Particl MCP logo

Integrate Particl MCP with your AI CRM

Particl MCP gives agents read-only access to authorized market intelligence covering companies, products, pricing, sales estimates, and marketing activity.

Automate your first taskSign up with Google

Explore Triggers and Actions

Get company details

Get detailed information about a specific company. Args: company_id: The Particl company ID (e.g., '1234567') analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 credit per call (not charged if no data is returned). Returns: Company profile including name, domain, country, vertical, start date, product count, whether product-level data exists (has_product_data), and top product categories. The has_product_data field indicates whether the company has product-level data. When has_sales_data is false, sales fields are not populated for this company. When has_pricing_data is false, pricing fields are not populated.

ActionTry it

Get company events

Get promotional events for a company — product launches, sales, restocks, price changes, and discounts — with associated marketing assets and products. Supports pagination. Use search_companies first to find the company_id. Args: company_id: The Particl company ID (e.g., '1234567') - required start_date: Start of date range (ISO format, e.g., '2025-01-01'). Defaults to ~30 days ago. end_date: End of date range (ISO format, e.g., '2025-01-31'). Defaults to ~2 days ago. event_types: Filter by event type(s). Common values: 'Promotion', 'Product Release', 'Special Event', 'Collaboration', 'Restock'. Many other values exist — call without this filter first to discover available types for a company. channels: Filter by marketing channel(s). Options: 'email', 'instagram_post', 'facebook_ads', 'sms', 'homepage_screenshot' page_size: Results per page (default 25, max 100). Set higher to get more results per call. page: Page number (0-indexed, default 0). Increment to get additional pages of results. analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 per row returned (e.g., page_size=10 costs up to 10 credits). Returns: List of promotional events with associated marketing assets and products. Includes has_more and total_count for pagination.

ActionTry it

Get company marketing assets

List a company's marketing assets — emails, Instagram posts, Facebook/Meta ads, SMS messages, and homepage screenshots. Supports pagination. Use search_companies first to find the company_id. Args: company_id: The Particl company ID (e.g., '1234567') - required asset_types: Filter by asset type(s). Options: 'email', 'instagram_post', 'facebook_ads', 'sms', 'homepage_screenshot'. Defaults to all types. start_date: Start of date range (ISO format, e.g., '2025-01-01'). Defaults to ~30 days ago. end_date: End of date range (ISO format, e.g., '2025-01-31'). Defaults to ~2 days ago. page_size: Results per page (default 25, max 100). Set to 100 to get up to 100 results in one call. page: Page number (0-indexed, default 0). Increment to get additional pages of results. analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 per row returned (e.g., page_size=25 costs up to 25 credits). Returns: List of marketing assets with content, engagement metrics, and metadata. Includes has_more and total_count for pagination. Assets include product_ids when available — use these with get_company_products to look up sales and revenue data for the featured products.

ActionTry it

Get company marketing stats

Get aggregated marketing engagement statistics for a company — posting frequency by type, average likes, engagement rate, most liked post, and posting hour distribution. Use search_companies first to find the company_id. Args: company_id: The Particl company ID (e.g., '1234567') - required analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Returns: total_assets: total number of marketing assets in the last 30 days assets_by_type: dict of asset counts keyed by type (e.g. {"instagram_post": 42, "email": 10}) engagement_metrics: dict of {avg_likes, avg_comments} keyed by type posting_hours: dict of hour-frequency lists keyed by type estimated_reach: dict of average follower counts keyed by type (e.g. {"instagram_post": 50000.0}) avg_likes: dict of average likes keyed by type (e.g. {"instagram_post": 1200.5}) engagement_rate: dict of engagement rate percentages keyed by type (e.g. {"instagram_post": 4.2}) most_liked_post: dict of most liked post details keyed by type Credits: 1 credit per call (not charged if no data is returned).

ActionTry it

Get company products

Deep-dive into a specific company's product catalog. Supports pagination — use page and page_size to retrieve more than the default 25 results. Use search_companies first to find the company_id. Args: company_id: The Particl company ID (e.g., '1234567') - required. Use search_companies to find this. product_type_id: Product type UUID from get_product_types. Pass the product_type_id value, not the name. keyword: Filter by keyword/tag (e.g., 'wool', 'recycled'). Searches product tags only, NOT product titles. Use title_search to search by title. title_search: Search products by title (e.g., 'tank top', 'winter jacket'). Matches against product display title. brand: Filter by brand name min_price: Minimum price filter max_price: Maximum price filter sort_by: Sort by 'sales_revenue' (default), 'sales_volume', 'price', or 'launch_date' sort_direction: 'desc' (default) or 'asc' page_size: Results per page (default 25, max 100). Set to 100 to get up to 100 results in one call. page: Page number (0-indexed, default 0). Increment to get additional pages of results. start_date: Start of sales data aggregation window (ISO format, e.g., '2025-01-01'). Defaults to ~30 days ago. end_date: End of sales data aggregation window (ISO format, e.g., '2025-01-31'). Defaults to today. confidence_threshold: Minimum data confidence score (0-10) for included products. Defaults to 6.0, which filters out products with unreliable sales estimates (e.g., gap-filled or anomalous data). You usually don't need to change this. Lower it (e.g., to 0) only if you're looking for a specific product that isn't appearing in results — it may be hidden due to lower data confidence. Set to 0 to include all products regardless of data quality. analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 per row returned (e.g., page_size=25 costs up to 25 credits). Returns: List of products from the specified company with pricing, sales estimates, and metadata. Includes has_more and total_count for pagination.

ActionTry it

Get credit balance

Check your remaining export credits. Credits: Free — no credits charged. Returns: Credit balance including total limit, used, and remaining credits

ActionTry it

Get market pricing analysis

Get pricing analysis for a market or category. Args: product_type_id: Product type UUID from get_product_types. Pass the product_type_id value, not the name. keyword: Filter by keyword/tag end_date: End of trailing 30-day analysis window (ISO format, e.g., '2025-01-15'). Defaults to ~2 days ago. analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 credit per call (not charged if no data is returned). Returns: Pricing summary with average, median, range, and distribution buckets

ActionTry it

Get market sales

Get aggregated market-level sales data with monthly timeseries. Returns total market revenue and volume, plus monthly timeseries showing how the market is trending over time. Use with get_market_top_companies and get_market_top_products for complete market analysis. Args: product_type_id: Product type UUID from get_product_types. Pass the product_type_id value, not the name. keyword: Filter by keyword/tag (e.g., 'sustainable', 'organic') end_date: End of trailing analysis window (ISO format, e.g., '2025-01-15'). Defaults to ~2 days ago. analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 credit per call (not charged if no data is returned). Returns: Market sales summary with total revenue/volume and monthly timeseries

ActionTry it

Get market top companies

Get top-performing companies in a market or category. Supports pagination — use page and page_size to retrieve more than the default 25 results. Args: product_type_id: Product type UUID from get_product_types. Pass the product_type_id value, not the name. keyword: Filter by keyword/tag (e.g., 'luxury', 'athleisure') end_date: End of trailing 30-day analysis window (ISO format, e.g., '2025-01-15'). Defaults to ~2 days ago. page: Page number (0-indexed, default 0). Use with page_size to paginate through results. page_size: Results per page (default 25, max 100). Set to 100 to get up to 100 results in one call. analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 per row returned (e.g., page_size=25 costs up to 25 credits). Returns: List of top companies with revenue and sales volume estimates. Includes has_more and total_count for pagination.

ActionTry it

Get market top products

Get best-selling products in a market or category. Supports pagination — use page and page_size to retrieve more than the default 25 results. Args: product_type_id: Product type UUID from get_product_types. Pass the product_type_id value, not the name. keyword: Filter by keyword/tag (e.g., 'sustainable', 'organic') end_date: End of trailing 30-day analysis window (ISO format, e.g., '2025-01-15'). Defaults to ~2 days ago. page: Page number (0-indexed, default 0). Use with page_size to paginate through results. page_size: Results per page (default 25, max 100). Set to 100 to get up to 100 results in one call. analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 per row returned (e.g., page_size=25 costs up to 25 credits). Returns: List of top products with sales estimates, pricing, and product details. Includes has_more and total_count for pagination.

ActionTry it

Get marketing asset details

Get full details of a specific marketing asset — email content, social post with engagement metrics, ad creative with cards, or homepage screenshot. Args: company_id: The Particl company ID (e.g., '1234567') - required asset_id: The asset ID - required analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 credit per call (not charged if no data is returned). Returns: Full asset details including content, engagement metrics, metadata, and product_ids when available. Use product_ids with get_company_products to look up sales and revenue data for the featured products.

ActionTry it

Get product breakdown

Get product data broken down by a specific attribute (color, brand, material, keyword, gender, or location). Useful for understanding product mix — e.g., which colors sell best, which brands dominate, what materials are most popular. Args: company_id: The Particl company ID (e.g., '1234567') - required. Use search_companies to find this. breakdown_type: What to break down by. One of: 'keyword', 'material', 'colors', 'brands', 'gender', 'locations'. product_type_id: Product type UUID from get_product_types to filter by category. keyword: Filter by keyword/tag (e.g., 'sustainable'). Searches product tags only. start_date: Start of date range (ISO format, e.g., '2025-01-01'). Defaults to ~30 days ago. end_date: End of date range (ISO format, e.g., '2025-01-31'). Defaults to today. confidence_threshold: Minimum data confidence score (0-10) for included products. Defaults to 6.0, which filters out products with unreliable sales estimates (e.g., gap-filled or anomalous data). You usually don't need to change this. Lower it (e.g., to 0) only if you're looking for a specific product that isn't appearing in results — it may be hidden due to lower data confidence. Set to 0 to include all products regardless of data quality. analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 per row returned. Returns: List of breakdown buckets with name, count, sales_revenue, sales_volume, and pricing

ActionTry it

Get product details

Get full details for a specific product. Returns comprehensive product information including pricing, brand, gender, ratings, reviews, images, materials, keywords, and category data. Use get_company_products first to find product_ids. Args: company_id: The Particl company ID (e.g., '1234567') - required product_id: The product ID - required. Get this from get_company_products or get_market_top_products. analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 credit per call (not charged if no data is returned). Returns: Full product profile with metadata, ratings, and attributes

ActionTry it

Get product types

Browse the product type taxonomy to discover valid categories for filtering. Use the returned product_type_id values in other tools' product_type_id parameter. How to use: 1. Call with no parameters to get root-level categories 2. Pass a product_type_id as parent_product_type_id to see its subcategories 3. Keep drilling deeper until you find the right category 4. Use the chosen product_type_id as the product_type_id filter in other tools Args: parent_product_type_id: Get child categories under this product type. Omit to get root-level categories. Credits: Free — no credits charged. Returns: List of product type categories with product_type_id and name

ActionTry it

Get product variants

Get variant-level data for a specific product (colors, sizes, individual pricing and sales). Use get_company_products first to find product_ids. Args: company_id: The Particl company ID (e.g., '1234567') - required product_id: The product ID - required. Get this from get_company_products or get_market_top_products. start_date: Start of sales data window (ISO format, e.g., '2025-01-01'). Defaults to ~30 days ago. end_date: End of sales data window (ISO format, e.g., '2025-01-31'). Defaults to today. analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 per row returned. Returns: List of product variants with color, size, pricing, and sales data

ActionTry it

Get sales timeseries

Get historical sales timeseries data for a company or a single product. Returns daily, weekly, or monthly sales data points showing revenue, volume, pricing, and inventory over time. Essential for understanding trends, seasonality, and growth. Pass product_id to get timeseries for a single product, or omit for company-wide data. Args: company_id: The Particl company ID (e.g., '1234567') - required. Use search_companies to find this. product_id: Optional product ID to get timeseries for a single product. Get this from get_company_products or get_market_top_products. product_type_id: Product type UUID from get_product_types to filter by category. keyword: Filter by keyword/tag (e.g., 'sustainable', 'organic'). Searches product tags only. start_date: Start of date range (ISO format, e.g., '2025-01-01'). Defaults to ~30 days ago. end_date: End of date range (ISO format, e.g., '2025-01-31'). Defaults to today. aggregation_type: Time granularity - 'daily', 'weekly', or 'monthly'. Auto-selected if omitted. confidence_threshold: Minimum data confidence score (0-10) for included products. Defaults to 6.0, which filters out products with unreliable sales estimates (e.g., gap-filled or anomalous data). You usually don't need to change this. Lower it (e.g., to 0) only if you're looking for a specific product that isn't appearing in results — it may be hidden due to lower data confidence. Set to 0 to include all products regardless of data quality. analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: 1 per data point returned. Returns: Timeseries data points with sales_revenue, sales_volume, avg_current_price, and totals

ActionTry it

Get started

Return first-session guidance, sample prompts, and follow-up ideas for new Particl MCP users. Call this when the user is new, asks what they can do with Particl, or seems unsure how to start. Credits: Free — no credits charged. Returns: is_first_time, welcome_message, sample_prompts, suggested_follow_ups, tracked_competitors, and assistant_guidance for presenting options in plain English.

ActionTry it

Search companies

Search for companies by name or domain. Supports pagination. Args: search: Search term (company name or domain) page_size: Results per page (default 25, max 100). Set higher to get more results per call. page: Page number (0-indexed, default 0). Increment to get additional pages of results. sort_by: Sort results by 'popularity' (default) or 'start_date' country_code: Filter by country code (default 'US'). Common values: 'US', 'CA', 'AU', 'GB'. Pass empty string to search all countries. analysis_goal: Brief summary of the user's research objective for this query. Fill this in based on what the user is trying to learn. Omit if unknown. Credits: Free — no credits charged. Returns: List of matching companies with their basic info. Includes has_more and total_count for pagination.

ActionTry it

How the Particl MCP integration works

The Particl MCP integration connects your Dench AI CRM directly to Particl MCP, so agents can read and act on your Particl MCP data as part of everyday work — answering questions in chat, keeping your CRM in sync, and running automations without anyone copying data between tools.

18 actions are available for agents to invoke on your behalf. Every call runs through Particl MCP's own authorization, scoped to the account you connect.

Set up Particl MCP in Dench

  1. 1

    Sign in to your Dench workspace and open Integrations.

  2. 2

    Find Particl MCP and click Connect — you'll authorize access through Particl MCP's own sign-in flow. No API keys or code required.

  3. 3

    Ask an agent to use Particl MCP in chat, or call it from an automation.

  4. 4

    Manage or disconnect the connection any time from workspace settings.

Frequently asked questions

How does the Particl MCP integration work with Dench?

The Dench Particl MCP integration connects your AI CRM to Particl MCP, so AI agents can work with your Particl MCP data as part of chats, automations, and CRM workflows. You connect your account once, and every agent in your workspace can use it — governed by your workspace permissions.

What actions can AI agents perform with Particl MCP via Dench?

The Particl MCP integration currently exposes 18 actions, including Get company details, Get company events, Get company marketing assets, Get company marketing stats, Get company products, and Get credit balance. Agents invoke them on your behalf from chat or from automations.

Do I need to write code to connect Particl MCP to Dench?

No. You connect Particl MCP from your Dench workspace using Particl MCP's own sign-in and authorization flow — no API keys to copy, no glue code to maintain.

Is the Particl MCP integration secure?

Connections are authorized through Particl MCP's own authentication flow, and Dench stores only the authorization needed to act on your behalf. You can review and disconnect the Particl MCP connection from your workspace settings at any time.

Particl MCP | Dench AI CRM