web_data_linkedin_people_search
Extract structured LinkedIn profile data using a reliable cache lookup method to avoid web scraping issues. This tool helps find professional information by searching with first name, last name, and URL parameters.
Instructions
Quickly read structured linkedin people search data This can be a cache lookup, so it can be more reliable than scraping
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| first_name | Yes | ||
| last_name | Yes |
Implementation Reference
- server.js:687-743 (handler)The core handler logic for the 'web_data_linkedin_people_search' tool. It triggers a data collection job on BrightData using the specific dataset_id, polls for the snapshot to complete (up to 600 attempts), and returns the structured 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 ${max_attempts} seconds waiting ` +`for data`); }), - server.js:674-745 (registration)The registration loop that dynamically creates and registers all 'web_data_*' tools, including 'web_data_linkedin_people_search', using configurations from the 'datasets' array.
for (let {dataset_id, id, description, inputs, defaults = {}} of datasets) { let parameters = {}; for (let input of inputs) { let param_schema = input=='url' ? z.string().url() : z.string(); parameters[input] = defaults[input] !== undefined ? param_schema.default(defaults[input]) : param_schema; } addTool({ name: `web_data_${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`); }), }); } - server.js:401-408 (schema)Dataset configuration for 'linkedin_people_search', providing the dataset_id, description, and input fields ('url', 'first_name', 'last_name') used to generate the tool schema and parameters.
id: 'linkedin_people_search', dataset_id: 'gd_m8d03he47z8nwb5xc', description: [ 'Quickly read structured linkedin people search data', 'This can be a cache lookup, so it can be more reliable than scraping', ].join('\n'), inputs: ['url', 'first_name', 'last_name'], }, { - server.js:752-778 (helper)Helper wrapper 'tool_fn' applied to the handler, providing rate limiting, usage statistics tracking, logging, error handling, and timing for all tools including 'web_data_linkedin_people_search'.
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); } }; }