planned

Mission Run

Codex Mission Control: MCP Server + Next.js Control Plane + Antigravity Integration

Design an MCP server that exposes project, task, documentation, and execution-context tools, then integrate it with Antigravity so users can inspect repositories, trigger mission workflows, retrieve generated PRDs and technical designs, and preserve execution memory across AI coding sessions.

Created: 14 Jun 2026, 10:30 am

Updated: 14 Jun 2026, 10:30 am

Repository Context

New project from scratch. Build a Next.js control plane with an MCP server layer, local persistence, OpenAI-powered document generation, and a clear integration guide for Antigravity-compatible MCP clients.

Constraints

Do not generate code directly for users. Focus on MCP tool design, secure API-key handling, traceable mission documents, local caching, and a polished demo flow for judges.

AI Software Execution Operating System

Primary users

Founders, product leads, architects, delivery teams, and AI-native engineering teams that need a system of record between idea and execution.

Problem solved

It transforms mission intelligence into PRDs, technical designs, engineering plans, AI execution packs, architecture maps, risk models, and traceable workflows.

Product Flow Diagram

Idea to execution intelligence

User

Submits a software idea and constraints

Mission Plan

Tasks, dependencies, owners, risks

Document Studio

PRD, TDD, engineering plan, AI pack

Mission Memory

Reusable organizational knowledge

Architecture Diagram

System components for this idea

Users

Founders + product teams

Edge

Route 53 / CDN / WAF boundary

Application VPC

Web App

Next.js App Router

API Server

TypeScript

Worker

Mission/document generation jobs

Cache

Saved docs + fast reloads

Data Plane

Primary DB

Local JSON for prototype, SQLite/PostgreSQL for production knowledge storage.

Object Storage

Generated docs, exports, artifacts

missionstasksartifactsdecisionsrisks

AI + Operations

OpenAI API

PRD, TDD, plan, AI pack

Observability

Logs, risks, decision trail

IAM / Secrets

Server-side keys + access control

Alerts

Execution and risk signals

Cloud Diagram

Deployment-ready shape

ChannelExperienceMiddlewareResources

Browser

User session

Mission UI

Dashboard + document studio

API Routes

Validate + orchestrate

Storage

Missions + cached docs

AI Client

Codex / external tools

AI Pack

Portable execution context

Doc Engine

Timeout + local fallback

OpenAI

Optional enrichment

Security posture

Keep API keys server-side, validate payloads, and preserve audit logs.

3 risk signals

Risks become visible before execution moves to tools.

Mission Document Studio

Export mission intelligence

Turn the mission into production-ready documents for executives, engineers, delivery teams, and external AI execution tools.

Choose a document type to generate an export-ready artifact.

Traceability Map

Why every task exists

This replaces vague “AI said so” planning. Each path shows which goal, requirement, task, architecture choice, or risk explains the work.

goal

requirement

Define scope, success criteria, and demo storyline

task

Define scope, success criteria, and demo storyline

requirement

Draft MCP tool taxonomy and capability matrix

task

Draft MCP tool taxonomy and capability matrix

requirement

Security and secret handling design

task

Security and secret handling design

requirement

Local persistence and caching architecture

task

Local persistence and caching architecture

requirement

Execution memory model and session continuity

task

Execution memory model and session continuity

requirement

MCP server interface specification (transport, discovery, tool schemas)

task

MCP server interface specification (transport, discovery, tool schemas)

requirement

OpenAI-powered document generation design (traceable + reproducible)

Trace paths

Kept because traceability is the product moat; renamed from relationships for clarity.

Define scope, success criteria, and demo storylinesatisfied byDefine scope, success criteria, and demo storyline

Draft MCP tool taxonomy and capability matrixsatisfied byDraft MCP tool taxonomy and capability matrix

Define scope, success criteria, and demo storylineunblocksDraft MCP tool taxonomy and capability matrix

Security and secret handling designsatisfied bySecurity and secret handling design

Define scope, success criteria, and demo storylineunblocksSecurity and secret handling design

Draft MCP tool taxonomy and capability matrixunblocksSecurity and secret handling design

Mission Decision Log

Explain the important choices

Use Mission Control as the documentation system of record

The mission needs traceable planning artifacts before execution moves into external tools.

Tradeoffs

Improves clarity and handoff quality, but requires users to maintain mission context.

Alternatives

Unstructured chat logs, standalone docs, tickets, or ad hoc planning notes.

Mission Memory

Reuse organizational knowledge

Compare MissionsPlanned capability, not shown as an executable action yet. Current executable memory action is duplicate.

Mission Steps

Task Timeline

8 tasks

Task 1

Define scope, success criteria, and demo storyline

queued

Clarify must-have vs. nice-to-have features for the MCP server and Next.js control plane. Define the judge-facing demo flow (start-to-finish) and measurable acceptance criteria: repository inspection, mission workflow triggering, PRD/tech design generation, and cross-session memory preservation. Establish non-goals (no direct code generation for users) and confirm local-first persistence requirements.

Owner: plannerDependencies: 0

Task 2

Draft MCP tool taxonomy and capability matrix

queued

Design the full set of MCP tools grouped into: project tools, task tools, documentation tools, and execution-context tools. For each tool define: name, purpose, inputs/outputs schema, error model, permissions, and expected latency. Map each tool to the demo storyline and to Antigravity client capabilities (discoverability, invocation, and result rendering).

Owner: builderDependencies: 1

Task 3

Security and secret handling design

queued

Define secure handling for API keys (OpenAI) and any repo access tokens (if applicable). Specify storage strategy (local encrypted storage or OS keychain abstraction), environment variable policy, redaction rules in logs, and audit trail requirements. Define how MCP requests are authenticated/authorized when called from Antigravity-compatible clients (e.g., local-only transport, signed session tokens, or loopback-only server).

Owner: builderDependencies: 2

Task 4

Local persistence and caching architecture

queued

Design local data model for: projects, tasks, documents (PRD/tech design/mission logs), repository metadata snapshots, and execution memory. Choose persistence (SQLite recommended) and caching rules (content-addressed or timestamped snapshots). Define retention, indexing, and how to trace every generated document back to inputs, tool calls, and model parameters (document lineage).

Owner: builderDependencies: 2

Task 5

Execution memory model and session continuity

queued

Specify how execution-context tools store and retrieve memory across AI coding sessions: mission objectives, decisions, open questions, constraints, previous tool outputs, and summaries. Define schemas for: memory entries, run sessions, and linking memory to tasks/documents. Include controls for user review, pinning, and forgetting (selective deletion) for privacy and correctness.

Owner: builderDependencies: 2

Task 6

MCP server interface specification (transport, discovery, tool schemas)

queued

Produce a concrete MCP server spec: transport choice (stdio vs HTTP/SSE depending on Antigravity expectations), tool discovery behavior, JSON schemas for each tool, pagination patterns, deterministic IDs, and standardized error codes. Define a minimal set of example requests/responses for each tool (no code), including edge cases and permission failures.

Owner: builderDependencies: 4

Task 7

OpenAI-powered document generation design (traceable + reproducible)

queued

Design the document generation pipeline for PRDs and technical designs: prompt templates, required context inputs, citation of sources (repo files, issues, tasks), and reproducibility metadata (model name, temperature, timestamp, tool-call references). Include guardrails to avoid generating code directly for users, focusing on structured specs, checklists, and implementation guidance.

Owner: builderDependencies: 3

Task 8

Next.js control plane UX plan (judge-ready)

queued

Design the UI/UX of the control plane: project selector, repo inspection view, mission workflow runner, document viewer with lineage, memory timeline, and settings (API key management, retention). Define a polished demo mode: seeded sample repo, one-click workflow run, and visual traces of tool calls and outputs.

Owner: builderDependencies: 4

Audit Trail

Execution Log

1 logs

Mission plan generated successfully.

success

14 Jun 2026, 10:30 am

Mission Outputs

Artifacts

1 artifacts

Mission Plan

plan

This mission builds a new-from-scratch Codex Mission Control system: an MCP server layer paired with a Next.js control plane, local persistence, OpenAI-assisted document generation, and a first-class integration guide for Antigravity-compatible MCP clients. Execution starts by locking scope and a judge-oriented demo storyline (Task 0), then designing the MCP tool taxonomy (Task 1) around four pillars: project, task, documentation, and execution-context tools. Security is designed early (Task 2) to ensure API keys and any tokens are handled safely, with clear redaction rules and local-only access assumptions. With the tool set defined, the plan establishes a local persistence and caching architecture (Task 3) plus an execution memory model (Task 4) so mission context survives across AI sessions. These feed into a concrete MCP interface specification (Task 5) with precise tool schemas, deterministic IDs, error codes, and example request/response shapes. Document generation is then designed as a traceable pipeline (Task 6): PRDs and technical designs include lineage metadata tying outputs to inputs, tool calls, and model settings, while explicitly avoiding direct code generation. In parallel, the Next.js control plane UX is planned for a polished demo (Task 7), emphasizing transparent traces: tool calls, artifacts, and memory entries are visible and navigable. Antigravity integration is treated as a deliverable (Task 8) with a compatibility checklist and troubleshooting guidance. Mission workflows (Task 9) provide deterministic, callable end-to-end sequences (inspect repo → plan → generate docs → persist memory) that are safe, auditable, and idempotent when feasible. Finally, observability (Task 10) ensures every interaction is inspectable and secrets are protected, and packaging/demo hardening (Task 11) ensures a reliable judge experience: quick setup, seeded demo path, graceful handling of missing keys or rate limits, and a clear runbook. The outcome is a practical MCP server and control plane design that integrates cleanly with Antigravity clients and demonstrates traceable, persistent mission execution without generating code directly for users.

Run the mission to generate executive summary, risks, architecture, database, and handoff guidance.