Convex
MCP Server** - Introspect and query your apps deployed to Convex.
Capabilities8 decomposed
deployment-aware context introspection
Medium confidenceQueries and returns accessible Convex deployments (production, development, preview) with deployment selectors that serve as routing identifiers for all subsequent tool operations. The MCP server maintains a credential-scoped view of deployments, enabling the model to understand which data environments it can access before attempting queries or function calls.
Provides deployment-scoped context routing via selectors, enabling the model to understand and switch between production, development, and preview environments without manual configuration — this is built into the MCP protocol layer rather than requiring explicit environment variable management
Unlike REST API clients that require manual environment switching, Convex MCP automatically exposes all accessible deployments and their selectors, allowing agents to reason about and route to the correct backend without external configuration
schema-aware table enumeration with inferred metadata
Medium confidenceLists all tables in a selected deployment and returns both declared schema (developer-defined) and inferred schema (automatically tracked by Convex's runtime). This enables the model to understand data structure without manual schema documentation, supporting intelligent query construction and data exploration. The dual schema approach allows detection of schema drift or undocumented fields.
Combines declared schema (developer intent) with inferred schema (runtime reality), enabling detection of schema drift and providing automatic type information without requiring developers to maintain separate schema documentation — this dual-layer approach is unique to Convex's runtime tracking architecture
Unlike generic database introspection tools, Convex MCP provides both intended and actual schema, allowing agents to detect and reason about inconsistencies; also avoids the need for separate schema documentation or manual type definitions
paginated document retrieval with table scanning
Medium confidenceRetrieves documents from a specified table with pagination support, allowing the model to iterate through large datasets without loading entire tables into memory. The tool abstracts Convex's document storage layer, returning structured records that can be filtered, analyzed, or used as context for subsequent operations.
Integrates with Convex's document-oriented storage model, providing native pagination over the actual runtime storage layer rather than requiring SQL queries or custom API endpoints — pagination is handled transparently by the MCP server's connection to the Convex backend
Simpler than writing custom Convex query functions for data exploration; avoids the need to deploy temporary functions or use REST APIs; pagination is built into the MCP protocol layer
sandboxed arbitrary query execution with read-only enforcement
Medium confidenceExecutes developer-written or model-generated JavaScript code against a deployment in a fully sandboxed environment that blocks all write operations. The sandbox enforces read-only semantics at the runtime level, preventing accidental or malicious data modification while allowing complex queries, aggregations, and data transformations. Code execution is isolated from the main application runtime.
Provides a fully sandboxed JavaScript execution environment with write-operation blocking enforced at the runtime level, not just through permission checks — this allows safe ad-hoc querying without deploying functions or managing separate query APIs. The sandbox is integrated into the Convex backend's execution layer.
More flexible than table enumeration for complex queries; safer than direct database access because writes are blocked at runtime; avoids the need to deploy temporary functions or use REST endpoints for one-off analysis
function signature introspection with visibility metadata
Medium confidenceLists all deployed functions in a deployment with their type signatures, parameter types, return types, and visibility settings (public, private, internal). This enables the model to understand the function API surface without reading source code, supporting intelligent function selection and parameter construction for the run tool.
Provides runtime function metadata directly from the Convex deployment, including visibility settings and type signatures, without requiring separate API documentation or schema files — this is extracted from the deployed function registry rather than static code analysis
Unlike OpenAPI/GraphQL schema inspection, Convex MCP provides function metadata directly from the runtime, ensuring accuracy with deployed code; avoids the need for separate API documentation or schema generation steps
type-safe deployed function invocation with parameter binding
Medium confidenceExecutes deployed Convex functions with type-checked parameter binding, routing calls through the MCP server to the target deployment. The tool handles parameter serialization, error handling, and return value deserialization, abstracting away the complexity of direct RPC calls. Functions can be mutating or read-only depending on implementation.
Provides direct function invocation through the MCP protocol, allowing agents to call Convex functions without deploying separate API endpoints or managing authentication tokens — the MCP server handles credential routing and parameter serialization transparently
More direct than HTTP REST calls; avoids the need to expose functions via separate API routes; integrates seamlessly with MCP-aware agents that can discover and call functions via functionSpec introspection
multi-agent mcp server deployment with credential scoping
Medium confidenceRuns as an MCP server process that can be connected to multiple AI agents (Cursor, Claude Desktop, Windsurf, etc.) with a single set of Convex credentials. The server maintains credential scope per connection, ensuring agents only access deployments the authenticated user has permissions for. Configuration is managed via MCP client settings (e.g., Cursor's mcp.json).
Provides a single MCP server entry point that can be shared across multiple agents while maintaining credential scoping — agents inherit the server's authentication context rather than managing separate credentials, reducing configuration complexity and improving security
Simpler than configuring separate API keys for each agent; leverages MCP protocol for standardized agent integration; credential scoping ensures agents respect the authenticated user's permission model without additional configuration
development-to-production deployment routing
Medium confidenceSupports querying and executing operations across multiple deployment types (production, development, preview) within a single Convex project. The MCP server routes operations to the correct deployment based on the deployment selector, enabling developers to test against development deployments before running operations on production.
Integrates with Convex's multi-deployment model (one prod, one dev per team member, multiple previews), allowing agents to route operations to the correct environment via deployment selectors — this is built into the Convex project structure rather than requiring external environment management
Avoids accidental production modifications by requiring explicit deployment selection; supports Convex's native dev/prod/preview deployment model without additional configuration
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓teams managing multiple Convex deployments (prod/dev/preview)
- ✓developers integrating Convex introspection into AI-assisted workflows
- ✓agents that need to route queries to the correct backend environment
- ✓developers exploring unfamiliar Convex codebases
- ✓AI agents generating queries without pre-loaded schema documentation
- ✓teams debugging schema drift or data inconsistencies
- ✓rapid prototyping workflows where schema inspection is frequent
- ✓agents building context from production data
Known Limitations
- ⚠read-only operation — cannot create or delete deployments
- ⚠access limited to deployments the authenticated user has credentials for
- ⚠permission model (role-based, team-based, etc.) is undocumented
- ⚠no filtering or search across deployments — returns full list
- ⚠read-only operation — cannot modify or create tables
- ⚠document count per table not exposed (if available)
Requirements
Input / Output
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** - Introspect and query your apps deployed to Convex.
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