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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables AI assistants to send messages to Webex spaces and direct conversations through the Model Context Protocol, translating natural language intents into Webex API calls. The MCP server acts as a bridge between LLM tool-use requests and Webex's REST API, handling authentication via bearer tokens and message formatting for both plain text and markdown content.
Unique: Implements Webex messaging as an MCP resource, allowing any MCP-compatible LLM client (Claude, custom agents) to send messages without writing Webex SDK code. Uses MCP's tool-calling protocol to expose Webex API operations as callable functions with schema-based validation.
vs alternatives: Simpler than building custom Webex SDK integrations because MCP abstracts authentication and API details; more flexible than Webex bots because it works with any LLM that supports MCP, not just Webex's native bot framework.
Allows AI assistants to fetch and read messages from Webex spaces and direct conversations through MCP, enabling context-aware responses based on conversation history. The server queries Webex's message API with pagination support, returning message metadata (sender, timestamp, content) that LLMs can analyze for context or decision-making.
Unique: Exposes Webex message history as MCP resources that LLMs can query directly, avoiding the need for custom API clients or message caching layers. Integrates with MCP's resource protocol to provide paginated, schema-validated message retrieval.
vs alternatives: More lightweight than building a separate message indexing service; integrates directly with Webex's official API rather than relying on webhooks or polling, ensuring real-time accuracy.
Provides AI assistants with the ability to list, create, and manage Webex spaces and room memberships through MCP tool calls. The server translates LLM intents into Webex API operations for space CRUD, member addition/removal, and space metadata queries, with schema validation for space properties like title and description.
Unique: Exposes Webex space and membership operations as MCP tools, allowing LLMs to manage team structure without custom Webex SDK code. Uses MCP's schema-based tool registry to validate space properties and membership changes before API calls.
vs alternatives: Simpler than Webex's native admin APIs for programmatic space creation because MCP abstracts authentication and provides a standardized interface; more flexible than Webex's UI-based space management because it integrates with AI decision-making workflows.
The MCP server implements the Model Context Protocol specification to translate between LLM tool-use requests and Webex API calls, including schema validation, error handling, and response formatting. It uses MCP's tool and resource definitions to expose Webex capabilities with typed parameters, ensuring that LLM-generated requests conform to Webex API requirements before execution.
Unique: Implements the full MCP protocol stack for Webex, including tool definitions with JSON Schema, resource URIs, and error handling. Uses MCP's standardized request/response format to ensure compatibility with any MCP-compliant LLM client.
vs alternatives: More standardized than custom REST API wrappers because it follows the MCP specification, enabling interoperability with multiple LLM platforms; more type-safe than direct API calls because MCP enforces schema validation before execution.
Handles Webex API authentication by accepting bearer tokens and managing their lifecycle within the MCP server context. The server validates tokens, handles authentication errors, and provides clear error messages when tokens are invalid or lack required scopes, without exposing token details in logs or responses.
Unique: Centralizes Webex authentication at the MCP server level, preventing tokens from being exposed to LLM prompts or logs. Uses HTTP Bearer authentication standard with scope validation to ensure tokens have required permissions before attempting API calls.
vs alternatives: More secure than passing tokens directly to LLMs because it isolates credentials at the server layer; more flexible than hardcoded credentials because it supports environment-based token injection.
Enables AI assistants to upload and reference files in Webex messages through MCP, translating file paths or URLs into Webex-compatible attachments. The server handles file type validation, size limits, and Webex's file upload API, allowing LLMs to attach documents, images, or other media to messages without manual file handling.
Unique: Abstracts Webex's file upload API through MCP, allowing LLMs to attach files to messages without understanding Webex's multipart upload protocol. Validates file types and sizes before upload to prevent API errors.
vs alternatives: Simpler than direct Webex SDK file uploads because MCP handles protocol details; more flexible than message-only communication because it enables rich media sharing from AI agents.
Provides AI assistants with the ability to search for and retrieve Webex user information (email, display name, user ID) through MCP, enabling context-aware addressing of messages and membership operations. The server queries Webex's people API with optional filters, returning user metadata that LLMs can use to identify recipients or validate user existence.
Unique: Exposes Webex's people directory as an MCP search resource, allowing LLMs to resolve user identities without hardcoding user IDs. Uses Webex's official people API with schema-validated search parameters.
vs alternatives: More flexible than hardcoded user lists because it queries the live Webex directory; more efficient than manual user lookups because it integrates directly with Webex's API.
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 Webex at 24/100.
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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
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