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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 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
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs kite-mcp at 31/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities