the verb (read), resource (Tiktok comments data), and key characteristic (structured). It distinguishes from siblings like 'web_data_tiktok_posts' by focusing on comments, but doesn't explicitly differentiate from other comment-reading tools like 'web_data_instagram_comments' or 'web_data_youtube_comments' beyond the Tiktok platform.

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

Usage Guidelines3/5

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

The description provides some usage context: 'Requires a valid Tiktok video URL' gives a prerequisite, and 'This can be a cache lookup, so it can be more reliable than scraping' implies when to prefer this tool over scraping alternatives. However, it doesn't explicitly name when to use this vs. specific sibling tools (like 'scrape_as_html' or 'web_data_tiktok_posts'), nor does it provide clear exclusions or detailed alternative guidance.

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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