# Particl MCP ```json { "name": "Particl MCP", "slug": "particl_mcp", "url": "https://composio.dev/toolkits/particl_mcp", "markdown_url": "https://composio.dev/toolkits/particl_mcp.md", "logo_url": "https://logos.composio.dev/api/particl_mcp", "categories": [ "analytics & data" ], "is_composio_managed": false, "updated_at": "2026-09-05T05:41:04.267Z" } ``` ![Particl MCP logo](https://logos.composio.dev/api/particl_mcp) ## Description Securely connect your AI agents and chatbots (Claude, ChatGPT, Cursor, etc) with Particl MCP or direct API to fetch company profiles, compare product pricing, get sales estimates, and surface marketing activity through natural language. ## Summary Particl MCP is a market intelligence service providing authorized company and product data. Use it to get timely, read-only insights on pricing, sales, and marketing. ## Categories - analytics & data ## Toolkit Details - Tools: 18 ## Images - Logo: https://logos.composio.dev/api/particl_mcp ## Authentication - **Dcr Oauth** - Type: `custom` - Description: Dcr Oauth authentication for Particl MCP. - Setup: - Configure Dcr Oauth credentials for Particl MCP. - Use the credentials when creating an auth config in Composio. ## Suggested Prompts - List competitors' pricing for Product X - Show sales estimates for Company Acme - Retrieve recent marketing spend trends for Acme ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PARTICL_MCP_GET_COMPANY_DETAILS` | 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. | | `PARTICL_MCP_GET_COMPANY_EVENTS` | 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. | | `PARTICL_MCP_GET_COMPANY_MARKETING_ASSETS` | 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. | | `PARTICL_MCP_GET_COMPANY_MARKETING_STATS` | 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). | | `PARTICL_MCP_GET_COMPANY_PRODUCTS` | 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. | | `PARTICL_MCP_GET_CREDIT_BALANCE` | Get credit balance | Check your remaining export credits. Credits: Free — no credits charged. Returns: Credit balance including total limit, used, and remaining credits | | `PARTICL_MCP_GET_MARKETING_ASSET_DETAILS` | 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. | | `PARTICL_MCP_GET_MARKET_PRICING_ANALYSIS` | 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 | | `PARTICL_MCP_GET_MARKET_SALES` | 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 | | `PARTICL_MCP_GET_MARKET_TOP_COMPANIES` | 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. | | `PARTICL_MCP_GET_MARKET_TOP_PRODUCTS` | 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. | | `PARTICL_MCP_GET_PRODUCT_BREAKDOWN` | 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 | | `PARTICL_MCP_GET_PRODUCT_DETAILS` | 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 | | `PARTICL_MCP_GET_PRODUCT_TYPES` | 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 | | `PARTICL_MCP_GET_PRODUCT_VARIANTS` | 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 | | `PARTICL_MCP_GET_SALES_TIMESERIES` | 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 | | `PARTICL_MCP_GET_STARTED` | 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. | | `PARTICL_MCP_SEARCH_COMPANIES` | 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. | ## Supported Triggers None listed. ## Installation and MCP Setup ### Path 1: SDK Installation #### Path 1, Step 1: Install Composio Install the Composio SDK ```python pip install composio_openai ``` ```typescript npm install @composio/openai ``` #### Path 1, Step 2: Initialize Composio and Create Tool Router Session Import and initialize Composio client, then create a Tool Router session ```python from openai import OpenAI from composio import Composio from composio_openai import OpenAIResponsesProvider composio = Composio(provider=OpenAIResponsesProvider()) openai = OpenAI() session = composio.create(user_id='your-user-id') ``` ```typescript import OpenAI from 'openai'; import { Composio } from '@composio/core'; import { OpenAIResponsesProvider } from '@composio/openai'; const composio = new Composio({ provider: new OpenAIResponsesProvider(), }); const openai = new OpenAI({}); const session = await composio.create('your-user-id'); ``` #### Path 1, Step 3: Execute Particl MCP Tools via Tool Router with Your Agent Get tools from Tool Router session and execute Particl MCP actions with your Agent ```python tools = session.tools response = openai.responses.create( model='gpt-4.1', tools=tools, input=[{ 'role': 'user', 'content': 'YOUR_SPECIFIC_PROMPT_HERE' }] ) result = composio.provider.handle_tool_calls( response=response, user_id='your-user-id' ) print(result) ``` ```typescript const tools = session.tools; const response = await openai.responses.create({ model: 'gpt-4.1', tools: tools, input: [{ role: 'user', content: 'YOUR_SPECIFIC_PROMPT_HERE' }], }); const result = await composio.provider.handleToolCalls( 'your-user-id', response.output ); console.log(result); ``` ### Path 2: MCP Server Setup #### Path 2, Step 1: Install Composio Install the Composio SDK for Python or TypeScript ```python pip install composio claude-agent-sdk ``` ```typescript npm install @composio/core ai @ai-sdk/openai @ai-sdk/mcp ``` #### Path 2, Step 2: Initialize Client and Create Tool Router Session Import and initialize the Composio client, then create a Tool Router session for Particl MCP ```python from composio import Composio from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions composio = Composio(api_key='your-composio-api-key') session = composio.create(user_id='your-user-id') url = session.mcp.url ``` ```typescript import { Composio } from '@composio/core'; const composio = new Composio({ apiKey: 'your-api-key' }); const session = await composio.create('your-user-id'); console.log(`Tool Router session created: ${session.mcp.url}`); ``` #### Path 2, Step 3: Connect to AI Agent Use the MCP server with your AI agent (Anthropic Claude or Mastra) ```python import asyncio options = ClaudeAgentOptions( permission_mode='bypassPermissions', mcp_servers={ 'tool_router': { 'type': 'http', 'url': url, 'headers': { 'x-api-key': 'your-composio-api-key' } } }, system_prompt='You are a helpful assistant with access to Particl MCP tools.', max_turns=10 ) async def main(): async with ClaudeSDKClient(options=options) as client: await client.query('YOUR_SPECIFIC_PROMPT_HERE') async for message in client.receive_response(): if hasattr(message, 'content'): for block in message.content: if hasattr(block, 'text'): print(block.text) asyncio.run(main()) ``` ```typescript import { openai } from '@ai-sdk/openai'; import { experimental_createMCPClient as createMCPClient } from '@ai-sdk/mcp'; import { generateText } from 'ai'; const client = await createMCPClient({ transport: { type: 'http', url: session.mcp.url, headers: { 'x-api-key': 'your-composio-api-key', }, }, }); const tools = await client.tools(); const { text } = await generateText({ model: openai('gpt-4o'), tools, messages: [{ role: 'user', content: 'YOUR_SPECIFIC_PROMPT_HERE' }], maxSteps: 5, }); console.log(`Agent: ${text}`); ``` ## Why Use Composio? ### 1. AI Native Particl MCP Integration - Supports both Particl MCP and direct API based integrations - Structured, LLM-friendly schemas for reliable tool execution - Rich coverage for reading and querying your Particl MCP market intelligence ### 2. Managed Auth - Built-in OAuth handling with automatic token refresh and rotation - Central place to manage, scope, and revoke Particl MCP access - Per user and per environment credentials instead of hard-coded keys ### 3. Agent Optimized Design - Tools are tuned using real error and success rates to improve reliability over time - Comprehensive execution logs so you always know what ran, when, and on whose behalf ### 4. Enterprise Grade Security - Fine-grained RBAC so you control which agents and users can access Particl MCP - Scoped, least privilege access to Particl MCP resources - Full audit trail of agent actions to support review and compliance ## Use Particl MCP with any AI Agent Framework Choose a framework you want to connect Particl MCP with: - [ChatGPT Work](https://composio.dev/toolkits/particl_mcp/framework/chatgpt) - [Claude Cowork](https://composio.dev/toolkits/particl_mcp/framework/claude-cowork) - [Hermes](https://composio.dev/toolkits/particl_mcp/framework/hermes-agent) ## Related Toolkits - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources. - [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research. - [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows. - [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research. - [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data. - [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds. - [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights. - [Ahrefs MCP](https://composio.dev/toolkits/ahrefs_mcp) - Ahrefs MCP is Ahrefs' hosted MCP server for SEO data and insights. Use it to access backlinks, organic metrics, keyword research, and competitor analysis. - [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly. - [Audioscrape MCP](https://composio.dev/toolkits/audioscrape_mcp) - Audioscrape MCP lets agents search and retrieve speaker-attributed audio, transcripts, entities, and citations from public and workspace content. Use it to surface searchable, speaker-labeled audio and rich metadata for research, meetings, and content discovery. - [Baremetrics](https://composio.dev/toolkits/baremetrics) - Baremetrics is a subscription analytics platform for recurring-revenue businesses. It helps teams track MRR, churn, customers, and revenue trends in one place. - [Bing Webmaster Tools](https://composio.dev/toolkits/bing_webmaster_tools) - Bing Webmaster Tools is Microsoft's search console for site performance, crawling, indexing, URL submission, and verified site management. It helps site owners understand Bing Search visibility and fix issues that affect organic traffic. - [Bread & Butter](https://composio.dev/toolkits/bread_butter) - Bread & Butter is a lead-intelligence and identity platform for website visitor tracking, user profiles, attribution, authentication, and conversion workflows. It helps teams understand who is visiting, where leads come from, and how users convert. - [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools. - [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping. - [BSC Designer](https://composio.dev/toolkits/bsc_designer) - BSC Designer is a strategy execution platform for balanced scorecards, KPIs, dashboards, and strategy maps. It helps teams turn goals into measurable performance plans they can track over time. - [Chameleon](https://composio.dev/toolkits/chameleon) - Chameleon is a product adoption platform for building in-app experiences, managing customer data, and analyzing user engagement. It helps teams improve onboarding, feature discovery, and product adoption with targeted user experiences. - [Chartly](https://composio.dev/toolkits/chartly) - Chartly renders Chart.js configurations as PNG or SVG images and creates permanent chart URLs for sharing and embedding. Share and embed charts easily with stable image URLs and downloadable vector graphics. - [ChartMogul](https://composio.dev/toolkits/chartmogul) - ChartMogul is a subscription analytics and revenue data platform. It helps teams monitor MRR, churn, customer segments, and billing metrics. - [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights. ## Frequently Asked Questions ### Do I need my own developer credentials to use Particl MCP with Composio? Yes, Particl MCP requires you to configure your own Dcr Oauth credentials. Once set up, Composio handles secure credential storage and management for you. ### Can I use multiple toolkits together? Yes! Composio's Tool Router enables agents to use multiple toolkits. [Learn more](https://docs.composio.dev/tool-router/overview). ### Is Composio secure? Composio is SOC 2 and ISO 27001 compliant with all data encrypted in transit and at rest. [Learn more](https://trust.composio.dev). ### What if the API changes? Composio maintains and updates all toolkit integrations automatically, so your agents always work with the latest API versions. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)