CodeAlive MCP: Deepest Context Engine for your projects (especially for large codebases)

Connect your AI assistant to CodeAlive's powerful code understanding platform in seconds!

This MCP (Model Context Protocol) server enables AI clients like Claude Code, Cursor, Claude Desktop, Continue, VS Code (GitHub Copilot), Cline, Codex, OpenCode, SourceCraft Code Assistant, SourceCraft CLI, Zed, KodaCode, GigaCode, Qwen Code, Gemini CLI, Roo Code, Goose, Kilo Code, Windsurf, Kiro, Qoder, n8n, and Amazon Q Developer to access CodeAlive's advanced semantic code search and codebase interaction features.

What is CodeAlive?

CodeAlive is a Context Engine for large codebases, powered by graph-based retrieval and exposed through MCP. It gives AI agents like Cursor, Claude Code, Codex, and other MCP-compatible tools precise repository context instead of forcing them to read files blindly. In our RepoQA benchmark, CodeAlive + Qwen3.6 deep reached frontier-agent quality at ~25x lower model cost, and semantic search reduced captured tokens by 45%.

It's like Context7, but for your (large) codebases.

It allows AI-Coding Agents to:

• Find relevant code faster with semantic search
• Understand the bigger picture beyond isolated files
• Provide better answers with full project context
• Reduce costs and time by removing guesswork

🛠 Available Tools

Once connected, you'll have access to these powerful tools:

1. get_data_sources - List your indexed repositories and workspaces
2. semantic_search - Canonical semantic search across indexed artifacts
3. grep_search - Exact literal or regex text search inside file content, plus literal file-name/path matching (returns files like Form.xml even when their content never mentions the name), with line-level previews for content matches
4. get_repository_ontology - Get repository-level orientation for one selected repository
5. get_file_tree - Inspect a bounded file tree for one repository
6. read_file - Read a repository-relative file path, optionally with a line range
7. fetch_artifacts - Load the full source for relevant search hits (missing or inaccessible identifiers are reported back, not silently dropped)
8. get_artifact_relationships - Expand call graph, inheritance, and reference relationships for one artifact
9. get_artifact_query_schema - Inspect supported ArtifactQuery entities, fields, and examples
10. query_artifact_metadata - Run read-only metadata analytics across selected repositories
11. chat - Stateless, slower synthesized codebase Q&A; call only when explicitly requested

🎯 Usage Examples

After setup, try these commands with your AI assistant:

• "Show me all available repositories" → Uses get_data_sources
• "Find authentication code in the user service" → Uses semantic_search
• "Find the exact regex that matches JWT tokens" → Uses grep_search
• "Explain how the payment flow works in this codebase" → Usually starts with semantic_search/grep_search, then optionally uses chat

semantic_search and grep_search should be the default tools for most agents. chat is a slower stateless synthesis fallback that can take substantially longer than retrieval, and is usually unnecessary when an agent can run a multi-step workflow with ontology, search, fetch/read, relationships, ArtifactQuery, and local file reads. If your agent supports subagents, the highest-confidence path is to delegate a focused subagent that orchestrates semantic_search and grep_search first.

📚 Agent Skill

For an even better experience, install the CodeAlive Agent Skill alongside the MCP server. The MCP server gives your agent access to CodeAlive's tools; the skill teac

... View full README on GitHub