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web_data_facebook_events

Extract structured Facebook events data using event URLs to access reliable information for analysis or integration.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

Implementation Reference

  • server.js:497-505 (registration)
    Dataset configuration defining the ID 'facebook_events', dataset_id, description, and inputs ['url'] used to register and parameterize the 'web_data_facebook_events' tool.
        id: 'facebook_events',
        dataset_id: 'gd_m14sd0to1jz48ppm51',
        description: [
            'Quickly read structured Facebook events data.',
            'Requires a valid Facebook event URL.',
            'This can be a cache lookup, so it can be more reliable than scraping',
        ].join('\n'),
        inputs: ['url'],
    }, {
  • Dynamically builds the Zod input schema for the tool based on dataset 'inputs' field. For 'facebook_events', creates parameters: { 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;
    }
    addTool({
        name: `web_data_${id}`,
        description,
        parameters: z.object(parameters),
  • The core handler logic that triggers the BrightData dataset collection using the specific dataset_id, polls for the snapshot to complete, and returns the JSON data.
        execute: tool_fn(`web_data_${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 wrapper applied to all tool handlers, providing rate limiting, usage statistics tracking, logging, 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 full burden. It discloses key behavioral traits: the cache lookup mechanism and reliability advantage over scraping. However, it doesn't mention rate limits, authentication requirements, error conditions, or what 'structured data' specifically includes. The description adds value but leaves significant behavioral aspects unspecified.

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 perfectly concise: three short sentences with zero wasted words. Each sentence adds distinct value (purpose, parameter requirement, behavioral advantage). It's front-loaded with the core purpose and appropriately sized for a single-parameter tool.

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 no annotations, 0% schema description coverage, and no output schema, the description provides adequate basics but has gaps. It covers purpose, parameter semantics, and a key behavioral advantage. However, for a data retrieval tool, it doesn't describe the return format, error handling, or data structure details. The context is minimally complete but could be more comprehensive.

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 description coverage is 0% (no parameter descriptions in schema), but the tool has only 1 parameter. The description adds crucial semantics: 'Requires a valid Facebook event URL' clarifies that the 'url' parameter must be a Facebook event URL specifically, not just any URI. This compensates well for the schema's lack of description, though it could specify URL format expectations more precisely.

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 events data' specifies the verb ('read'), resource ('Facebook events data'), and key characteristic ('structured'). It distinguishes from generic scraping tools by mentioning structured data extraction. However, it doesn't explicitly differentiate from all sibling web_data_* tools beyond the Facebook 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 event 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. other Facebook-related tools or general scraping siblings, nor does it provide exclusion criteria.

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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