paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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.