paulpas 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
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 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
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:
- 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
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 | |