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web_data_etsy_products

Extract structured product data from Etsy URLs using cached web data retrieval for reliable access to product information.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

Implementation Reference

  • Core handler logic for the web_data_etsy_products tool. Triggers a BrightData dataset snapshot using the etsy_products dataset_id, polls the snapshot status up to 600 times (1s intervals), and returns the JSON 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 ${max_attempts} seconds waiting `
            +`for data`);
    }),
  • Dataset configuration providing the id ('etsy_products'), dataset_id, description, and inputs schema definition (url parameter) used to construct the web_data_etsy_products tool schema.
        id: 'etsy_products',
        dataset_id: 'gd_ltppk0jdv1jqz25mz',
        description: [
            'Quickly read structured etsy product data.',
            'Requires a valid etsy product URL.',
            'This can be a cache lookup, so it can be more reliable than scraping',
        ].join('\n'),
        inputs: ['url'],
    }, {
  • server.js:683-744 (registration)
    Registration of the web_data_etsy_products tool within the loop over datasets. Sets tool name to 'web_data_etsy_products', uses description and parameters from dataset config, and assigns the shared handler wrapped by tool_fn.
    addTool({
        name: `web_data_${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`);
        }),
    });
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 it's a read operation ('read'), requires a valid URL, and mentions caching behavior ('cache lookup') and reliability advantage over scraping. However, it lacks details on rate limits, error handling, authentication needs, or what 'structured data' specifically includes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is brief and front-loaded with the core purpose. All three sentences add value: stating the action, parameter requirement, and behavioral trait. It avoids redundancy but could be slightly more structured (e.g., separating requirements from benefits).

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, no output schema, and low schema coverage, the description provides basic purpose and parameter guidance but lacks details on output format, error cases, or performance characteristics. It's minimally viable for a simple read tool but leaves gaps in behavioral context.

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 schema has 1 parameter with 0% description coverage. The description adds meaningful context: 'Requires a valid etsy product URL.' This clarifies the 'url' parameter's purpose and format beyond the schema's basic 'uri' format. Since there's only one parameter, the baseline is high, and the description adequately compensates for the low schema coverage.

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 etsy product data.' It specifies the verb ('read'), resource ('etsy product data'), and key characteristic ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_ebay_product' or 'scrape_as_html' beyond mentioning 'more reliable than scraping'.

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 etsy product URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for Etsy products, preferring reliability) but doesn't explicitly state when to choose alternatives like 'scrape_as_html' or other web_data_* tools for different sites.

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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