| 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Converts conversational natural language requests into executable stock orders on Zerodha Kite by parsing intent (buy/sell), quantity, price, and order type from user messages and routing them through the Kite API. Uses MCP tool schema to map user utterances to structured order parameters with validation before submission.
Unique: Implements MCP tool schema mapping that translates free-form conversational trading intent directly into Kite REST API calls without intermediate form-filling, with TOTP-based session management baked into the server initialization
vs alternatives: Simpler than building custom Kite integrations because MCP handles protocol negotiation and tool discovery; more direct than web UI automation because it uses native API calls with session persistence
Manages Zerodha Kite login sessions automatically by intercepting TOTP (Time-based One-Time Password) requirements, generating codes from a stored secret, and maintaining authenticated session state across multiple API calls. Eliminates manual re-authentication by storing and refreshing access tokens server-side.
Unique: Embeds TOTP generation directly in the MCP server initialization rather than requiring manual OTP entry or external auth services, using time-synchronized HMAC-SHA1 to generate valid codes on-demand
vs alternatives: More secure than storing plaintext passwords; more convenient than manual OTP entry; avoids dependency on third-party OAuth providers that Zerodha doesn't natively support
Interprets conversational trading requests and maps them to specific trading actions (buy, sell, modify, cancel) with appropriate parameters. Uses LLM context to disambiguate intent, infer missing parameters from conversation history, and validate trading logic before execution.
Unique: Leverages LLM reasoning to parse conversational trading requests and map them to tool invocations, using multi-turn context to infer missing parameters and validate trading logic before execution
vs alternatives: More flexible than form-based trading UIs; enables natural conversation flow; allows LLM to ask clarifying questions if intent is ambiguous
Coordinates execution of multi-leg trading strategies (e.g., buy stock + set stop loss + set profit target) by sequencing dependent orders and handling partial failures. Executes primary order first, then conditionally executes secondary orders based on primary execution status.
Unique: Implements order sequencing logic that executes primary orders first, then conditionally executes dependent orders (stop loss, profit target) based on primary execution status, handling partial failures gracefully
vs alternatives: More reliable than manual order placement because it automates sequencing; more flexible than Kite's native GTT because it supports arbitrary order dependencies; enables complex strategies in conversational interface
Maintains conversation context across multiple trading requests, remembering previously mentioned symbols, quantities, and trading preferences. Enables anaphoric references (e.g., 'sell it' referring to previously mentioned stock) and reduces repetition in multi-turn trading conversations.
Unique: Leverages LLM's built-in context window to maintain trading session state across multiple turns, enabling anaphoric references and reducing parameter repetition without explicit state management
vs alternatives: Simpler than building custom context stores because it relies on LLM's native context; more natural than form-based UIs because users don't repeat parameters; enables fluid multi-turn trading conversations
Fetches current holdings, quantities, entry prices, and P&L metrics from Zerodha Kite API and structures them for natural language querying. Aggregates position data across segments (equity, derivatives) and calculates derived metrics like total portfolio value, unrealized gains, and position-level returns.
Unique: Wraps Kite's holdings endpoint in an MCP tool that normalizes response format and calculates derived P&L metrics on-server, making portfolio state directly queryable by LLM agents without additional client-side processing
vs alternatives: Faster than scraping Kite web UI; more structured than raw API responses because it aggregates and calculates metrics; enables LLM agents to make context-aware trading decisions based on current portfolio state
Fetches real-time stock quotes, OHLC data, and market depth information from Zerodha Kite for specified symbols. Queries the Kite quote API with symbol lookup and returns structured price, volume, and bid-ask spread data suitable for order decision-making.
Unique: Integrates Kite's quote API as an MCP tool that accepts natural language symbol references and returns normalized quote objects, enabling LLM agents to fetch live prices without parsing Kite's raw response format
vs alternatives: More reliable than web scraping; lower latency than polling external data providers; integrated with Kite's native data feed so prices align with order execution prices
Creates Good-Till-Triggered (GTT) conditional orders on Zerodha Kite that automatically execute when specified price conditions are met. Accepts natural language trigger conditions (e.g., 'buy if price drops to 2000') and converts them into GTT rule objects with order parameters.
Unique: Parses natural language conditional expressions ('if price < X then buy Y shares') and maps them to Kite's GTT API schema, which requires explicit trigger type, trigger value, and nested order parameters — abstracting away the schema complexity
vs alternatives: Simpler than building custom monitoring bots because Zerodha handles trigger evaluation server-side; more reliable than client-side price monitoring because it doesn't depend on continuous agent uptime
+5 more capabilities
Scrapes a single URL and converts HTML content to clean markdown using Firecrawl's content extraction pipeline. The firecrawl_scrape tool accepts a URL and optional parameters (formats, headers, wait time, screenshot capability) and returns structured markdown output with automatic cleanup of boilerplate, navigation, and ads. Implements MCP tool handler pattern that marshals arguments through the @mendable/firecrawl-js client library to Firecrawl's backend processing engine.
Unique: Integrates Firecrawl's proprietary content extraction engine (which uses ML-based boilerplate removal and semantic content identification) through MCP protocol, enabling AI agents to access production-grade web scraping without managing browser automation or parsing logic themselves. The markdown conversion is handled server-side rather than client-side, reducing latency and ensuring consistent output formatting.
vs alternatives: Cleaner markdown output than regex-based scrapers like Cheerio or Puppeteer-only solutions because Firecrawl uses ML models to identify main content; simpler than self-hosted solutions because it's fully managed and requires only an API key.
Scrapes multiple URLs in a single operation using Firecrawl's batch processing pipeline. The firecrawl_batch_scrape tool accepts an array of URLs and shared options, submitting them to Firecrawl's backend which processes them in parallel and returns an array of markdown-converted content objects. Implements batching through the @mendable/firecrawl-js client's batch method, which handles request queuing, parallel execution, and result aggregation without requiring client-side coordination.
Unique: Implements server-side parallel batch processing through Firecrawl's backend rather than client-side loop iteration, reducing network round-trips and enabling true concurrent scraping. The batch operation is atomic from the MCP client perspective — a single tool call returns all results, simplifying agent orchestration logic.
More efficient than sequential scraping loops because Firecrawl handles parallelization server-side; simpler than managing Promise.all() with individual scrape calls because batching is a first-class operation with built-in error handling.
Firecrawl MCP Server scores higher at 62/100 vs kite-mcp at 31/100.
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Packages the Firecrawl MCP server as a Docker container with environment-based configuration, enabling deployment to containerized infrastructure (Kubernetes, Docker Compose, cloud platforms). The Dockerfile builds a Node.js runtime with the server code and exposes configuration through environment variables, allowing operators to deploy without modifying code. Supports both cloud and self-hosted Firecrawl instances through configuration.
Unique: Provides production-ready Docker packaging with environment-based configuration, enabling zero-code deployment to containerized infrastructure. The Dockerfile handles Node.js runtime setup and dependency installation, reducing deployment complexity.
vs alternatives: Simpler than manual deployment because Docker handles environment setup; more portable than binary distribution because containers run consistently across platforms.
Registers the Firecrawl MCP server in the Smithery registry, enabling one-click installation and discovery through Smithery's MCP client marketplace. The server is published to Smithery with metadata (description, tags, configuration schema) allowing users to discover and install it without manual setup. Smithery handles server distribution, version management, and client integration.
Unique: Leverages Smithery's MCP server registry to enable one-click installation without manual configuration, reducing friction for end users. Smithery handles server discovery, versioning, and client integration, abstracting deployment complexity.
vs alternatives: More user-friendly than manual installation because Smithery handles discovery and setup; more discoverable than GitHub-only distribution because Smithery provides a centralized marketplace.
Supports connecting to self-hosted Firecrawl instances in addition to Firecrawl's cloud service through configurable API endpoint. The FIRECRAWL_API_URL environment variable allows operators to specify a custom Firecrawl endpoint, enabling deployment scenarios where Firecrawl runs on-premises or in a private cloud. The @mendable/firecrawl-js client library handles endpoint abstraction, routing all API calls to the configured endpoint.
Unique: Enables flexible deployment by supporting both cloud and self-hosted Firecrawl instances through simple endpoint configuration, allowing operators to choose deployment model without code changes. The endpoint abstraction is handled by @mendable/firecrawl-js, making self-hosted support transparent to MCP server code.
vs alternatives: More flexible than cloud-only solutions because self-hosted option is available; simpler than maintaining separate server implementations because endpoint configuration is unified.
Discovers all URLs within a website by crawling from a base URL and building a sitemap-like structure. The firecrawl_map tool accepts a base URL and optional parameters (max depth, include patterns, exclude patterns) and returns a hierarchical array of discovered URLs with metadata about page structure. Uses Firecrawl's crawler to traverse internal links up to specified depth, filtering by inclusion/exclusion patterns, and returns the complete URL graph without fetching full page content.
Unique: Provides lightweight URL discovery without content extraction, allowing agents to plan scraping strategy before committing credits to full content fetches. The depth-based crawling with pattern filtering enables selective discovery — agents can discover only URLs matching specific criteria (e.g., /blog/* paths) without exploring entire site.
vs alternatives: More efficient than scraping every page to build a sitemap because it skips content extraction; more reliable than parsing robots.txt or sitemaps.xml because it performs actual crawling and discovers dynamically-linked content.
Crawls an entire website and extracts content from all discovered pages in a single asynchronous operation. The firecrawl_crawl tool accepts a base URL and options (max pages, allowed domains, exclude patterns, scrape options) and returns a crawl ID for polling. The crawler discovers URLs, extracts markdown content from each page, and stores results server-side. Clients poll firecrawl_crawl_status to retrieve results as they complete, implementing an async job pattern rather than blocking until completion.
Unique: Implements server-side asynchronous crawling with job-based result retrieval, decoupling the crawl initiation from result consumption. The MCP server handles polling coordination through firecrawl_crawl_status, allowing AI agents to initiate long-running crawls and check progress without blocking. Firecrawl's backend manages the entire crawl lifecycle including URL discovery, content extraction, and result storage.
vs alternatives: More scalable than sequential scraping because crawling happens server-side in parallel; simpler than managing Puppeteer/Playwright browser pools because Firecrawl abstracts browser automation and handles rate limiting internally.
Polls the status of an in-progress or completed website crawl and retrieves extracted content. The firecrawl_crawl_status tool accepts a crawl ID and returns current progress (pages crawled, pages remaining, completion percentage), status state (running/completed/failed), and paginated results. Implements polling pattern where clients repeatedly call this tool with the same crawl ID to check progress and incrementally retrieve content as pages are processed, supporting streaming-like result consumption.
Unique: Provides non-blocking status and result retrieval for asynchronous crawls, enabling agents to manage long-running operations without blocking. The polling pattern with pagination allows incremental result consumption — agents can start processing results before the entire crawl completes, reducing end-to-end latency for large crawls.
vs alternatives: More flexible than blocking crawl operations because agents can check progress and retrieve partial results; simpler than webhook-based result delivery because polling requires no external infrastructure setup.
+5 more capabilities