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web_data_facebook_posts

Extract structured Facebook post data from URLs using cached lookups for reliable access to post information without direct scraping.

Instructions

Quickly read structured Facebook post data. Requires a valid Facebook 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 core handler function for the web_data_facebook_posts tool. It triggers a BrightData dataset collection using the specific dataset_id, polls the snapshot status every second up to 600 attempts, and returns the JSON data when ready.
    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:467-476 (registration)
    Dataset configuration registration for the 'facebook_posts' dataset. This object is used in the loop to register the 'web_data_facebook_posts' tool with its name, description, schema inputs, and dataset_id.
    {
        id: 'facebook_posts',
        dataset_id: 'gd_lyclm1571iy3mv57zw',
        description: [
            'Quickly read structured Facebook post data.',
            'Requires a valid Facebook post URL.',
            'This can be a cache lookup, so it can be more reliable than scraping',
        ].join('\n'),
        inputs: ['url'],
    },
  • Dynamic generation of the Zod input schema object based on the 'inputs' array from the dataset configuration. For 'facebook_posts', it creates {url: z.string().url()}.
    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;
    }
  • Helper wrapper function 'tool_fn' that wraps all tool execute functions, providing rate limiting, usage statistics tracking, logging, timing, and enhanced error handling.
    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. It discloses that the tool reads data (implied non-destructive), mentions reliability via cache lookup, and hints at performance ('quickly'). However, it lacks details on rate limits, authentication needs, error handling, or what 'structured data' entails. The disclosure is partial but adds some behavioral context beyond the basic purpose.

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 front-loaded with the core purpose, followed by key requirements and a reliability note. All three sentences earn their place by adding distinct value: purpose, input requirement, and behavioral insight. It's efficient with zero wasted words.

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 (reading web data), no annotations, no output schema, and low schema coverage, the description is minimally adequate. It covers the purpose and parameter semantics but lacks details on output format, error cases, or deeper behavioral traits. It meets basic needs but leaves gaps for an agent to use it effectively in varied contexts.

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 meaningful context for the single parameter: 'Requires a valid Facebook post URL.' This clarifies the 'url' parameter's purpose and format beyond the schema's basic type/format. With 0% schema description coverage and only one parameter, this compensates adequately, though it could specify URL examples or constraints.

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 Facebook post data.' It specifies the verb ('read'), resource ('Facebook post data'), and key characteristic ('structured'). However, it doesn't explicitly differentiate from siblings like 'web_data_facebook_events' or 'web_data_facebook_marketplace_listings' beyond the 'post' focus.

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 Facebook post URL' and mentions it 'can be more reliable than scraping,' which implies an alternative to scraping tools. However, it doesn't explicitly state when to use this versus siblings like 'scrape_as_html' or 'extract,' nor does it provide clear exclusions or prerequisites beyond the URL requirement.

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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