PRD.md•9.32 kB
# Advanced MCP Server — PRD & System Architecture
## 1. Product Requirements Document (PRD)
### Overview
The Advanced MCP (Model Context Protocol) Server is designed to provide AI coding agents with a secure, flexible, and extensible environment for executing coding tasks. It allows multiple AI coders to connect, negotiate capabilities, run commands, access resources, and collaborate in a sandboxed, controlled environment.
### Objectives
* Provide a standardized protocol for AI coders to interact with development environments.
* Securely isolate execution through sandboxing (Docker-based).
* Enable extensibility via plugins/tools.
* Support multi-agent collaboration.
* Ensure auditability and safety through policy controls.
### Key Features
1. **Session Management** – Initialize and negotiate capabilities between AI and MCP server.
2. **Resource Management** – Secure file operations and persistent storage APIs.
3. **Tool Execution** – Run commands or tools in sandboxed environments with streaming logs.
4. **Policy Engine** – Enforce permissions, audit logs, and sandbox constraints.
5. **Multi-Agent Collaboration** – Allow multiple AI coders to connect to the same workspace.
6. **Extensible Tooling** – Support dynamically loadable tools (linters, compilers, frameworks).
---
## 2. System Architecture
```mermaid
graph TD
A[AI Coder Client] -->|MCP/gRPC/WebSocket| B[MCP Gateway]
B --> C[Session Manager]
B --> D[Resource Manager]
B --> E[Execution Manager]
B --> F[Policy Engine]
E --> G[Sandbox Runtime - Docker]
C --> H[Audit Log]
D --> H[Audit Log]
E --> H[Audit Log]
F --> H[Audit Log]
G --> I[Workspace Storage]
```
### Components
* **MCP Gateway** – Accepts connections (gRPC + WebSocket for streaming).
* **Session Manager** – Handles authentication, session lifecycle, and capability negotiation.
* **Resource Manager** – File system abstraction with policy enforcement.
* **Execution Manager** – Runs commands/tools inside sandboxed runtimes (Docker).
* **Policy Engine** – Enforces access rules and maintains audit logs.
* **Audit Log** – Immutable logging system for accountability.
* **Sandbox Runtime** – Docker containers with controlled resources.
* **Workspace Storage** – Persistent project storage (bind-mounted or virtual FS).
---
## 3. API Surface
### Session APIs
* `InitSession(request: Capabilities) -> SessionInfo`
* `ListCapabilities() -> Capabilities`
### Resource APIs
* `GetFile(path) -> FileContent`
* `WriteFile(path, content) -> Status`
* `ListFiles(path) -> FileList`
### Execution APIs
* `RunCommand(command, args, options) -> Stream<Log|Result>`
* `CancelExecution(executionId) -> Status`
### Policy APIs
* `GetPolicies() -> PolicyList`
* `SetPolicy(policy) -> Status`
### Collaboration APIs
* `JoinWorkspace(workspaceId) -> SessionInfo`
* `BroadcastMessage(message) -> Ack`
---
## 4. Security Model
* **Sandbox Isolation** – Each AI coder runs commands in a separate Docker container.
* **Resource Policy** – Explicit allowlist for file operations and commands.
* **Auditability** – All actions logged with session ID, timestamp, and parameters.
* **Capability Negotiation** – Clients must declare supported tools/capabilities.
* **Rate Limiting** – Prevent misuse by throttling command execution.
---
## 5. Data Models
```ts
interface SessionInfo {
sessionId: string;
capabilities: string[];
}
interface FileContent {
path: string;
content: string;
}
interface ExecutionResult {
executionId: string;
exitCode: number;
logs: string[];
}
interface Policy {
id: string;
rules: string[];
}
```
---
## 6. Roadmap
### Phase 1 (MVP)
* Session management
* File read/write
* Command execution in sandbox
* Basic logging
### Phase 2
* Policy engine
* Multi-agent collaboration
* Extensible tooling system
### Phase 3
* Advanced audit + monitoring
* SDKs (JS/Python)
* CLI for developers
---
## 7. Detailed API Spec
### OpenAPI Spec (YAML Skeleton)
```yaml
openapi: 3.0.3
info:
title: MCP Server API
version: 1.0.0
servers:
- url: http://localhost:8080
paths:
/session/init:
post:
summary: Initialize a session
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/Capabilities'
responses:
'200':
description: Session info
content:
application/json:
schema:
$ref: '#/components/schemas/SessionInfo'
components:
schemas:
Capabilities:
type: object
properties:
tools:
type: array
items:
type: string
SessionInfo:
type: object
properties:
sessionId:
type: string
capabilities:
type: array
items:
type: string
```
### gRPC Proto Skeleton
```proto
syntax = "proto3";
service MCPServer {
rpc InitSession(Capabilities) returns (SessionInfo);
rpc ListCapabilities(Empty) returns (Capabilities);
rpc GetFile(FileRequest) returns (FileContent);
rpc WriteFile(FileContent) returns (Status);
rpc RunCommand(CommandRequest) returns (stream ExecutionLog);
rpc CancelExecution(ExecutionId) returns (Status);
}
message Capabilities {
repeated string tools = 1;
}
message SessionInfo {
string sessionId = 1;
repeated string capabilities = 2;
}
message FileRequest {
string path = 1;
}
message FileContent {
string path = 1;
string content = 2;
}
message CommandRequest {
string command = 1;
repeated string args = 2;
}
message ExecutionLog {
string executionId = 1;
string logLine = 2;
}
message ExecutionId {
string executionId = 1;
}
message Status {
bool success = 1;
string message = 2;
}
```
---
## 8. Prototype Server Scaffold (TypeScript)
```ts
import express from 'express';
const app = express();
app.use(express.json());
// Session
app.post('/session/init', (req, res) => {
const sessionId = Date.now().toString();
res.json({ sessionId, capabilities: req.body.tools });
});
// Resource
app.get('/files/:path', (req, res) => {
// TODO: sandboxed FS read
res.json({ path: req.params.path, content: "// file content" });
});
// Execution
app.post('/execute', (req, res) => {
// TODO: sandboxed exec
res.json({ executionId: "exec-123", exitCode: 0, logs: ["started", "done"] });
});
app.listen(8080, () => console.log("MCP Server running on :8080"));
```
---
## 9. AI Development Requirements
For an AI to autonomously develop and maintain the MCP server, it needs:
### Environment Configuration
* **Dev containers**: Dockerfile + `docker-compose.yml`.
* **Pinned toolchain**: Node.js + TypeScript versions.
* **Preconfigured tooling**: ESLint, Prettier, Jest.
* **Git hooks**: enforce lint/tests before commit.
### Coding Conventions & Guidelines
* Style guide: naming, error handling, folder structure.
* Documentation: JSDoc/TSDoc required for all functions.
* Security rules: deny-by-default FS access, no external net by default.
* “Definition of Done”: feature is complete only when tested + documented.
### Testing Framework & Test Cases
* Jest/Mocha for unit tests.
* Integration test harness simulating client ↔ server ↔ sandbox.
* Pre-seeded tests (e.g., `RunCommand` returns logs within timeout).
* Coverage minimum: 80%.
### Release & Distribution
* Semantic versioning (SemVer).
* CI/CD pipelines (GitHub Actions/GitLab CI).
* SDK auto-build + publish.
* Documentation auto-generated from OpenAPI + gRPC.
### Feedback & Self-Improvement
* Error log collection with structured JSON.
* GitHub issue templates for bugs/features.
* AI self-review process: analyze logs, propose patches.
### Seed Examples & Recipes
* Prebuilt workflows:
* Bootstrap a Next.js app.
* Refactor existing repo.
* Run & fix tests.
* These act as **ground truth scenarios** for validation.
---
## 10. AI Meta-Rules & Workflow Context
This defines **how the AI should behave** while building and maintaining the MCP server.
### Workflow Rules
* Always follow PRD + Roadmap as the **single source of truth**.
* Break work into **small, testable increments**.
* Each increment must include: code, tests, documentation, verification.
* Never leave dead code, TODOs, or partial implementations.
### Decision Rules
* If spec conflict arises → default to **security & simplicity**.
* If multiple valid approaches exist → prefer **standards (OpenAPI, gRPC, Docker)**.
* If stuck → draft a mini design doc before coding.
### Collaboration Model
* Treat development as a **team process**: open issues, write PRDs for new features, clear commit messages.
* Each change must include code + tests + docs + changelog update + security check.
### Governance
* **Deny by default**: no unrestricted network or FS access.
* **Audit everything**: every action logged with session ID and timestamp.
* **Traceability**: each feature links back to a PRD or roadmap item.
### Context Flow for AI
1. Load PRD + Roadmap + Dev Requirements.
2. Inspect current repo state.
3. Select next roadmap task.
4. Plan → implement → test → document.
5. Verify against “Definition of Done.”
6. Commit, push, release milestone if applicable.