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web_data_homedepot_products

Extract structured product data from Home Depot URLs using cached lookups for reliable access to specifications, pricing, and availability.

Instructions

Quickly read structured homedepot product data. Requires a valid homedepot 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_homedepot_products' tool. Uses the dataset_id to trigger a Bright Data dataset snapshot with the provided 'url', polls the API up to 600 times for the result, 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 ${max_attempts} seconds waiting `
            +`for data`);
    }),
  • Dynamically constructs the Zod input schema for the tool. For homedepot_products (inputs=['url']), creates parameters: {url: z.string().url()}
    let parameters = {};
    for (let input of inputs)
    {
        let param_schema = input=='url' ? z.string().url() : z.string();
        parameters[input] = defaults[input] !== undefined ?
            param_schema.default(defaults[input]) : param_schema;
    }
    addTool({
        name: `web_data_${id}`,
        description,
        parameters: z.object(parameters),
  • server.js:333-341 (registration)
    Dataset configuration entry that defines the tool name 'web_data_homedepot_products' (via id), the BrightData dataset_id, description, and input fields (url). This feeds into the dynamic tool registration loop.
        id: 'homedepot_products',
        dataset_id: 'gd_lmusivh019i7g97q2n',
        description: [
            'Quickly read structured homedepot product data.',
            'Requires a valid homedepot product URL.',
            'This can be a cache lookup, so it can be more reliable than scraping',
        ].join('\n'),
        inputs: ['url'],
    }, {
  • server.js:674-745 (registration)
    The dynamic registration loop that creates and adds the 'web_data_homedepot_products' tool (and others) to the FastMCP server using addTool, deriving name, schema, description from the datasets config.
    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`);
            }),
        });
    }
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 reveals important operational traits: the cache lookup capability and reliability advantage over scraping. However, it doesn't disclose potential limitations like rate limits, authentication requirements, error conditions, or what 'structured data' specifically entails. 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 extremely efficient with three sentences that each serve distinct purposes: stating the core functionality, specifying the required input, and providing comparative advantages. There's zero wasted language, and the information is front-loaded with the primary purpose. This represents optimal conciseness for a tool with a single parameter and clear differentiation from alternatives.

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 (data extraction from a specific website), no annotations, no output schema, and 0% schema description coverage, the description provides a reasonable foundation but has gaps. It covers the basic purpose, input requirement, and reliability advantage, but doesn't explain the output format, error handling, or detailed behavioral constraints. For a data extraction tool without structured output documentation, more completeness would be beneficial.

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 input schema has 0% description coverage, so the description must compensate. It specifies that the single 'url' parameter must be 'a valid homedepot product URL,' which adds crucial semantic context beyond the schema's URI format. However, it doesn't elaborate on URL format requirements, validation rules, or example patterns. With one parameter and some added meaning, this meets the baseline for adequate parameter guidance.

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 homedepot product data' specifies the verb ('read'), resource ('homedepot product data'), and key characteristic ('structured'). It distinguishes from generic scraping tools by mentioning structured data extraction, though it doesn't explicitly differentiate from all sibling web_data_* tools. The description avoids tautology by not just restating the tool name.

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 homedepot product URL' establishes the prerequisite. It also offers comparative guidance: 'This can be a cache lookup, so it can be more reliable than scraping' suggests this tool as a preferred alternative to scraping methods. However, it doesn't explicitly name specific sibling tools to avoid or provide detailed 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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