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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Google Meet, Zoom, and Microsoft Teams meeting recordings and metadata through the Model Context Protocol (MCP), allowing AI agents to query and retrieve meeting data without direct API integration. The server acts as a unified abstraction layer that translates MCP tool calls into authenticated requests to the tl;dv backend service, which handles OAuth token management and platform-specific API translation for each video conferencing provider.
Unique: Implements a unified MCP abstraction layer for three major video conferencing platforms (Google Meet, Zoom, Microsoft Teams) through tl;dv's backend, eliminating the need for agents to manage separate OAuth flows and platform-specific API differences. Uses tl;dv's existing recording infrastructure and transcription pipeline rather than requiring direct platform API access.
vs alternatives: Simpler than building custom integrations for each platform's API because tl;dv handles OAuth, transcription, and platform-specific translation; more accessible than raw platform APIs because it uses standardized MCP protocol instead of REST endpoints.
Retrieves full or partial meeting transcripts from tl;dv's indexed recording library and enables semantic or keyword-based search across meeting content. The MCP server translates search queries into tl;dv backend calls, which leverage pre-processed transcripts stored in tl;dv's database, returning matching segments with timestamps and speaker attribution for context-aware agent reasoning.
Unique: Leverages tl;dv's pre-processed transcript database and indexing infrastructure rather than requiring agents to parse raw audio or video, enabling fast search across multiple meetings without local storage or processing overhead. Integrates speaker diarization and timestamp alignment from tl;dv's transcription pipeline.
vs alternatives: Faster than agents transcribing recordings on-demand because transcripts are pre-computed; more accurate than keyword-only search if tl;dv uses semantic indexing; eliminates need for agents to manage local transcript storage or search indices.
Retrieves AI-generated summaries, key points, action items, and meeting insights from tl;dv's analysis engine through MCP tool calls. The server queries tl;dv's backend for pre-computed summaries and structured insights derived from meeting transcripts and recordings, returning formatted data that agents can use for decision-making or context enrichment without re-analyzing the full recording.
Unique: Exposes tl;dv's proprietary meeting analysis engine (which generates summaries, action items, and insights) through MCP, allowing agents to access pre-computed intelligence without running their own summarization models. Integrates tl;dv's multi-platform transcript processing and AI analysis pipeline.
vs alternatives: More efficient than agents summarizing transcripts themselves because analysis is pre-computed; more consistent than prompt-based summarization because it uses tl;dv's trained models; eliminates token overhead of passing full transcripts to LLMs for analysis.
Aggregates meeting metadata (participants, duration, date, platform source) across Google Meet, Zoom, and Microsoft Teams recordings through a unified MCP interface. The server queries tl;dv's backend to fetch and normalize metadata from each platform's API, presenting a consistent schema regardless of source platform, enabling agents to reason about meetings without platform-specific logic.
Unique: Normalizes metadata across three major platforms (Google Meet, Zoom, Teams) into a unified schema through tl;dv's backend, eliminating the need for agents to handle platform-specific metadata structures or API differences. Uses tl;dv's existing OAuth infrastructure and platform connectors.
vs alternatives: Simpler than querying each platform's API separately because it abstracts platform differences; more maintainable than custom normalization logic because tl;dv handles platform API changes; enables cross-platform queries that would require multiple API calls otherwise.
Implements the Model Context Protocol (MCP) server specification, translating MCP tool calls and resource requests into tl;dv backend API calls and returning results in MCP-compliant formats. The server handles MCP transport (stdio, SSE, or HTTP), request/response serialization, and error handling, allowing any MCP-compatible client (Claude Desktop, custom agents, etc.) to interact with tl;dv meeting data without direct API knowledge.
Unique: Implements the MCP server specification to expose tl;dv as a standardized tool for any MCP-compatible client, rather than requiring custom API bindings. Abstracts tl;dv's REST API behind MCP's tool/resource model, enabling protocol-agnostic client integration.
vs alternatives: More flexible than direct API integration because clients don't need tl;dv SDK knowledge; more portable than custom integrations because MCP is a standard protocol; enables use with Claude Desktop and other MCP clients without custom code.
Manages OAuth authentication flows and credential storage for Google Meet, Zoom, and Microsoft Teams through tl;dv's backend, allowing agents to access recordings without storing or managing platform-specific API keys. The MCP server delegates authentication to tl;dv's OAuth handlers, which refresh tokens and maintain secure credential storage, exposing only authenticated meeting data to the agent.
Unique: Centralizes OAuth credential management in tl;dv's backend rather than requiring agents to handle token refresh, storage, or rotation. Provides agents with authenticated access to three platforms without exposing API keys or tokens, improving security posture.
vs alternatives: More secure than agents managing their own OAuth tokens because credentials are stored server-side; simpler than implementing OAuth flows in agent code because tl;dv handles token lifecycle; more maintainable than embedding platform-specific auth logic in multiple agents.
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 Tldv at 18/100. Supabase also has a free tier, making it more accessible.
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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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