---

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

• guidance
• examples
• do-dont description: Implements intelligent mcp builder ms 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-ms, mcp builder ms, how do i mcp-builder-ms, orchestrate mcp-builder-ms, automate mcp-builder-ms, agent mcp-builder-ms 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 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

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

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