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web_data_x_posts

Extract structured X post data using a post URL to access cached information for reliable content analysis.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

Implementation Reference

  • The handler function for web_data_x_posts (shared with other web_data tools). Triggers a BrightData dataset collection using the provided URL, polls for the snapshot to complete (up to 600 seconds), and returns the JSON-stringified result 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 ${max_attempts} seconds waiting `
            +`for data`);
    }),
  • server.js:674-745 (registration)
    Registers all web_data_* tools, including web_data_x_posts, by iterating over the datasets array, constructing Zod input schemas based on inputs (e.g., url), and calling addTool with name, description, parameters, and execute handler.
    for (let {dataset_id, id, description, inputs, defaults = {}} of datasets)
    {
        let parameters = {};
        for (let input of inputs)
        {
            let param_schema = input=='url' ? z.string().url() : z.string();
            parameters[input] = defaults[input] !== undefined ?
                param_schema.default(defaults[input]) : param_schema;
        }
        addTool({
            name: `web_data_${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`);
            }),
        });
    }
  • Dataset configuration specific to x_posts, defining the dataset_id used by BrightData, tool description, and input fields (url), which determines the input schema for web_data_x_posts.
    {
        id: 'x_posts',
        dataset_id: 'gd_lwxkxvnf1cynvib9co',
        description: [
            'Quickly read structured X post data.',
            'Requires a valid X post URL.',
            'This can be a cache lookup, so it can be more reliable than scraping',
        ].join('\n'),
        inputs: ['url'],
    },
  • Utility function that wraps all tool execute functions, providing rate limiting checks, usage statistics tracking, execution logging, error handling with API response details, and timing logs.
    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?

No annotations are provided, so the description carries the full burden. It discloses that it 'can be a cache lookup' and 'more reliable than scraping,' which adds useful behavioral context about performance and reliability. However, it lacks details on error handling, rate limits, authentication needs, or what 'structured data' entails, leaving gaps for a tool with no output schema.

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 appropriately sized and front-loaded: three concise sentences with zero waste. The first sentence states the purpose, the second specifies the parameter requirement, and the third adds behavioral context, all efficiently structured.

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 (single parameter, no annotations, no output schema), the description is partially complete. It covers the purpose, parameter semantics, and some behavioral traits, but lacks details on output format, error cases, or explicit differentiation from siblings, leaving room for improvement.

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?

The description adds meaning beyond the input schema: it specifies that the 'url' parameter must be 'a valid X post URL,' clarifying the expected content. With 0% schema description coverage and only 1 parameter, this compensates well, though it doesn't detail URL format constraints beyond 'valid.'

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 X post data.' It specifies the verb ('read') and resource ('structured X post data'), making it distinct from generic scraping tools. However, it doesn't explicitly differentiate from sibling tools like 'web_data_facebook_posts' or 'web_data_linkedin_posts' beyond the 'X' platform specificity.

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 X post URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for X posts, preferring reliability) but doesn't explicitly state when not to use it or name alternatives among the many sibling scraping tools, leaving the agent to infer.

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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