| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) server specification to expose ChainLens functionality as a standardized tool interface compatible with Claude Desktop and other MCP-compliant clients. Uses MCP's request-response messaging pattern to translate client tool calls into ChainLens API operations, handling schema validation, error mapping, and response serialization across the protocol boundary.
Unique: Bridges ChainLens (a Web3/data discovery platform) into the MCP ecosystem by implementing the full server-side protocol stack, allowing Claude and other MCP clients to treat ChainLens operations as first-class tools rather than requiring custom integrations
vs alternatives: Provides standardized MCP access to ChainLens vs. building custom Claude plugins or REST API wrappers, enabling interoperability with any MCP-compatible client ecosystem
Exposes ChainLens seller discovery as an MCP tool that accepts filter parameters (location, capabilities, reputation metrics) and returns paginated seller profiles. Implements query parameter validation, result ranking/sorting, and structured response formatting compatible with MCP's tool result schema, allowing agents to programmatically search and evaluate data providers.
Unique: Integrates ChainLens's seller indexing directly into MCP's tool schema, enabling Claude and agents to discover data providers using natural language queries that are translated into structured filter parameters, rather than requiring manual API calls
vs alternatives: Simpler than building a custom agent loop with ChainLens REST API calls; MCP abstraction handles protocol details while preserving full filtering capability
Provides an MCP tool for submitting data requests to discovered sellers and retrieving request status/results. Implements request creation (with seller ID, data schema, pricing negotiation), asynchronous job tracking (polling or webhook-based status updates), and result retrieval. Handles request state transitions (pending, accepted, processing, completed, failed) and integrates with ChainLens's job queue system.
Unique: Wraps ChainLens's asynchronous request-response model as MCP tools, allowing Claude and agents to submit data requests and poll status without managing HTTP connections or retry logic directly — the MCP server handles protocol translation and state management
vs alternatives: Cleaner abstraction than direct REST API calls for agents; MCP tool interface provides consistent error handling and response formatting across multiple concurrent requests
Implements a dedicated MCP tool for checking the status of submitted data requests and retrieving completed results. Polls ChainLens's job queue system using request IDs, returns structured status objects (state, progress percentage, error messages), and handles result deserialization when jobs complete. Supports both synchronous polling (blocking until completion) and asynchronous status checks (return current state without waiting).
Unique: Decouples job status checking from request submission, allowing agents to manage multiple concurrent requests without blocking on any single one — MCP tool interface enables non-blocking polling patterns that would be cumbersome with raw API calls
vs alternatives: More agent-friendly than raw REST polling; MCP abstraction provides consistent error codes and timeout handling across multiple concurrent jobs
Defines and validates the JSON Schema for all exposed ChainLens tools (seller discovery, data requests, job status), ensuring that Claude and MCP clients can introspect available operations, required parameters, and response formats. Implements schema validation on incoming requests and outgoing responses, providing clear error messages for malformed inputs. Handles type coercion (string to number, array flattening) and default parameter injection.
Unique: Implements strict JSON Schema validation for all ChainLens operations exposed via MCP, preventing invalid requests from reaching the backend and providing Claude with precise parameter documentation for natural language tool selection
vs alternatives: More robust than optional validation; ensures all tool invocations conform to ChainLens API expectations before transmission, reducing error rates and improving agent reliability
Implements a unified error handling layer that translates ChainLens API errors (rate limits, authentication failures, seller unavailable) into MCP-compliant error responses with consistent structure. Maps HTTP status codes to MCP error codes, enriches errors with retry guidance (Retry-After headers, exponential backoff recommendations), and normalizes all responses (success and failure) into MCP's standard JSON-RPC format with proper error objects.
Unique: Centralizes error translation from ChainLens API semantics to MCP protocol semantics, providing agents with actionable error information (retry timing, error classification) rather than raw HTTP errors
vs alternatives: Better error recovery than agents handling raw API errors; MCP abstraction provides consistent retry guidance and error classification across all tools
Manages ChainLens API credentials (API keys, tokens) securely within the MCP server process, handling credential injection into outgoing requests, token refresh logic, and credential rotation. Supports multiple authentication methods (API key, OAuth2 bearer token) and implements credential caching to avoid repeated lookups. Provides secure credential storage patterns (environment variables, credential files with restricted permissions) and logs authentication failures without exposing secrets.
Unique: Implements credential management at the MCP server level, allowing Claude and other clients to invoke ChainLens tools without handling credentials directly — the server acts as a trusted credential broker
vs alternatives: Safer than passing credentials through MCP protocol; server-side credential management prevents credential exposure in client logs or network traffic
Provides structured logging of all MCP tool invocations, ChainLens API calls, and responses, enabling debugging and monitoring. Logs include request parameters (sanitized of sensitive data), response status, execution time, and error details. Implements observability hooks (timing instrumentation, error counters) compatible with standard logging frameworks (Winston, Pino) and monitoring systems (Prometheus, DataDog). Supports log level configuration (debug, info, warn, error) for production vs. development environments.
Unique: Integrates structured logging throughout the MCP server stack, providing end-to-end visibility from Claude's tool invocation through ChainLens API response, enabling rapid debugging and performance analysis
vs alternatives: More comprehensive than basic HTTP logging; structured logs with execution timing and error context enable faster root-cause analysis than raw API logs
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 @chain-lens/mcp-tool at 28/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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