⚡ Create Macro-Driven Tool Discovery System for AI Agent Integration

[avrabe]

⚡ Create Macro-Driven Tool Discovery System for AI Agent Integration

Problem Statement

Based on analysis of the framework and 2025 AI agent integration patterns, there's a gap in intelligent tool discovery and composition:

Current Limitations:

• Tools are static and individually defined
• No automatic tool grouping or workflow discovery
• AI agents must know exact tool names and parameters
• Missing tool relationship mapping for complex workflows
• No support for tool composition patterns recommended in 2025 MCP best practices

AI Agent Integration Challenges:

• "Avoid mapping every API endpoint to a new MCP tool" - current approach does exactly this
• Tool grouping and workflow chaining are manual
• Intentional tool design requires extensive boilerplate
• Discovery metadata for AI agents is insufficient

Motivation

2025 MCP Best Practices Research

"Intentional Tool Design: Avoid mapping every API endpoint to a new MCP tool. Instead, group related tasks and design higher-level functions."

"Workflow Chaining: Use macros and chaining that trigger complex workflows via a single instruction."

AI Agent Requirements

• Self-discovering tools that expose their capabilities
• Semantic tool grouping for related operations
• Workflow composition for multi-step processes
• Intent-based tool selection rather than exact name matching
• Tool relationship mapping for dependency resolution

Framework Competitive Analysis

• Python MCP SDK: Uses decorators with automatic discovery
• Other frameworks: Provide tool composition and semantic grouping
• PulseEngine gap: Missing intelligent discovery layer

Solution Design

1. Enhanced Tool Discovery Macros

#[mcp_tool_group(
    name = "file_operations",
    description = "Complete file management workflow",
    intent = ["file management", "document handling", "storage operations"]
)]
impl FileManager {
    #[mcp_tool(
        workflow_step = "discovery",
        dependencies = [],
        outputs = ["file_list"]
    )]
    pub async fn list_files(&self, path: String) -> Result<Vec<FileInfo>> {
        // Implementation
    }
    
    #[mcp_tool(
        workflow_step = "action", 
        dependencies = ["list_files"],
        outputs = ["processed_file"]
    )]
    pub async fn process_file(&self, file_path: String) -> Result<ProcessedFile> {
        // Implementation  
    }
    
    #[mcp_tool(
        workflow_step = "completion",
        dependencies = ["process_file"],
        outputs = ["workflow_result"]
    )]
    pub async fn finalize_workflow(&self, processed: ProcessedFile) -> Result<WorkflowResult> {
        // Implementation
    }
}

2. Intelligent Tool Discovery

#[derive(Debug, Serialize, Deserialize)]
pub struct ToolDiscoveryInfo {
    pub tool_id: String,
    pub name: String,
    pub description: String,
    pub intent_keywords: Vec<String>,
    pub workflow_group: Option<String>,
    pub dependencies: Vec<String>,
    pub outputs: Vec<String>,
    pub complexity_score: u8,
    pub usage_patterns: Vec<UsagePattern>,
}

pub struct ToolDiscoveryEngine {
    tools: HashMap<String, ToolDiscoveryInfo>,
    workflow_graphs: HashMap<String, WorkflowGraph>,
    intent_index: IntentIndex,
}

impl ToolDiscoveryEngine {
    pub async fn discover_tools_for_intent(&self, intent: &str) -> Result<Vec<ToolRecommendation>> {
        // Semantic matching of intent to available tools
        let candidates = self.intent_index.search(intent);
        
        // Rank by relevance and complexity
        let ranked = self.rank_tools_by_intent(candidates, intent);
        
        // Group into workflows if applicable
        let workflows = self.identify_workflows(&ranked);
        
        Ok(workflows)
    }
    
    pub async fn suggest_tool_chain(&self, starting_tool: &str, desired_outcome: &str) -> Result<Vec<ToolChain>> {
        // Find workflow paths from starting tool to outcome
        let graph = self.workflow_graphs.get(starting_tool)?;
        let paths = graph.find_paths_to_outcome(desired_outcome);
        
        Ok(paths)
    }
}

3. Semantic Tool Composition

#[mcp_workflow(
    name = "data_processing_pipeline",
    description = "Complete data ingestion, processing, and output workflow",
    intent = ["data processing", "ETL", "data pipeline"]
)]
pub struct DataProcessingWorkflow;

impl DataProcessingWorkflow {
    #[mcp_workflow_entrypoint]
    pub async fn execute_pipeline(&self, input: DataInput) -> Result<WorkflowResult> {
        // Auto-generated workflow execution
        let ingested = self.ingest_data(input).await?;
        let processed = self.process_data(ingested).await?;
        let output = self.output_data(processed).await?;
        
        Ok(WorkflowResult {
            data: output,
            workflow_id: self.workflow_id(),
            execution_time: self.execution_duration(),
            tools_used: self.get_tools_chain(),
        })
    }
    
    // Individual steps auto-discovered from tool_group
    async fn ingest_data(&self, input: DataInput) -> Result<IngestedData> { ... }
    async fn process_data(&self, data: IngestedData) -> Result<ProcessedData> { ... }
    async fn output_data(&self, data: ProcessedData) -> Result<OutputData> { ... }
}

4. AI Agent Discovery API

// New endpoint for AI agent tool discovery
#[mcp_discovery_endpoint]
impl DiscoveryHandler {
    pub async fn discover_tools(&self, query: ToolDiscoveryQuery) -> Result<DiscoveryResponse> {
        match query {
            ToolDiscoveryQuery::ByIntent(intent) => {
                self.discovery_engine.discover_tools_for_intent(&intent).await
            }
            ToolDiscoveryQuery::ByCapability(capability) => {
                self.discovery_engine.find_tools_with_capability(&capability).await
            }
            ToolDiscoveryQuery::WorkflowChain { start, end } => {
                self.discovery_engine.suggest_tool_chain(&start, &end).await
            }
        }
    }
    
    pub async fn get_workflow_suggestions(&self, context: WorkflowContext) -> Result<Vec<WorkflowSuggestion>> {
        // AI-friendly workflow recommendations
        self.discovery_engine.suggest_workflows_for_context(context).await
    }
}

5. Auto-Generated Tool Schema with Relationships

// Enhanced tool schema generation
pub struct EnhancedToolSchema {
    pub basic_schema: ToolSchema,
    pub discovery_metadata: ToolDiscoveryInfo,
    pub relationships: ToolRelationships,
    pub ai_hints: AIAgentHints,
}

#[derive(Debug, Serialize)]
pub struct AIAgentHints {
    pub when_to_use: String,
    pub common_patterns: Vec<String>,
    pub related_workflows: Vec<String>,
    pub complexity_guidance: ComplexityGuidance,
}

Implementation Plan

Phase 1: Core Discovery Engine (Week 1)

• Create pulseengine-mcp-discovery crate
• Implement ToolDiscoveryEngine with semantic matching
• Add #[mcp_tool_group] macro for grouping tools
• Create tool relationship mapping system
• Basic intent-based tool discovery

Phase 2: Workflow Composition (Week 2)

• Implement #[mcp_workflow] macro for complex workflows
• Add workflow dependency resolution
• Create workflow execution engine
• Add tool chaining and composition logic
• Implement workflow graph generation

Phase 3: AI Agent Integration (Week 3)

• Create discovery endpoint for AI agents
• Implement semantic tool search API
• Add workflow suggestion system
• Create AI-friendly tool metadata generation
• Add intent keyword indexing

Phase 4: Advanced Features (Week 4)

• Add machine learning-based tool recommendation
• Implement usage pattern analysis
• Create tool performance optimization hints
• Add A/B testing for tool discovery
• Comprehensive documentation and examples

Features to Implement

1. Semantic Tool Grouping

#[mcp_tool_group(
    intent = ["file operations", "document management"],
    complexity = "medium",
    related_groups = ["data_processing", "storage_management"]
)]
impl DocumentManager {
    // Tools automatically grouped and discoverable
}

2. Intent-Based Discovery

// AI agent can query: "I need to process financial data"
let tools = discovery_engine
    .discover_tools_for_intent("financial data processing")
    .await?;

// Returns ranked list of relevant tools and workflows

3. Workflow Composition

// Automatically compose workflows from available tools
let workflow = discovery_engine
    .compose_workflow("data ingestion", "report generation")
    .await?;

// Returns optimized workflow with tool dependencies

4. Tool Relationship Mapping

pub struct ToolRelationship {
    pub relationship_type: RelationshipType, // Prerequisite, Alternative, Complement
    pub related_tool: String,
    pub confidence_score: f32,
    pub usage_frequency: u32,
}

Acceptance Criteria

Discovery System

• Tools can be discovered by intent keywords
• Tool grouping works automatically via macros
• Workflow composition generates valid tool chains
• Tool relationships are accurately mapped
• Performance: <10ms for tool discovery queries

AI Agent Integration

• AI agents can discover tools without knowing exact names
• Workflow suggestions are semantically relevant
• Tool complexity is communicated clearly
• Usage patterns guide AI agent decisions
• Discovery API is well-documented for AI integration

Developer Experience

• Macro-driven approach requires minimal boilerplate
• Tool discovery works out-of-the-box
• Clear documentation with examples
• Migration path from current tool definitions
• Performance impact is negligible

Framework Integration

• Works with existing #[mcp_tools] macro
• Compatible with all transport layers
• Integrates with security middleware
• Maintains backward compatibility
• Extensible for future AI capabilities

Configuration Examples

Simple Tool Discovery

#[mcp_server(name = "Smart Server", discovery = true)]
struct SmartServer;

#[mcp_tool_group(intent = ["data analysis", "statistics"])]
impl DataAnalyzer {
    #[mcp_tool(complexity = "low")]
    pub async fn basic_stats(&self, data: Vec<f64>) -> Result<Stats> { ... }
    
    #[mcp_tool(complexity = "high", depends_on = ["basic_stats"])]  
    pub async fn advanced_analysis(&self, stats: Stats) -> Result<Analysis> { ... }
}

AI Agent Query Examples

// Natural language tool discovery
let response = client
    .post("/mcp/discover")
    .json(&json!({
        "intent": "I need to analyze financial data and generate a report",
        "context": {
            "data_type": "financial",
            "output_format": "report"
        }
    }))
    .send()
    .await?;

// Returns structured workflow recommendations

References & Research

2025 MCP Best Practices

• 7 MCP Server Best Practices for Scalable AI Integrations
• Intentional Tool Design principles
• Workflow Chaining patterns

AI Agent Integration Patterns

• LastMile AI MCP Agent Framework
• Intent-based tool discovery patterns
• Semantic tool composition examples

Current Framework Analysis

• Existing tool macro: mcp-macros/src/mcp_tool.rs
• Tool discovery gaps: Missing semantic layer
• AI agent feedback: "Need better tool discoverability"

Success Metrics

1. Discovery Performance: AI agents find relevant tools in <10ms
2. Workflow Accuracy: 90%+ of suggested workflows are useful
3. Developer Productivity: 50% reduction in tool definition boilerplate
4. AI Integration: 95% of tool discoveries result in successful execution
5. Framework Adoption: Used by 80%+ of new MCP server implementations

This creates a competitive advantage by making PulseEngine MCP the most AI-agent-friendly framework available, addressing the key 2025 requirement for intelligent tool composition.

Priority: High - Key differentiator for AI agent integration
Effort: High - Novel system requiring careful design
Impact: High - Major competitive advantage and AI agent enablement