Odoo

MCP ServerFree

** - Connect AI assistants to Odoo ERP systems for business data access and workflow automation.

Open Source

/ 100

10 capabilities

Capabilities10 decomposed

natural language crud operations on odoo models via mcp tools

Medium confidence

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.

Solves for

Create new customer records from unstructured data without manual form entryUpdate invoice statuses and line items based on business logic decisionsDelete obsolete records while respecting Odoo's cascade rulesQuery and filter records using natural language constraints+1 more

Best for

Teams automating Odoo workflows through AI assistants (Claude, Cursor, VS Code)

Developers building LLM agents that need ERP system integration

Non-technical business users leveraging AI to interact with Odoo without SQL

Requires

Odoo 14.0+ instance with XML-RPC enabled

Valid Odoo user credentials with model-level permissions

MCP-compatible AI assistant (Claude Desktop, Cursor, VS Code, Zed)

Limitations

CRUD operations execute synchronously — no built-in async/batch queue for high-volume writes

Field validation depends on Odoo model definitions; complex custom field types may require manual mapping

No transaction rollback support — failed operations within a workflow don't automatically revert prior changes

What makes it 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.

uri-based resource access to odoo records with query filtering

Medium confidence

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.

Solves for

Reference specific customer or invoice records in AI conversations without re-queryingBrowse related records (e.g., all orders for a customer) through URI patternsImplement RAG-style context injection where Odoo records become part of the LLM's knowledge basePaginate large result sets to stay within token budgets+1 more

Best for

Building conversational AI agents that maintain context across multi-turn interactions

Implementing retrieval-augmented generation (RAG) with live Odoo data

Teams needing read-only data access patterns without tool-based operations

Requires

Odoo 14.0+ with XML-RPC enabled

Valid Odoo user credentials with read permissions on target models

MCP client supporting resource URIs (Claude Desktop 0.7+, Cursor, VS Code)

Limitations

Resource URIs are read-only by design — modifications require tool-based CRUD operations

Pagination is offset-based, not cursor-based; inefficient for large datasets with frequent updates

Related record expansion is limited to 2-3 levels deep to prevent exponential data bloat

What makes it 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.

permission-aware access control with optional yolo mode bypass

Medium confidence

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.

Solves for

Ensure AI operations respect Odoo's role-based access control (RBAC) without additional configurationAllow trusted environments to bypass permissions for rapid prototyping or admin automationCache permission checks to reduce latency in high-frequency operationsAudit which models and operations the AI assistant can access based on user role

Best for

Production deployments requiring strict security enforcement

Teams with complex Odoo permission hierarchies (multi-company, record rules)

Development/testing environments where permission bypass accelerates iteration

Requires

Odoo 14.0+ with ir.model.access and ir.rule tables

Valid Odoo user credentials with appropriate role assignments

Environment variable ODOO_YOLO_MODE=true to enable bypass (default: false)

Limitations

YOLO mode completely disables access control — use only in trusted, isolated environments

Permission caching can lag behind real-time permission changes (default TTL 300s)

Record-level rules (ir.rule) are evaluated per-operation; no bulk permission pre-check

What makes it 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.

smart field selection and hierarchical data formatting for llm consumption

Medium confidence

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.

Solves for

Reduce token usage by excluding irrelevant fields (binary attachments, internal IDs, timestamps)Present related records (customer, product, order lines) in a single hierarchical structureFormat large search results with pagination to fit within LLM context windowsCustomize field visibility per model to match business domain requirements

Best for

Teams optimizing LLM token usage in high-volume Odoo integrations

Building conversational agents where context window is a bottleneck

Scenarios requiring human-readable output formatting for LLM processing

Requires

Odoo 14.0+ with field metadata available via ir.model.fields

Python 3.9+ for the MCP server

Limitations

Hierarchical expansion is limited to 2-3 levels deep to prevent exponential data bloat

Computed fields are excluded by default; custom field types may not format correctly

Pagination is offset-based, not cursor-based; inefficient for large, frequently-updated datasets

What makes it 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.

multi-tier caching system with connection pooling for performance optimization

Medium confidence

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.

Solves for

Reduce latency for repeated queries on the same records or modelsMinimize Odoo server load by caching frequently-accessed dataMaintain persistent connections to avoid repeated authenticationMonitor performance metrics to identify bottlenecks in AI workflows

Best for

High-frequency Odoo access patterns (100+ operations/minute)

Teams running multiple concurrent AI assistants against the same Odoo instance

Production deployments where latency and server load are critical

Requires

Python 3.9+ with caching libraries (functools.lru_cache or Redis client)

Optional: Redis 6.0+ for distributed caching across multiple server instances

Environment variables: ODOO_CACHE_TTL (default 300s), ODOO_POOL_SIZE (default 5)

Limitations

In-memory cache is process-local; not shared across multiple MCP server instances

Cache invalidation is write-operation-based; external Odoo changes are not detected

Connection pooling adds ~50-100ms overhead for pool initialization

What makes it 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.

xml-rpc authentication and connection management with health checking

Medium confidence

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.

Solves for

Establish and maintain authenticated connections to Odoo instancesAutomatically reconnect on transient network failures without user interventionValidate connection health before executing operationsSupport multiple authentication methods (credentials, API tokens)

Best for

Teams integrating with Odoo instances over unreliable networks

Production deployments requiring high availability and automatic failover

Scenarios with multiple concurrent AI assistants sharing connection pools

Requires

Odoo 14.0+ with XML-RPC enabled (default: enabled)

Valid Odoo user credentials or API token

Network connectivity to Odoo instance (HTTP/HTTPS)

Limitations

Health checks add ~50ms latency per operation; can be disabled for performance

Connection pooling is process-local; not shared across multiple MCP server instances without Redis

API token authentication requires Odoo 14.0+; older versions only support username/password

What makes it 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.

multi-protocol transport support (stdio and http) with fastmcp integration

Medium confidence

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.

Solves for

Run the MCP server locally within VS Code, Cursor, or Claude Desktop via stdioDeploy the MCP server as a remote service accessible over HTTP/HTTPSSupport both synchronous operations and streaming responses for long-running tasksEnable flexible deployment topologies (embedded, containerized, serverless)

Best for

Teams deploying MCP servers in diverse environments (local, cloud, containerized)

Scenarios requiring both embedded (stdio) and remote (HTTP) access patterns

Production deployments where transport flexibility is critical

Requires

Python 3.9+ with FastMCP library

Environment variable: TRANSPORT (stdio or http, default: stdio)

For HTTP: PORT environment variable (default: 8000)

Limitations

Stdio transport is process-local; not suitable for multi-client scenarios

HTTP transport adds network latency (~50-200ms) vs. stdio

Streaming responses require client support; not all MCP clients support streaming

What makes it 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.

configuration management with environment variable validation and odooconfig class

Medium confidence

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.

Solves for

Manage Odoo connection parameters (URL, database, credentials) via environment variablesConfigure caching behavior (TTL, backend) without code changesEnable/disable YOLO mode and other operational modes via configurationValidate configuration at startup to catch errors early

Best for

Teams deploying MCP servers in containerized/cloud environments

Scenarios requiring different configurations for dev/staging/production

Deployments where secrets management is critical (API tokens, credentials)

Requires

Python 3.9+

Environment variables: ODOO_URL, ODOO_DB, ODOO_USERNAME, ODOO_PASSWORD (or ODOO_API_TOKEN)

Optional: ODOO_CACHE_TTL, ODOO_POOL_SIZE, ODOO_YOLO_MODE, TRANSPORT, PORT

Limitations

Configuration is static at startup; runtime changes require server restart

No built-in secrets management; relies on external tools (Kubernetes secrets, AWS Secrets Manager)

Validation is basic; complex interdependencies between parameters are not checked

What makes it 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.

error handling and exception translation from odoo xml-rpc to structured messages

Medium confidence

The system translates Odoo XML-RPC exceptions (access denied, record not found, validation errors) into structured error messages that are human-readable and actionable for AI assistants. Error context includes the original operation, affected model/record, and suggested remediation steps. Errors are logged with full stack traces for debugging.

Solves for

Provide clear error messages when operations fail due to permissions, validation, or data issuesEnable AI assistants to understand and recover from errors without manual interventionLog errors with full context for debugging and monitoringDistinguish between transient (retry-able) and permanent (non-retry-able) errors

Best for

Production deployments requiring robust error handling and observability

Teams building autonomous AI agents that need to recover from errors

Scenarios where error context is critical for debugging

Requires

Python 3.9+ with logging module

Odoo 14.0+ with standard exception types

Limitations

Error translation is best-effort; some Odoo exceptions may not map cleanly to structured messages

Logging is process-local; requires external aggregation (ELK, Datadog) for centralized monitoring

No built-in retry logic; AI assistants must implement their own retry strategies

What makes it unique

Translates Odoo's XML-RPC exceptions into structured error messages with operation context and suggested remediation, enabling AI assistants to understand and recover from errors. Distinguishes between permission errors, validation errors, and transient failures.

vs alternatives

More informative than raw XML-RPC exceptions because it provides operation context and remediation suggestions; structured format enables AI assistants to parse and respond to errors programmatically.

dynamic tool registration based on odoo model metadata with schema generation

Medium confidence

The OdooToolHandler dynamically registers MCP tools for each Odoo model accessible to the authenticated user by querying ir.model and ir.model.fields metadata. Tool schemas are generated automatically from field definitions, including type information, constraints, and help text. This enables AI assistants to discover and operate on custom models without code changes or manual schema definition.

Solves for

Enable AI assistants to discover available Odoo models and their fields without manual configurationGenerate accurate tool schemas from Odoo's field metadata, including types and constraintsSupport custom models and fields without requiring code changes to the MCP serverProvide field documentation (help text) to AI assistants for better decision-making

Best for

Teams with custom Odoo models that change frequently

Deployments where manual schema maintenance is impractical

Scenarios requiring support for multiple Odoo instances with different model sets

Requires

Odoo 14.0+ with ir.model and ir.model.fields tables

Valid Odoo user credentials with read access to ir.model and ir.model.fields

Limitations

Schema generation is static at server startup; new models require server restart

Complex field types (many2many, one2many) are simplified in schema generation

Field constraints (unique, required) are included but not fully validated by the MCP server

What makes it unique

Dynamically generates MCP tool schemas from Odoo's ir.model and ir.model.fields metadata at server startup, enabling support for custom models without code changes. Unlike static tool definitions, this approach automatically adapts to model changes and supports multiple Odoo instances with different model sets.

vs alternatives

More flexible than hardcoded tool schemas because it adapts to custom models and field changes; metadata-driven approach reduces maintenance burden vs. manual schema definition.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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document-crud-operations-via-mcpdocument-search-and-filtering-via-mcp

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mcp-native record crud with nocodb rest api v2 bridging

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** – Product‑discovery and strategy platform integration. Create, query and update opportunities, solutions, outcomes, requirements and feedback from any MCP‑aware LLM.

mcp-native opportunity crud operations

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AskYourDatabase

Chat with SQL database, explore and visualize data

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Best For

✓Teams automating Odoo workflows through AI assistants (Claude, Cursor, VS Code)
✓Developers building LLM agents that need ERP system integration
✓Non-technical business users leveraging AI to interact with Odoo without SQL
✓Building conversational AI agents that maintain context across multi-turn interactions
✓Implementing retrieval-augmented generation (RAG) with live Odoo data
✓Teams needing read-only data access patterns without tool-based operations
✓Production deployments requiring strict security enforcement
✓Teams with complex Odoo permission hierarchies (multi-company, record rules)

Known Limitations

⚠CRUD operations execute synchronously — no built-in async/batch queue for high-volume writes
⚠Field validation depends on Odoo model definitions; complex custom field types may require manual mapping
⚠No transaction rollback support — failed operations within a workflow don't automatically revert prior changes
⚠XML-RPC latency adds ~100-300ms per operation; high-frequency polling not recommended
⚠Resource URIs are read-only by design — modifications require tool-based CRUD operations
⚠Pagination is offset-based, not cursor-based; inefficient for large datasets with frequent updates

Requirements

Odoo 14.0+ instance with XML-RPC enabledValid Odoo user credentials with model-level permissionsMCP-compatible AI assistant (Claude Desktop, Cursor, VS Code, Zed)Python 3.9+ for the MCP serverOdoo 14.0+ with XML-RPC enabledValid Odoo user credentials with read permissions on target modelsMCP client supporting resource URIs (Claude Desktop 0.7+, Cursor, VS Code)Odoo 14.0+ with ir.model.access and ir.rule tables

Input / Output

Accepts: Natural language instructions from LLM, Structured field data (strings, numbers, booleans, dates), Domain filters (Odoo ORM syntax: [('field', 'operator', 'value')]), URI strings with model name and optional record ID, Domain filter expressions (Odoo ORM syntax), Query parameters (limit, offset, fields), Odoo model name, Operation type (create, read, update, delete), User context (UID from authentication), Odoo record data (dict or list of dicts), Model name for field metadata lookup, Pagination parameters (limit, offset), Cache key (model name, record ID, domain filter), TTL value in seconds, Connection parameters (host, port, credentials), Odoo instance URL (http://localhost:8069 or https://odoo.example.com), Database name, Credentials (username/password or API token), MCP protocol messages (JSON-RPC 2.0), Tool invocations and resource requests, Environment variables (strings, booleans, integers), Odoo XML-RPC exceptions, Operation context (model, operation type, parameters), Odoo model metadata (ir.model, ir.model.fields records)

Produces: Operation result (record ID for create, boolean for delete/update), Error messages with Odoo exception details, Formatted record data for read operations, Hierarchical JSON with record data and related records, Formatted text representation for LLM consumption, Pagination metadata (total count, offset, limit), Boolean (permission granted/denied), Error message with specific permission violation details, Formatted text representation optimized for LLM consumption, Hierarchical JSON with related records, Cached record data or cache miss indicator, Performance metrics (hit rate, latency, pool utilization), Authenticated session object, Connection status (connected, disconnected, error), MCP protocol responses (JSON-RPC 2.0), Tool results and resource data, OdooConfig object with validated parameters, Validation errors with specific parameter details, Structured error messages with model, operation, and remediation details, Log entries with full stack traces, MCP tool definitions with JSON schemas, Tool descriptions with field documentation

UnfragileRank

Adoption15%(25% weight)

Quality20%(25% weight)

Ecosystem30%(15% weight)

Match Graph25%(30% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

10 capabilities

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** - Connect AI assistants to Odoo ERP systems for business data access and workflow automation.

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Capabilities10 decomposed

natural language crud operations on odoo models via mcp tools

Medium confidence

Solves for

Best for

Teams automating Odoo workflows through AI assistants (Claude, Cursor, VS Code)

Developers building LLM agents that need ERP system integration

Non-technical business users leveraging AI to interact with Odoo without SQL

Requires

Odoo 14.0+ instance with XML-RPC enabled

Valid Odoo user credentials with model-level permissions

MCP-compatible AI assistant (Claude Desktop, Cursor, VS Code, Zed)

Limitations

CRUD operations execute synchronously — no built-in async/batch queue for high-volume writes

Field validation depends on Odoo model definitions; complex custom field types may require manual mapping

No transaction rollback support — failed operations within a workflow don't automatically revert prior changes

What makes it unique

vs alternatives

uri-based resource access to odoo records with query filtering

Medium confidence

Solves for

Best for

Building conversational AI agents that maintain context across multi-turn interactions

Implementing retrieval-augmented generation (RAG) with live Odoo data

Teams needing read-only data access patterns without tool-based operations

Requires

Odoo 14.0+ with XML-RPC enabled

Valid Odoo user credentials with read permissions on target models

MCP client supporting resource URIs (Claude Desktop 0.7+, Cursor, VS Code)

Limitations

Resource URIs are read-only by design — modifications require tool-based CRUD operations

Pagination is offset-based, not cursor-based; inefficient for large datasets with frequent updates

Related record expansion is limited to 2-3 levels deep to prevent exponential data bloat

What makes it unique

vs alternatives

permission-aware access control with optional yolo mode bypass

Medium confidence

Solves for

Best for

Production deployments requiring strict security enforcement

Teams with complex Odoo permission hierarchies (multi-company, record rules)

Development/testing environments where permission bypass accelerates iteration

Requires

Odoo 14.0+ with ir.model.access and ir.rule tables

Valid Odoo user credentials with appropriate role assignments

Environment variable ODOO_YOLO_MODE=true to enable bypass (default: false)

Limitations

YOLO mode completely disables access control — use only in trusted, isolated environments

Permission caching can lag behind real-time permission changes (default TTL 300s)

Record-level rules (ir.rule) are evaluated per-operation; no bulk permission pre-check

What makes it unique

vs alternatives

smart field selection and hierarchical data formatting for llm consumption

Medium confidence

Solves for

Best for

Teams optimizing LLM token usage in high-volume Odoo integrations

Building conversational agents where context window is a bottleneck

Scenarios requiring human-readable output formatting for LLM processing

Requires

Odoo 14.0+ with field metadata available via ir.model.fields

Python 3.9+ for the MCP server

Limitations

Hierarchical expansion is limited to 2-3 levels deep to prevent exponential data bloat

Computed fields are excluded by default; custom field types may not format correctly

Pagination is offset-based, not cursor-based; inefficient for large, frequently-updated datasets

What makes it unique

vs alternatives

multi-tier caching system with connection pooling for performance optimization

Medium confidence

Solves for

Best for

High-frequency Odoo access patterns (100+ operations/minute)

Teams running multiple concurrent AI assistants against the same Odoo instance

Production deployments where latency and server load are critical

Requires

Python 3.9+ with caching libraries (functools.lru_cache or Redis client)

Optional: Redis 6.0+ for distributed caching across multiple server instances

Environment variables: ODOO_CACHE_TTL (default 300s), ODOO_POOL_SIZE (default 5)

Limitations

In-memory cache is process-local; not shared across multiple MCP server instances

Cache invalidation is write-operation-based; external Odoo changes are not detected

Connection pooling adds ~50-100ms overhead for pool initialization

What makes it unique

vs alternatives

xml-rpc authentication and connection management with health checking

Medium confidence

Solves for

Best for

Teams integrating with Odoo instances over unreliable networks

Production deployments requiring high availability and automatic failover

Scenarios with multiple concurrent AI assistants sharing connection pools

Requires

Odoo 14.0+ with XML-RPC enabled (default: enabled)

Valid Odoo user credentials or API token

Network connectivity to Odoo instance (HTTP/HTTPS)

Limitations

Health checks add ~50ms latency per operation; can be disabled for performance

Connection pooling is process-local; not shared across multiple MCP server instances without Redis

API token authentication requires Odoo 14.0+; older versions only support username/password

What makes it unique

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.

multi-protocol transport support (stdio and http) with fastmcp integration

Medium confidence

Solves for

Best for

Teams deploying MCP servers in diverse environments (local, cloud, containerized)

Scenarios requiring both embedded (stdio) and remote (HTTP) access patterns

Production deployments where transport flexibility is critical

Requires

Python 3.9+ with FastMCP library

Environment variable: TRANSPORT (stdio or http, default: stdio)

For HTTP: PORT environment variable (default: 8000)

Limitations

Stdio transport is process-local; not suitable for multi-client scenarios

HTTP transport adds network latency (~50-200ms) vs. stdio

Streaming responses require client support; not all MCP clients support streaming

What makes it unique

vs alternatives

configuration management with environment variable validation and odooconfig class

Medium confidence

Solves for

Best for

Teams deploying MCP servers in containerized/cloud environments

Scenarios requiring different configurations for dev/staging/production

Deployments where secrets management is critical (API tokens, credentials)

Requires

Python 3.9+

Environment variables: ODOO_URL, ODOO_DB, ODOO_USERNAME, ODOO_PASSWORD (or ODOO_API_TOKEN)

Optional: ODOO_CACHE_TTL, ODOO_POOL_SIZE, ODOO_YOLO_MODE, TRANSPORT, PORT

Limitations

Configuration is static at startup; runtime changes require server restart

No built-in secrets management; relies on external tools (Kubernetes secrets, AWS Secrets Manager)

Validation is basic; complex interdependencies between parameters are not checked

What makes it unique

vs alternatives

Cleaner than scattered environment variable access throughout the codebase; centralized validation catches configuration errors early vs. runtime failures.

error handling and exception translation from odoo xml-rpc to structured messages

Medium confidence

Solves for

Best for

Production deployments requiring robust error handling and observability

Teams building autonomous AI agents that need to recover from errors

Scenarios where error context is critical for debugging

Requires

Python 3.9+ with logging module

Odoo 14.0+ with standard exception types

Limitations

Error translation is best-effort; some Odoo exceptions may not map cleanly to structured messages

Logging is process-local; requires external aggregation (ELK, Datadog) for centralized monitoring

No built-in retry logic; AI assistants must implement their own retry strategies

What makes it unique

vs alternatives

dynamic tool registration based on odoo model metadata with schema generation

Medium confidence

Solves for

Best for

Teams with custom Odoo models that change frequently

Deployments where manual schema maintenance is impractical

Scenarios requiring support for multiple Odoo instances with different model sets

Requires

Odoo 14.0+ with ir.model and ir.model.fields tables

Valid Odoo user credentials with read access to ir.model and ir.model.fields

Limitations

Schema generation is static at server startup; new models require server restart

Complex field types (many2many, one2many) are simplified in schema generation

Field constraints (unique, required) are included but not fully validated by the MCP server

What makes it unique

vs alternatives

More flexible than hardcoded tool schemas because it adapts to custom models and field changes; metadata-driven approach reduces maintenance burden vs. manual schema definition.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Claude Code for VS Code48Extension

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Odoo

Capabilities10 decomposed

natural language crud operations on odoo models via mcp tools

uri-based resource access to odoo records with query filtering

permission-aware access control with optional yolo mode bypass

smart field selection and hierarchical data formatting for llm consumption

multi-tier caching system with connection pooling for performance optimization

xml-rpc authentication and connection management with health checking

multi-protocol transport support (stdio and http) with fastmcp integration

configuration management with environment variable validation and odooconfig class

error handling and exception translation from odoo xml-rpc to structured messages

dynamic tool registration based on odoo model metadata with schema generation

Related Artifactssharing capabilities

functional-models-orm-mcp

django-mcp-server

Paperless-MCP

NocoDB

Squad AI

AskYourDatabase

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

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Alternatives to Odoo

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Data Sources

Odoo

Capabilities10 decomposed

natural language crud operations on odoo models via mcp tools

uri-based resource access to odoo records with query filtering

permission-aware access control with optional yolo mode bypass

smart field selection and hierarchical data formatting for llm consumption

multi-tier caching system with connection pooling for performance optimization

xml-rpc authentication and connection management with health checking

multi-protocol transport support (stdio and http) with fastmcp integration

configuration management with environment variable validation and odooconfig class

error handling and exception translation from odoo xml-rpc to structured messages

dynamic tool registration based on odoo model metadata with schema generation

Related Artifactssharing capabilities

functional-models-orm-mcp

django-mcp-server

Paperless-MCP

NocoDB

Squad AI

AskYourDatabase

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Odoo

Are you the builder of Odoo?

Get the weekly brief

Data Sources

Squad AI

Squad AI