refactor: enhance system prompt handling and tool integration in LlmChatAgent

server-rs/crates/module-editor-agent/examples/llm_chat_agent.rs

@@ -70,7 +70,7 @@ impl Hook for ToolValidationHook {
 
 struct LlmChatAgentBuilder {
     client: Option<LlmClient>,
-    custom_system_prompt: Option<String>,
+    system_prompt_parts: Vec<String>,
     tools: Vec<Box<dyn ToolDyn>>,
     hooks: Vec<Box<dyn Hook>>,
     max_turns: usize,
@@ -84,7 +84,7 @@ impl AgentBuilder<LlmMessage, LlmCompletionModel> for LlmChatAgentBuilder {
     fn new() -> Self {
         Self {
             client: None,
-            custom_system_prompt: None,
+            system_prompt_parts: Vec::new(),
             tools: Vec::new(),
             hooks: Vec::new(),
             max_turns: 10,
@@ -99,7 +99,7 @@ impl AgentBuilder<LlmMessage, LlmCompletionModel> for LlmChatAgentBuilder {
     }
 
     fn system_prompt(mut self, system_prompt: impl Into<String>) -> Self {
-        self.custom_system_prompt = Some(system_prompt.into());
+        self.system_prompt_parts.push(system_prompt.into());
         self
     }
 
@@ -133,13 +133,23 @@ impl AgentBuilder<LlmMessage, LlmCompletionModel> for LlmChatAgentBuilder {
             client: self.client.expect("call .with_client() first"),
         };
         let mut agent = Agent::new(model);
+        let tool_specs = self
+            .tools
+            .iter()
+            .map(|tool| ToolPromptSpec {
+                name: tool.tool_name().to_string(),
+                description: tool.description(),
+                parameters: tool.parameters(),
+            })
+            .collect::<Vec<_>>();
+        let base_prompt = self.system_prompt_parts.join("\n\n");
+        let system_prompt = build_tools_system_prompt(&base_prompt, &tool_specs);
         agent.tools = self.tools;
         agent.hooks = self.hooks;
         agent.default_max_turns = self.max_turns;
         agent.memory = self.memory_data;
-        if let Some(sp) = self.custom_system_prompt {
-            agent.system_prompt = Some(LlmMessage::system(&sp));
-        }
+        agent.context = self.context;
+        agent.system_prompt = Some(LlmMessage::system(&system_prompt));
         agent
     }
 }
@@ -196,6 +206,12 @@ impl Tool for EchoTool {
 // 6. System prompt builder
 // ---------------------------------------------------------------------------
 
+struct ToolPromptSpec {
+    name: String,
+    description: String,
+    parameters: serde_json::Value,
+}
+
 fn tool_names(agent: &Agent<LlmCompletionModel, LlmMessage>) -> Vec<String> {
     agent
         .tools()
@@ -204,12 +220,12 @@ fn tool_names(agent: &Agent<LlmCompletionModel, LlmMessage>) -> Vec<String> {
         .collect()
 }
 
-fn build_tools_system_prompt(base_prompt: &str, tool_names: &[String]) -> String {
+fn build_tools_system_prompt(base_prompt: &str, tool_specs: &[ToolPromptSpec]) -> String {
     let mut prompt = String::new();
     prompt.push_str(base_prompt);
     prompt.push_str("\n\nYou have access to the following tools.\n\n");
 
-    if tool_names.is_empty() {
+    if tool_specs.is_empty() {
         prompt.push_str("(No tools available.)\n");
     } else {
         prompt.push_str("## JSON Response Format\n");
@@ -220,7 +236,7 @@ fn build_tools_system_prompt(base_prompt: &str, tool_names: &[String]) -> String
         prompt.push_str("  \"reply_text\": \"your message to the user\",\n");
         prompt.push_str("  \"tool_calls\": [\n    {\n");
         prompt.push_str("      \"tool_name\": \"tool_name_here\",\n");
-        prompt.push_str("      \"args\": { /* tool-specific arguments */ }\n");
+        prompt.push_str("      \"args\": { \"argument_name\": \"argument_value\" }\n");
         prompt.push_str("    }\n  ]\n");
         prompt.push_str("}\n\n");
         prompt.push_str("If you don't need to use a tool, respond with:\n");
@@ -230,8 +246,14 @@ fn build_tools_system_prompt(base_prompt: &str, tool_names: &[String]) -> String
         prompt.push_str("}\n\n");
         prompt.push_str("## Available Tools\n\n");
 
-        for name in tool_names {
-            prompt.push_str(&format!("- {name}\n"));
+        for tool in tool_specs {
+            prompt.push_str(&format!("- {}\n", tool.name));
+            prompt.push_str(&format!("  Description: {}\n", tool.description));
+            prompt.push_str("  Arguments JSON Schema:\n");
+            let parameters = serde_json::to_string_pretty(&tool.parameters)
+                .unwrap_or_else(|_| tool.parameters.to_string());
+            prompt.push_str(&indent_multiline(&parameters, "  "));
+            prompt.push_str("\n");
         }
 
         prompt.push_str(
@@ -251,6 +273,13 @@ fn build_tools_system_prompt(base_prompt: &str, tool_names: &[String]) -> String
     prompt
 }
 
+fn indent_multiline(text: &str, indent: &str) -> String {
+    text.lines()
+        .map(|line| format!("{indent}{line}"))
+        .collect::<Vec<_>>()
+        .join("\n")
+}
+
 // ---------------------------------------------------------------------------
 // 7. Config loading via dotenvy
 // ---------------------------------------------------------------------------
@@ -318,16 +347,12 @@ async fn run_chat_agent(config: LlmConfig) -> Result<(), LlmError> {
         .build();
 
     let names = tool_names(&agent);
-    let system_prompt_text =
-        build_tools_system_prompt("You are a helpful assistant with an echo tool.", &names);
-
     let tool_hook = ToolValidationHook::new(names.clone());
 
     let output = agent
         .prompt(LlmMessage::user(
             "Use the echo tool to echo 'Hello from the JSON harness!', then tell me what it said.",
         ))
-        .system_prompt(LlmMessage::system(&system_prompt_text))
         .max_turns(5)
         .add_hook(tool_hook)
         .await