# MCP Builder Ms > Implements intelligent mcp builder ms with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/mcp-builder-ms` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/mcp-builder-ms` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/mcp-builder-ms/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-ms --- # Mcp Builder Ms Orchestrates intelligent skill selection and execution for mcp builder ms 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 route_mcp_tool_request( user_query: str, available_tools: List[McpToolSchema], min_confidence: float = 0.75 ) -> Optional[ToolRoutingResult]: """Route a user query to the optimal MCP tool using multi-factor scoring. Applies Law 1 (Early Exit) and Law 2 (Immutable State) to ensure only valid, high-confidence tool matches proceed to execution. """ if not user_query or not available_tools: raise ValueError("Query and tool registry must be non-empty") query_vector = _embed_query(user_query) best_match = None best_score = 0.0 for tool in available_tools: if not tool.is_available: continue name_similarity = _cosine_similarity(query_vector, tool.name_vector) desc_similarity = _cosine_similarity(query_vector, tool.description_vector) historical_success = tool.metrics.success_rate_30d composite_score = (name_similarity * 0.4) + (desc_similarity * 0.4) + (historical_success * 0.2) if composite_score > best_score and composite_score >= min_confidence: best_score = composite_score best_match = tool if best_match is None: return None return ToolRoutingResult( tool_name=best_match.name, confidence=best_score, parameters=best_match.extract_params(user_query), timestamp=time.time() ) ``` ### Pattern 2: Execution with Fallback ```python def execute_mcp_tool_with_resilience( routing_result: ToolRoutingResult, mcp_client: McpClient, fallback_tools: List[str] = None ) -> ExecutionOutcome: """Execute an MCP tool call with a structured fallback chain. Implements Law 4 (Fail Fast/Loud) by immediately halting on schema mismatches and Law 3 (Atomic Predictability) by returning immutable result objects. """ if not routing_result or not mcp_client: raise ExecutionError("Missing routing result or MCP client connection") tool_name = routing_result.tool_name params = routing_result.parameters attempts = 0 max_attempts = 2 while attempts <= max_attempts: try: # Validate parameters against tool schema before sending validated_params = _validate_against_schema(params, tool_name) raw_response = mcp_client.call_tool(tool_name, validated_params) return ExecutionOutcome( success=True, tool=tool_name, data=raw_response, confidence=routing_result.confidence, latency_ms=_elapsed_ms(), attempts=attempts + 1 ) except SchemaValidationError as e: raise ExecutionError(f"Schema mismatch for {tool_name}: {e}") from e except TransientMcpError as e: attempts += 1 if attempts > max_attempts: break time.sleep(0.5 * attempts) # Fallback Chain: Try alternative tools if primary fails if fallback_tools: for alt_tool in fallback_tools: try: alt_result = mcp_client.call_tool(alt_tool, params) return ExecutionOutcome( success=True, tool=alt_tool, data=alt_result, confidence=0.6, latency_ms=_elapsed_ms(), attempts=attempts + 1, fallback_triggered=True ) except Exception: continue raise ExecutionError(f"All attempts and fallbacks exhausted for {tool_name}") ``` ### 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 ## Related Skills | Skill | Purpose | | --- --- ## 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 ## Live References > Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [Model Context Protocol (MCP) Specification]() - [Anthropic MCP SDK Documentation]() - [MCP Server Development Guide]() - [MCP Transport Protocols (stdio, SSE, HTTP)]() - [Cline MCP Integration for AI Agents]()