Files
gpt/src/mcp/server.rs
2026-02-27 19:49:07 +09:00

193 lines
5.9 KiB
Rust

use anyhow::Result;
use serde_json::{json, Value};
use std::io::{self, BufRead, Write};
use crate::core::{reader, writer};
pub struct MCPServer;
impl MCPServer {
pub fn new() -> Self {
MCPServer
}
pub fn run(&self) -> Result<()> {
let stdin = io::stdin();
let mut stdout = io::stdout();
let reader = stdin.lock();
let lines = reader.lines();
for line_result in lines {
match line_result {
Ok(line) => {
let trimmed = line.trim().to_string();
if trimmed.is_empty() {
continue;
}
if let Ok(request) = serde_json::from_str::<Value>(&trimmed) {
let response = self.handle_request(request);
let response_str = serde_json::to_string(&response)?;
stdout.write_all(response_str.as_bytes())?;
stdout.write_all(b"\n")?;
stdout.flush()?;
}
}
Err(_) => break,
}
}
Ok(())
}
fn handle_request(&self, request: Value) -> Value {
let method = request["method"].as_str().unwrap_or("");
let id = request["id"].clone();
match method {
"initialize" => self.handle_initialize(id),
"tools/list" => self.handle_tools_list(id),
"tools/call" => self.handle_tools_call(request, id),
_ => json!({
"jsonrpc": "2.0",
"id": id,
"error": {
"code": -32601,
"message": "Method not found"
}
}),
}
}
fn handle_initialize(&self, id: Value) -> Value {
json!({
"jsonrpc": "2.0",
"id": id,
"result": {
"protocolVersion": "2024-11-05",
"capabilities": {
"tools": {}
},
"serverInfo": {
"name": "aigpt",
"version": "0.3.0"
}
}
})
}
fn handle_tools_list(&self, id: Value) -> Value {
let tools = vec![
json!({
"name": "read_core",
"description": "Read core.md - the AI's identity and instructions",
"inputSchema": {
"type": "object",
"properties": {}
}
}),
json!({
"name": "read_memory",
"description": "Read memory.md - the AI's accumulated memories",
"inputSchema": {
"type": "object",
"properties": {}
}
}),
json!({
"name": "save_memory",
"description": "Overwrite memory.md with new content",
"inputSchema": {
"type": "object",
"properties": {
"content": {
"type": "string",
"description": "Content to write to memory.md"
}
},
"required": ["content"]
}
}),
json!({
"name": "compress",
"description": "Compress conversation into memory. AI decides what to keep, tool writes the result to memory.md",
"inputSchema": {
"type": "object",
"properties": {
"conversation": {
"type": "string",
"description": "Compressed memory content to save"
}
},
"required": ["conversation"]
}
}),
];
json!({
"jsonrpc": "2.0",
"id": id,
"result": {
"tools": tools
}
})
}
fn handle_tools_call(&self, request: Value, id: Value) -> Value {
let tool_name = request["params"]["name"].as_str().unwrap_or("");
let arguments = &request["params"]["arguments"];
let result = match tool_name {
"read_core" => self.tool_read_core(),
"read_memory" => self.tool_read_memory(),
"save_memory" => self.tool_save_memory(arguments),
"compress" => self.tool_compress(arguments),
_ => json!({
"error": format!("Unknown tool: {}", tool_name)
}),
};
json!({
"jsonrpc": "2.0",
"id": id,
"result": {
"content": [{
"type": "text",
"text": result.to_string()
}]
}
})
}
fn tool_read_core(&self) -> Value {
match reader::read_core() {
Ok(content) => json!({ "content": content }),
Err(e) => json!({ "error": e.to_string() }),
}
}
fn tool_read_memory(&self) -> Value {
match reader::read_memory() {
Ok(content) => json!({ "content": content }),
Err(e) => json!({ "error": e.to_string() }),
}
}
fn tool_save_memory(&self, arguments: &Value) -> Value {
let content = arguments["content"].as_str().unwrap_or("");
match writer::save_memory(content) {
Ok(()) => json!({ "success": true }),
Err(e) => json!({ "error": e.to_string() }),
}
}
fn tool_compress(&self, arguments: &Value) -> Value {
let conversation = arguments["conversation"].as_str().unwrap_or("");
match writer::save_memory(conversation) {
Ok(()) => json!({ "success": true }),
Err(e) => json!({ "error": e.to_string() }),
}
}
}