---

name: mcp-builder compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent mcp builder with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: mcp-builder, mcp builder, how do i mcp-builder, orchestrate mcp-builder, automate mcp-builder, agent mcp-builder archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

---

Mcp Builder

Orchestrates intelligent skill selection and execution for mcp builder workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def build_mcp_tool_definition(
    tool_name: str,
    description: str,
    parameters: Dict[str, Any],
    schema_version: str = "2024-11-05"
) -> Dict[str, Any]:
    """Construct a valid MCP tool definition with strict schema validation.
    
    Applies Law 2 (Make Illegal States Unrepresentable) by enforcing
    JSON-RPC 2.0 compliant parameter schemas and preventing malformed tool specs.
    
    Args:
        tool_name: Unique identifier for the MCP tool
        description: Human-readable explanation of tool capabilities
        parameters: JSON Schema compliant parameter definitions
        schema_version: MCP protocol version to target
        
    Returns:
        Fully formed MCP tool definition dictionary ready for server registration
    """
    # Guard clause - Early Exit (Law 1)
    if not tool_name or not re.match(r"^[a-zA-Z0-9_-]+quot;, tool_name):
        raise ValueError("Tool name must be alphanumeric with underscores/hyphens")
        
    if not parameters or not isinstance(parameters, dict):
        raise ValueError("Parameters must be a non-empty JSON Schema dictionary")
    
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    validated_schema = _normalize_json_schema(parameters)
    
    # Atomic Predictability (Law 3) - Return new dict, never mutate inputs
    tool_def = {
        "name": tool_name,
        "description": description.strip(),
        "inputSchema": validated_schema,
        "meta": {
            "protocol_version": schema_version,
            "created_at": datetime.utcnow().isoformat(),
            "validation_status": "passed"
        }
    }
    
    # Fail immediately with descriptive errors on invalid states (Law 4)
    if not _validate_schema_against_draft(validated_schema):
        raise SchemaValidationError(f"Tool '{tool_name}' failed JSON Schema validation")
        
    return tool_def

Pattern 2: Execution with Fallback

def deploy_mcp_server_with_fallback(
    server_config: Dict[str, Any],
    transport_type: str = "stdio",
    max_retries: int = 2
) -> Dict[str, Any]:
    """Deploy an MCP server instance with transport-layer fallback resilience.
    
    Implements Fail Fast, Fail Loud (Law 4) for connection initialization:
    - Invalid transport configs halt immediately
    - Network timeouts trigger automatic fallback to alternative transports
    
    Fallback chain:
    1. Retry stdio transport with adjusted buffer sizes
    2. Fall back to SSE transport if stdio fails
    3. Defer to manual server restart if both fail
    
    Args:
        server_config: MCP server configuration including tool registry
        transport_type: Preferred transport protocol (stdio, sse, http)
        max_retries: Maximum connection attempts before fallback
        
    Returns:
        Deployment status with active transport and connection metadata
    """
    # Guard clause - validate transport config (Early Exit)
    if transport_type not in ("stdio", "sse", "http"):
        raise TransportError(f"Unsupported transport: {transport_type}")
        
    # Parse context - Ensure trusted state (Law 2)
    validated_config = _sanitize_server_config(server_config)
    
    active_transport = None
    for attempt in range(max_retries + 1):
        try:
            if transport_type == "stdio":
                active_transport = StdioTransport(validated_config)
            elif transport_type == "sse":
                active_transport = SseTransport(validated_config)
            else:
                active_transport = HttpTransport(validated_config)
                
            active_transport.initialize()
            
            # Success - Atomic Predictability (Law 3)
            return {
                "status": "deployed",
                "transport": transport_type,
                "connection_id": active_transport.session_id,
                "tools_registered": len(validated_config.get("tools", [])),
                "latency_ms": active_transport.get_startup_latency()
            }
            
        except ConnectionRefusedError:
            # Transient error - try fallback
            if attempt == max_retries:
                return _switch_to_fallback_transport(validated_config)
                
    # All retries exhausted - Fail Loud (Law 4)
    raise DeploymentError(f"MCP server failed to initialize after {max_retries + 1} attempts")

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

---

Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Related Skills

| Skill | Purpose | |