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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Translates natural language AI requests into XML-RPC CRUD operations (create, read, update, delete) against Odoo models through the MCP tools interface. The OdooToolHandler registers dynamic tools for each accessible model, mapping LLM function calls to Odoo's XML-RPC API with automatic field validation, type coercion, and error handling. Supports batch operations and maintains request context across multi-step workflows.
Unique: Dynamically registers MCP tools for each Odoo model accessible to the authenticated user, with automatic schema generation from Odoo's ir.model.fields metadata. Unlike static tool definitions, this enables AI assistants to discover and operate on custom models without code changes. Smart field selection automatically excludes binary/computed fields to optimize LLM token usage.
vs alternatives: Provides tighter Odoo integration than generic REST API wrappers because it leverages Odoo's native XML-RPC protocol and permission system, reducing latency and eliminating the need for intermediate REST layers.
Exposes Odoo records as MCP resources using a hierarchical URI schema (odoo://model/record_id or odoo://model?domain=filter), enabling AI assistants to reference and retrieve specific records without tool invocation. The OdooResourceHandler implements resource URIs with support for domain-based filtering, pagination, and lazy-loading of related records. Resources are formatted hierarchically with parent-child relationships preserved for context.
Unique: Implements a two-tier resource access pattern: direct record URIs (odoo://model/id) for fast lookups and domain-filtered URIs (odoo://model?domain=...) for dynamic queries. Resources are cached with TTL-based invalidation, and hierarchical formatting automatically includes parent-child relationships to provide rich context without requiring additional API calls.
vs alternatives: Outperforms REST API approaches by leveraging MCP's native resource protocol, reducing round-trips and enabling AI assistants to maintain persistent references to Odoo records across conversation turns without re-fetching.
Enforces Odoo's native access control through the AccessController, which validates user permissions against ir.model.access and ir.rule records before executing operations. Supports two modes: standard (permission-enforced) and YOLO (bypass), with read-only and full-access variants. Permission checks are cached with configurable TTL to reduce Odoo round-trips while maintaining security boundaries.
Unique: Integrates directly with Odoo's ir.model.access and ir.rule tables rather than implementing a separate permission layer, ensuring AI operations respect the same access control as native Odoo users. Provides a YOLO mode toggle for development environments that completely bypasses checks, with separate read-only and full-access variants to limit blast radius.
vs alternatives: Tighter security than generic API wrappers because it enforces Odoo's native permission model without requiring manual ACL configuration; permission caching reduces latency vs. checking permissions on every operation.
The RecordFormatter and DatasetFormatter classes optimize Odoo record output for LLM token budgets by automatically excluding binary fields, computed fields, and low-value metadata while preserving business-critical relationships. Hierarchical formatting includes parent records and related collections with configurable depth limits. Field selection is model-aware, using Odoo's field metadata to determine relevance.
Unique: Uses Odoo's ir.model.fields metadata to make intelligent decisions about field inclusion, automatically excluding binary/computed fields and low-value metadata. Hierarchical formatting preserves parent-child relationships (e.g., customer → orders → order lines) in a single output structure, reducing the number of API calls needed to provide rich context.
vs alternatives: Outperforms generic JSON formatters by understanding Odoo's field semantics and automatically optimizing for LLM consumption; hierarchical expansion reduces context fragmentation vs. flat record lists.
The PerformanceManager implements a multi-tier caching architecture (in-memory cache with TTL, optional Redis backend) and connection pooling to reduce latency and Odoo server load. Cache keys are model-aware, and invalidation is triggered by write operations. Connection pooling maintains persistent XML-RPC sessions, reducing authentication overhead. Performance metrics are collected for monitoring.
Unique: Implements a two-tier caching strategy: in-memory LRU cache for fast local access and optional Redis backend for distributed caching across multiple MCP server instances. Connection pooling maintains persistent XML-RPC sessions, reducing authentication overhead by 50-70% vs. per-request connections. Cache invalidation is write-aware, automatically clearing related entries when records are modified.
vs alternatives: Outperforms stateless API approaches by maintaining persistent connections and multi-tier caching; distributed caching support enables scaling to multiple concurrent AI assistants without cache coherency issues.
The OdooConnection class manages XML-RPC client lifecycle, handling authentication via Odoo's authenticate() RPC method, connection pooling, and health checks. Supports multiple authentication schemes (username/password, API tokens) and maintains connection state with automatic reconnection on failure. Error handling translates Odoo XML-RPC exceptions into structured error messages.
Unique: Wraps Odoo's XML-RPC protocol with connection pooling and health checks, providing automatic reconnection and error recovery without requiring manual intervention. Supports both username/password and API token authentication, with transparent fallback to credentials if tokens are unavailable. Health checks validate connection state before operations, reducing cascading failures.
vs alternatives: More robust than direct XML-RPC clients because it adds connection pooling, health checking, and automatic reconnection; API token support provides better security than storing plaintext credentials.
The MCP server supports multiple transport protocols through FastMCP: stdio (for local/embedded use) and HTTP (for remote/cloud deployments). Transport selection is configured via environment variables, and the server automatically adapts request/response handling to the chosen protocol. Supports both synchronous and streaming responses.
Unique: Abstracts transport protocol selection through FastMCP, enabling the same server code to run over stdio (for local clients) or HTTP (for remote clients) without code changes. Transport is configured via environment variables, supporting flexible deployment topologies from embedded to cloud-native.
vs alternatives: More flexible than single-protocol implementations because it supports both local (stdio) and remote (HTTP) deployments from the same codebase; FastMCP integration reduces boilerplate vs. manual protocol handling.
The OdooConfig class centralizes configuration management, parsing and validating environment variables for Odoo connection, authentication, caching, and operational modes. Supports configuration profiles (development, production) and provides sensible defaults. Validation ensures required parameters are present and have correct types before server startup.
Unique: Centralizes configuration in a single OdooConfig class with environment variable parsing and validation, supporting multiple deployment profiles without code changes. Provides sensible defaults for optional parameters while enforcing required ones at startup.
vs alternatives: Cleaner than scattered environment variable access throughout the codebase; centralized validation catches configuration errors early vs. runtime failures.
+2 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 Odoo at 22/100.
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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