Jev Context MCP
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Jev Context MCPRead context from src/auth.ts and check the latest output before routing."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Jev Context MCP
A local Model Context Protocol server that uses TypeSafe Jev for bounded routing and evidence judgments. Codex keeps responsibility for source retrieval, permissions, tool execution, tests, and substantive review. Jev selects from registered choices; it does not run arbitrary commands or switch the model of an already running Codex task.
The package offers triage_log, read_log_range, select_file_context, read_file_context, check_file_output, and route_task. Registered workflow mode adds prepare_work_packet, run_workflow_stage, get_run_report, and decide_computer_step. The default configuration leaves registered workflow mode disabled.
Install
Requires a recent Node.js version, Codex CLI for the optional native runner, and a TypeSafe API key for live Jev decisions. Without a key, the local evidence tools and deterministic fallback remain available.
npm ci
Copy-Item settings.example.json settings.jsonEdit the untracked settings.json to add only the absolute allowedRoots and contextRoots you want the server to read. The sample grants no file roots. Set TYPESAFE_API_KEY in the MCP process environment using your own secret manager; do not commit a key or put it in settings.json. On Windows, launch.mjs can optionally read a current-user DPAPI credential file configured locally as credentialFile, but that file must stay outside the repository.
Register the MCP server with an absolute path to this checkout:
codex mcp add jev-context -- node C:\absolute\path\to\jev-context-mcp\launch.mjsRestart the Codex MCP connection after registration. Run npm test to check the local code without making paid Jev calls.
Related MCP server: LightWorker
Using the tools
File tools resolve requested paths inside the configured roots and return bounded, redacted excerpts with source hashes. A changed file requires a new selection before range retrieval. Optional TypeSafe sharing is controlled per call with shareWithTypeSafe; leave it false for private material. Redaction is best effort, so inspect the material before authorizing external sharing.
check_file_output returns atomic scores, not a code review. route_task recommends a supported model, effort, skill references, and workflow profile but cannot change the active Codex task. decide_computer_step accepts a fresh text observation and caller-registered routine action IDs; it never supplies selectors, coordinates, typing, commands, or a visual judgment. The caller must perform and verify any selected action.
For advanced native execution, register a task manifest and its SHA-256 in your private settings.json, provide local artifact and limiter directories, and enable the workflow scheduler. The native hook accepts only jev: workflow=<registered-id>; all other prompts remain in their current task. Review the manifest's source roots, mutable files, tests, permissions, request limits, and review requirements before enabling it. The hook is optional and subject to Codex's own trust controls. The native runner persists separate builder and reviewer Codex tasks and reconciles their usage from completed sessions.
Public-release boundaries
This repository contains source, schemas, an empty skill catalog, examples, and local tests. It omits credentials, personal settings, local skill inventories, private logs, benchmark transcripts, generated candidates, and machine-specific installers. settings.json, .env*, *.dpapi, caches, and run artifacts are ignored. Do not loosen those exclusions when adding your own configuration.
No universal token or billing saving is claimed. Jev provider usage, Codex raw tokens, cached input, and quality gates must be measured separately on matched complete tasks. Jev's typed output is not a correctness guarantee; missing evidence, failed tests, and consequential decisions stay with Codex and deterministic checks.
Licensed under MIT. TypeSafe Jev and Codex are separate services with their own terms and usage accounting.
This server cannot be deployed
Maintenance
Related MCP Connectors
Pre-execution governance for AI agents. Deterministic PASS/FAIL/REVIEW verdicts, replayable proof.
A paid remote MCP for OpenAI Codex agent coordination MCP, built to return verdicts, receipts, usage
Machine-native research commons for agent evidence, discovery, rooms, and bounded research quests.
Securely search and manage workspace context files for AI agents and teams.
Related MCP Servers
- AlicenseBqualityCmaintenanceA production-packaged Model Context Protocol server for coding agents that routes large file, git, web, database, and other tasks through token-budgeted tools and workflows.62 npm1MIT
- AlicenseAqualityAmaintenanceLocal-first multi-agent delegation and approval control for Codex via MCP, with persistent task DAG, isolated worktrees, and a web console.141MIT
- AlicenseAqualityBmaintenanceProvides evidence-backed decision support for Codex by indexing local task history, assigning independent councillors, and verifying historical claims against local evidence.6MIT
- AlicenseNot gradedqualityBmaintenanceA local Model Context Protocol server that helps Codex turn requests into atomic tasks, manage plans, and rigorously verify completions without using any model API.7 npm1MIT