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web_data_amazon_product

Extract structured Amazon product data from URLs with /dp/ using reliable cached lookups instead of direct scraping.

Instructions

Quickly read structured amazon product data. Requires a valid product URL with /dp/ in it. This can be a cache lookup, so it can be more reliable than scraping

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

Implementation Reference

  • Handler that triggers the BrightData dataset collection for the specific dataset_id ('gd_l7q7dkf244hwjntr0' for amazon_product), polls the snapshot status up to 600 seconds, and returns the collected 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 ${max_attempts} seconds waiting `
            +`for data`);
    }),
  • Dataset configuration defining the tool's schema (input: url), description, and dataset_id used to generate the Zod parameters schema and register the tool.
    id: 'amazon_product',
    dataset_id: 'gd_l7q7dkf244hwjntr0',
    description: [
        'Quickly read structured amazon product data.',
        'Requires a valid product URL with /dp/ in it.',
        'This can be a cache lookup, so it can be more reliable than scraping',
    ].join('\n'),
    inputs: ['url'],
  • server.js:674-745 (registration)
    Registration loop that creates and adds the web_data_amazon_product tool (and siblings) to the MCP server using dataset configs.
    for (let {dataset_id, id, description, inputs, defaults = {}} of datasets)
    {
        let parameters = {};
        for (let input of inputs)
        {
            let param_schema = input=='url' ? z.string().url() : z.string();
            parameters[input] = defaults[input] !== undefined ?
                param_schema.default(defaults[input]) : param_schema;
        }
        addTool({
            name: `web_data_${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`);
            }),
        });
    }
  • Helper wrapper applied to all tools, including web_data_amazon_product, for rate limiting, stats tracking, logging, and 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 of behavioral disclosure. It adds useful context: the cache lookup behavior ('This can be a cache lookup') and reliability comparison ('more reliable than scraping'). However, it doesn't disclose critical traits like rate limits, authentication needs, error conditions, or what 'structured data' entails in the output. For a tool with zero annotation coverage, this leaves significant gaps.

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: the first sentence states the core purpose, followed by specific requirements and behavioral context. Every sentence earns its place with no wasted words, making it highly efficient for an AI agent to parse quickly.

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 (1 parameter, no output schema, no annotations), the description is partially complete. It covers the basic purpose, parameter constraint, and cache behavior, but lacks details on output format, error handling, or performance characteristics. Without annotations or an output schema, the description should do more to compensate, especially for a data-fetching tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate. It adds meaning for the single parameter by specifying 'Requires a valid product URL with /dp/ in it,' which clarifies the URL format requirement beyond the schema's generic 'uri' format. However, it doesn't explain other semantic aspects like URL validation rules or examples, so it only partially compensates for the lack of schema descriptions.

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 amazon product data.' It specifies the verb ('read') and resource ('amazon product data'), and distinguishes it from general scraping tools by mentioning structured data and cache lookup. However, it doesn't explicitly differentiate from its direct sibling 'web_data_amazon_product_reviews' or 'web_data_amazon_product_search', which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: 'Requires a valid product URL with /dp/ in it.' It implies an alternative by stating 'it can be more reliable than scraping,' suggesting it's preferable to scraping tools when possible. However, it doesn't explicitly name when NOT to use it or specify exact alternatives among the many sibling scraping tools, which limits the score.

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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