Dev intelligence layer: 7 MCP tools for graph-powered codebase reasoning.
Dev intelligence layer: 7 MCP tools for graph-powered codebase reasoning.
def score_application(app): decision = model.predict(app) # your deployed AI, untouched gov.attest( # <-- the one added line domain="loan", model_id="credit-risk-v4", inputs={"applicant_ref": gov.pseudonymize_ref(app.id)}, # PII-safe output={"decision": decision.label, "reason_code": decision.reason}, ) return decision ```
A governance-led multi-agent reasoning system for code, with a built-in cryptographic audit substrate for the AI you ship to production. Scan any codebase into a persistent knowledge graph. Every module becomes a reasoning agent. Agents decompose, debate, and synthesize answers with clearance-level governance. Every change — and every production decision — is impact-analysed, gate-checked, and cryptographically committed.
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{
"mcpServers": {
"graqle": {
"command": "uvx",
"args": ["graqle"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Read, search and edit files inside allowed directories with the Model Context Protocol reference Filesystem server.
Retrieve version-aware library documentation and code examples with Context7 MCP, using resolve-library-id and query-docs.
This is a Next.js-based application that provides an MCP (Model Context Protocol) server with OAuth 2.1 authentication support. It is intended as a
An MCP (Model Context Protocol) server that provides a powerful tool to perform code reviews using various Large Language Models (LLMs). This server is designed to be seamlessly integrated with AI…
Add to `~/.gemini/settings.json` (user scope) or `.gemini/settings.json` (project scope):
WildFly MCP server and other tooling to integrate WildFly in AI space