# MCP Builder > Implements intelligent mcp builder with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/mcp-builder` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/mcp-builder` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/mcp-builder/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/mcp-builder --- # 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 ```python 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_-]+$", 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 ```python 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 | |