codebase-memory-mcp vs MCP Bridge — Connect any API to any AI agent: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of codebase-memory-mcp and MCP Bridge — Connect any API to any AI agent — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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codebase-memory-mcp
DeusData
High-performance MCP server that indexes codebases into a persistent knowledge graph for sub-millisecond structural queries by AI coding agents.
Key features
- Fast Full Indexing: Indexes an average repo in milliseconds and 28M-line codebases in minutes.
- Sub-Millisecond Queries: Answers structural code queries in under 1ms from a persistent knowledge graph.
- Tree-sitter Parsing: High-quality AST analysis across 158 programming languages.
- Hybrid LSP: Adds semantic understanding via LSP integration for 9 languages.
- Single Static Binary: Ships dependency-free for macOS, Linux, and Windows with a simple install.
- MCP Integration: Exposes code intelligence to AI agents through the Model Context Protocol.
Best for
- Agent Code Memory: Give an AI coding agent persistent, queryable memory of a large codebase.
- Large Repo Navigation: Answer structural questions instantly across millions of lines of code.
- Cross-Language Analysis: Parse and query polyglot repositories spanning many languages.
- Faster Refactoring: Let agents locate symbols and dependencies quickly before making changes.
- Onboarding Assistants: Help agents explain unfamiliar codebases through graph-based context.
MCP Bridge — Connect any API to any AI agent
AppFactor
Auto-generate MCP tool definitions from REST, GraphQL, SOAP, or gRPC APIs to connect any API to any AI agent, self-hosted and production-ready.
Key features
- Schema Import: Supports OpenAPI (JSON/YAML), GraphQL introspection, WSDL (SOAP) and gRPC (server reflection or .proto files) via URL, paste, or file upload to onboard APIs without code changes.
- Auto-generated MCP Tools: Converts each API operation into a fully typed MCP tool with input/output schemas, parameter mappings, descriptive documentation, and behavioural annotations for accurate agent discovery and invocation.
- Runtime Validation & Mapping: Validates inputs against generated schemas, maps parameters and authentication details, and forwards requests to backend services while preventing malformed calls.
- Response Post-processing: Normalizes and trims API responses to reduce token consumption and produce agent-friendly outputs, improving cost-efficiency and relevance when used by LLMs.
- Authentication & Governance: Centralizes handling of API authentication, rate limiting, and access controls so agents call services securely without shipping credentials or custom glue code.
- High-performance Rust Core: Built in Rust for memory safety and high throughput to support production-scale deployments with minimal runtime dependencies.
- Deployability & Marketplaces: Self-hosted in minutes with availability via AWS Marketplace and Microsoft Azure Marketplace, enabling enterprise deployment patterns and marketplace procurement.