This server presents a critically incomplete MCP implementation. The primary tool 'execute_skill_action' has NO input schema visible in the source code, NO parameter descriptions, and only a trivial placeholder description. The mcp_server.py file contains only a JSON status message with no actual MCP protocol implementation, no tool registration with schemas, no request/response handling, no parameter validation. The client.py and example_usage.py files demonstrate a standalone Python orchestration client unrelated to MCP. There is no evidence of MCP-compliant tool definitions, schema validation, or protocol handling. The server cannot accept or process MCP requests. This is a non-functional MCP server in early stub form.
Tools (1)
execute_skill_actionwritesource verified7/100
Execute a skill action (registered in MCP server but no implementation details provided in source)
Tool description is trivial placeholder text ('Execute a skill action (registered in MCP server but no implementation details provided in source)'). Does not explain WHAT the tool does, WHEN to use it, or what it returns. Under 50 chars of actual content.
mcp_server.py contains no actual MCP protocol implementation. It is a stub that prints a JSON status message. No tool registration, no request/response loop, no schema definitions, no parameter handling. Not a functional MCP server.
No parameter descriptions. The tool has no documented parameters, input format, expected values, constraints, or dependency hints. LLMs cannot determine what input to provide.
execute_skill_action
Recommendations
Replace mcp_server.py stub with a complete MCP server implementation using an MCP library (e.g., mcp Python SDK). Register execute_skill_action with a proper JSON Schema input definition including all required and optional parameters.
Define the input schema explicitly. Document what parameters execute_skill_action accepts (e.g., skill_name, action_type, context_data, etc.), their types, formats, constraints, and whether they are required. Use JSON Schema with 'type', 'description', 'enum', 'minLength', 'maxLength', 'pattern' as needed.
Expand the tool description to 50-200 characters. Explain: (1) What this tool does (execute a skill action on behalf of which agent?). (2) When to use it (in what scenario?). (3) What it returns (success response, fields, or handoff event ID?). Example: 'Transfer control from one agent to another, persisting context and swapping tool schemas. Pass active_agent_role, target_agent, and context_data. Returns handoff_event_id and audit trail.'
Add parameter descriptions for all inputs. If execute_skill_action accepts 'active_agent_role', 'target_specialist_agent', 'transfer_context', document each: what it controls, valid values (enum if applicable), format, required vs optional, and dependencies. Do not leave parameters undescribed.
Document the output schema. List all fields returned (e.g., handoff_event_id, origin_agent, target_agent, handoff_latency_ms, context_variables_persisted, function_tool_schemas_swapped_count, agent_handoff_audit_url), their types, and what they represent. This enables LLMs to extract and chain data.
No output schema documented. LLMs do not know what fields to expect from execute_skill_action, making it impossible to chain with other tools or extract relevant data.
Tool naming ambiguous. 'execute_skill_action' does not clearly convey what action is executed, when to use it instead of alternatives, or what 'skill' and 'action' mean in context. Fails pattern:tool verb-noun clarity.
client.py and example_usage.py are standalone orchestration code unrelated to MCP. They do not implement or demonstrate the MCP protocol. The actual MCP server code is absent or stub only.
execute_skill_action
Add error handling guidance. Document failure modes: 'If target_agent not found, the call fails with error code AGENT_NOT_FOUND. Verify the agent exists using the available agent registry.' or similar. Categorize errors as retryable, user-fixable, or fatal.
Consider splitting execute_skill_action into more granular tools if it combines multiple concerns. For example: 'transfer_to_agent' (handoff logic), 'get_available_agents' (discovery), 'persist_context' (state management) may be separate. Each tool should do one thing well.
Ensure parameter naming follows pattern:tool conventions. If a parameter accepts a role, use 'role' or 'agent_role', not an overloaded generic. Provide enum constraints for known roles or agent types.
Remove or refactor client.py and example_usage.py. If they are meant to demonstrate the MCP server, integrate them into the MCP request/response flow. If they are separate orchestration utilities, document their relationship to the MCP server clearly.