About Agent Readiness
Agent Readiness is a local MCP server for read-only audits of repository readiness for AI coding agents and of agent context files. It runs offline without an LLM, does not execute repository scripts, and redacts credentials in findings.
Key features
- 01Score repository readiness across categories
- 02Check AGENTS.md, CLAUDE.md, and other context files
- 03Return ordered issues and recommendations
- 04Audit offline without running repository scripts
- 05Restrict audited paths and isolate worker processes
Use cases
- 01Check whether a repository has context for coding agents
- 02Review agent rules for safety and quality issues
- 03Audit a repository without running project scripts
- 04Retrieve full audit reports from the session
MCP support
It exposes three read-only MCP tools: audit repository readiness, audit agent context, and retrieve further pages of the current session's reports.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README@alipajand/agent-readiness-mcp
A local Model Context Protocol server that audits repositories for AI coding agents with two deterministic engines:
- agent-readiness-kit: a 0–100 readiness score across 13 categories (instructions, architecture, workflow, testing, safety, and more) for Node.js, Python, Go, and Rust projects.
- agent-context-doctor: quality and safety issues in
AGENTS.md,CLAUDE.md, Cursor and Copilot rules, and agent MCP/settings files. Credentials quoted in any finding are redacted.
The audit is read-only and offline. It uses no LLM and no API key, sends no telemetry, and never runs the audited repository's scripts. Both engines are compiled into the package from pinned commits, so it has no runtime dependencies.
Tools
| Tool | Returns |
|---|---|
audit_repository | Readiness score, categories, checks that did not pass (fail before warn), recommendations |
audit_agent_context | Context score and grade, files checked, issues ordered high → low |
get_audit_report | Further pages of a report from this session, optionally with the engine's complete JSON |
audit_repository and audit_agent_context take an optional path (default: the first root). All
three tools are annotated read-only.
Install
Requires Node.js 22.13 or later (23.5 or later on Node.js 23). Older releases have no stable permission model for the audit sandbox described below, and the server refuses to start on them.
npm install -g @alipajand/[email protected]
Configure a client
The server only audits directories inside its roots. Give one --root per directory the client
may audit; with none, it uses the directory it was started in.
Claude Code:
claude mcp add agent-readiness -- agent-readiness-mcp --root /path/to/projects
Cursor (.cursor/mcp.json) and other clients that use the mcpServers format:
{
"mcpServers": {
"agent-readiness": {
"command": "agent-readiness-mcp",
"args": ["--root", "/path/to/projects"]
}
}
}
Without a global install, use "command": "npx" with
"args": ["-y", "@alipajand/[email protected]", "--root", "/path/to/projects"]. Keep the
version pinned.
Options: --root <dir> (repeatable), --timeout <seconds> (default 120), --max-concurrency <n>
(default 2).
Security
- Paths are resolved through symlinks and must stay inside a root.
- Each audit runs in a separate Node.js process started without a shell, with an empty environment, a time limit, and an output limit. That process runs under Node's permission model: it can read only the audited directory and its own script, and cannot write files or start processes. Node lets a granted read follow a symlink, so the engines' own symlink checks still contain reads; the sandbox is a second layer.
- Stdout carries only MCP messages; diagnostics go to stderr.
- Reports are kept in memory for the session (the 20 most recent) and never written to disk.
- Text in results comes from the audited repository and should be treated as data.
- The server is offline, but the MCP host is not part of it: the host's model reads the results and the host may send them, or files it reads itself, to its model provider. Credential masking covers the server's results only.
Listings
Listed on MCPRush.
License
MIT. Bundled third-party licenses are in dist/THIRD-PARTY-LICENSES.txt and
dist/audit-worker.THIRD-PARTY-LICENSES.txt.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.