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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Runbear embeds an MCP client directly into Slack's messaging interface, allowing users to invoke AI agents and trigger tool calls through natural chat commands without leaving the workspace. The system translates Slack messages into MCP tool requests, executes them against integrated services, and returns results as formatted Slack messages. This eliminates context-switching and enables team-wide access to automated workflows through a familiar chat UX.
Unique: Runbear is a no-code MCP client embedded in chat platforms rather than a developer-facing MCP server; it abstracts away MCP protocol complexity and presents tool invocation as natural chat interactions, with pre-built integrations for 2,000+ services rather than requiring custom tool definitions
vs alternatives: Unlike Slack bots that require custom development or workflow builders that live outside chat, Runbear combines MCP's multi-tool orchestration with Slack's native UX, enabling non-technical users to compose cross-tool automations through conversation
Runbear enables users to create tickets in Jira or Linear directly from Slack conversations, automatically extracting context from the chat thread (participants, discussion history, attachments) and populating ticket fields. The system maps Slack message content to ticket schemas, handles OAuth authentication to target systems, and returns ticket links back to Slack. This capability supports mutating operations across multiple ticketing platforms with a single chat command.
Unique: Runbear extracts conversation context from Slack threads using the underlying AI model to intelligently populate ticket fields, rather than requiring users to manually specify all fields or relying on simple template substitution
vs alternatives: More context-aware than native Slack-to-Jira integrations which typically require manual field entry; faster than copy-pasting discussion into ticket systems because it preserves thread history and participant information automatically
Runbear claims to support Microsoft Teams and Discord in addition to Slack, embedding the MCP client in these chat platforms and enabling the same agent invocation and tool orchestration workflows. The system adapts the Slack-native interface to Teams and Discord APIs, handling platform-specific message formatting and authentication. This enables organizations using Teams or Discord to access the same automation capabilities as Slack users.
Unique: Runbear claims to provide a unified MCP client experience across Slack, Teams, and Discord, adapting to each platform's API and message format rather than requiring separate implementations
vs alternatives: unknown — insufficient data on Teams/Discord implementation quality and feature parity with Slack version
Runbear claims to encrypt API credentials and sensitive data both in transit (TLS) and at rest, and claims not to store sensitive content beyond what is needed for operations. The system manages OAuth tokens and API keys for integrated services, encrypting them before storage and using them only when invoking tools. This protects against credential exposure and unauthorized access to integrated systems.
Unique: Runbear claims to encrypt credentials at rest and in transit, and claims not to store sensitive content beyond what is needed, but implementation details are not documented
vs alternatives: unknown — insufficient data on encryption implementation, key management, and compliance verification compared to alternatives
Runbear enables users to create and update CRM records (HubSpot, Attio) directly from Slack conversations, mapping chat participants and discussion content to CRM contact/company fields. The system uses the AI model to extract relevant information from messages, authenticate to CRM APIs, and perform create/update operations. This allows teams to maintain CRM data freshness without leaving Slack or manually entering information into separate systems.
Unique: Runbear uses the AI model to intelligently extract and map unstructured Slack conversation content to CRM fields, rather than requiring explicit field specification or pre-defined templates
vs alternatives: More flexible than Zapier/Make automations which require explicit field mapping; faster than manual CRM entry because it infers field values from conversation context using natural language understanding
Runbear enables users to query information across integrated knowledge sources (Google Drive, Notion, Linear, HubSpot, Fireflies, Attio, Confluence, Gmail) directly from Slack chat. The system performs semantic search across these sources using embeddings, retrieves relevant documents/records, and returns formatted results in Slack. This is a read-only capability that aggregates information from multiple tools without requiring users to navigate each system separately.
Unique: Runbear aggregates search across 8+ heterogeneous knowledge sources (docs, CRM, meeting notes, email) with a single semantic search query, using the AI model to rank and synthesize results rather than returning raw search hits from each source
vs alternatives: More comprehensive than individual tool search because it queries across multiple systems simultaneously; faster than manual context-gathering because results are synthesized and ranked by relevance rather than requiring users to check each tool separately
Runbear monitors Gmail inboxes for incoming emails, parses email content using the AI model, and triggers automated actions (e.g., auto-replies, ticket creation, CRM updates) based on email content patterns. The system integrates with Gmail API for inbox monitoring, uses NLP to extract intent and entities from email bodies, and orchestrates downstream actions through MCP tools. This enables email-driven automation workflows without manual intervention.
Unique: Runbear uses the AI model to parse email content and infer appropriate actions (auto-reply, ticket creation, CRM update) based on email intent, rather than requiring explicit rules or regex patterns
vs alternatives: More intelligent than Gmail filters or Zapier rules because it understands email semantics and can trigger complex multi-step workflows; more flexible than templated auto-replies because responses can be customized based on email content
Runbear enables users to query Stripe for payment information (refund status, subscription details) and perform mutations (issue refunds, update subscriptions) directly from Slack. The system authenticates to Stripe API using provided credentials, translates natural language requests into Stripe API calls, and returns formatted results in Slack. This allows finance and support teams to manage payments without leaving the chat interface.
Unique: Runbear translates natural language payment requests into Stripe API calls without requiring users to know Stripe API syntax or navigate the dashboard, using the AI model to infer customer identity and operation type from chat context
vs alternatives: Faster than Stripe dashboard for quick lookups and refunds because it eliminates navigation overhead; more accessible to non-technical support staff because it accepts natural language rather than requiring API knowledge
+4 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 Runbear at 21/100. Runbear leads on quality, while Supabase is stronger on ecosystem. 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
+1 more capabilities