This server has a single tool 'execute_skill_action' with virtually no definition. The tool name is vague (execute_skill_action could mean anything), the description is generic (10 words, under the 34-char p10 baseline), and critically, NO input schema is visible in the source code. The mcp_server.py file only prints a JSON status message and does not implement the MCP protocol at all, it does not handle tool calls, parse requests, or return structured responses. The client.py and example_usage.py files are standalone Python classes unrelated to MCP. This is not a functional MCP server.

• Rename tool to a specific action verb + noun pair. Examples: 'execute_lcel_chain', 'run_orchestrator_graph', 'evaluate_expression_chain'. This clarifies intent for LLMs.
• Write a 50-200 character description explaining: (1) what the tool does, (2) when to use it, (3) what it returns. Example: 'Execute a composable LCEL runnable graph with streaming support. Returns chain execution ID, node count, latency, and manifest URL. Use this to orchestrate multi-step LLM chains with fallback branches.'
• Define a complete JSON Schema for input parameters. At minimum: skill_name (string, required), input_query (string, required), streaming_enabled (boolean, optional, default=true). Add descriptions for each parameter.
• Implement actual MCP protocol handling in mcp_server.py: parse JSON-RPC requests, dispatch tool calls, return structured responses with result/error fields. Use the official MCP Python SDK or implement the spec directly.
• Document the output schema with typed fields: chain_execution_id (string), chain_nodes_count (integer), streaming_first_chunk_latency_ms (integer), fallback_branches_configured (integer), graph_topological_valid (boolean), output_manifest_url (string).