Dawn Mcp
v1.0.0A powerful MCP server built with NitroStack
Add via Cursor Settings UI (Settings > Features > MCP > Add New MCP Server):
{
"mcpServers": {
// your other mcp servers
"dawn-mcp": {
"url": "https://dawnmcp-6a6d98d3-dawn-of-ai-srmist.app.nitrocloud.ai/mcp"
}
}
}
Connect remote tools directly via Claude's Web UI:
Configure custom tools directly via ChatGPT's Web UI:
Add the following configuration block under mcpServers in your Antigravity configuration file (~/.gemini/config/mcp_config.json):
{
"mcpServers": {
// your other mcp servers
"dawn-mcp": {
"serverUrl": "https://dawnmcp-6a6d98d3-dawn-of-ai-srmist.app.nitrocloud.ai/mcp"
}
}
}
Add the following configuration block to your Codex configuration file (~/.codex/config.toml):
[mcp_servers.dawn-mcp] url = "https://dawnmcp-6a6d98d3-dawn-of-ai-srmist.app.nitrocloud.ai/mcp"
Connect directly using the Server-Sent Events endpoint:
https://dawnmcp-6a6d98d3-dawn-of-ai-srmist.app.nitrocloud.ai/mcp
Save a piece of information to long-term memory. Use this to remember user preferences, project context, technical decisions, conversations, or important events for future reference.
Search stored memories by semantic similarity. Use this to recall past conversations, find previously stored project context, retrieve technical decisions, or look up user preferences.
Update the content of an existing memory. The embedding is regenerated automatically. Use the memory ID from a previous search result.
Permanently delete a stored memory by its ID. Use the memory ID from a previous search result.
Analyze a local repository to understand its structure, framework, dependencies, and architecture. Scans all files, performs AST parsing on source code, detects patterns, and generates an AI summary.
Scan a local repository and list all files with classified types, languages, and aggregate statistics.
Index a local repository for semantic code search. Scans files, splits them into function/class chunks, generates embeddings using Ollama, and stores them in ChromaDB.
Ask a natural language question about an indexed codebase. Uses RAG (retrieval-augmented generation) over ChromaDB vector search and Qwen2.5-Coder LLM.
Construct a structural knowledge graph of files, functions, classes, and dependencies for a repository.
Query the repository knowledge graph by entity name or type (file, function, class, module, dependency).
Generate a detailed architectural explanation of a repository.
Generate a comprehensive project explanation including architecture, technology stack, and module structure.
Locate where a specific feature or capability is implemented in the codebase.
Analyze potential impact of modifying a specific file or symbol across the codebase.
Get a concise high-level technical summary of the repository.
Decompose a complex software engineering request into structured implementation phases using the PlannerAgent.
Generate production code or step-by-step implementation for a planned step using the ExecutorAgent.
Perform a comprehensive code review auditing security, performance, quality, and best practices using the ReviewerAgent.
Diagnose error logs, stack traces, or failing tests to identify root cause and recommended fix using the DebuggerAgent.
Store and index a project document, spec, or architecture guide for semantic retrieval.
Search stored project documentation by semantic similarity.
List all stored project documents and specs.
Delete a stored document by its UUID.