This server has critical definition and structural deficiencies. The single tool 'execute_skill_action' has a reasonable input schema with two parameters (audio_recording_url and participant_roster), but the implementation is fundamentally incomplete. The mcp_server.py file contains only a stub that prints a hardcoded JSON status response, it does not actually implement MCP protocol message handling, tool invocation, or any real connection to the client. The client.py and example_usage.py are standalone Python classes unconnected to the MCP server. The tool definition lacks error handling guidance, output schema documentation, and proper description depth. The naming 'execute_skill_action' is generic and does not clearly convey what the tool does (it should be something like 'extract_meeting_action_items'). The description, while present, is only 87 characters and lacks crucial details about what the tool returns, prerequisites, or failure modes.
Tool name 'execute_skill_action' is generic and does not follow verb_noun pattern with domain clarity. Should be 'extract_meeting_action_items' to clearly convey purpose.
Tool description is only 87 characters and lacks critical context: no explanation of what the tool returns, prerequisites, time/cost implications, or error recovery guidance. LLMs cannot infer behavior from this sparse description.
Output schema is completely undocumented. The tool returns a complex object with fields like 'extraction_job_id', 'agenda_topics_covered', 'synthesized_action_items' (nested array with owner/task/priority), 'consensus_alignment_pct', and 'meeting_minutes_markdown_url', but none of this is visible in the MCP tool definition. LLMs cannot plan downstream actions without knowing what fields to expect.
execute_skill_action
CRITICAL
Recommendations
Rename tool from 'execute_skill_action' to 'extract_meeting_action_items' to clearly convey the action and domain. Follow verb_noun pattern: verb='extract', noun='meeting_action_items'.
Expand tool description to 150-200 characters explaining: (1) what the tool does (extracts agenda topics and assigns action items from a meeting recording), (2) when to use it (after a meeting concludes and audio is available), (3) what it returns (job ID, topics, action items, consensus score, minutes link), (4) prerequisites (audio must be accessible URL, participants list must be non-empty), (5) limitations (e.g., 'Processing may take 1-5 minutes depending on audio length').
Add explicit output schema to the tool definition documenting: extraction_job_id (string, job identifier), agenda_topics_covered (integer, count of topics identified), synthesized_action_items (array of {owner: string, task: string, priority: enum[P0|P1|P2|P3]}), consensus_alignment_pct (number 0-100), meeting_minutes_markdown_url (string, URI).
Add error handling documentation: describe what happens if (a) audio URL is unreachable, (b) audio format is unsupported, (c) participants list is empty, (d) processing times out, (e) speech recognition fails due to poor audio quality. For each error, provide actionable recovery: 'If audio URL is unreachable, verify the URL is publicly accessible and retry. Try alternative formats (MP3, WAV) if video processing fails.'
Clarify 'participant_roster' parameter format: specify that items should be strings in format 'Name (Role)' (e.g., 'Alice (Product Lead)', 'Bob (Engineer)'), and document that roles are optional but recommended for better action item assignment. Add example: ['Alice (Product Lead)', 'Bob (Engineer)', 'Charlie (Finance)'].
No error handling or recovery guidance. Tool description does not explain what happens on invalid audio URLs, missing participants, network failures, or extraction failures. No guidance for LLM on retry logic or next steps.
Parameter 'participant_roster' lacks depth: description says 'List of meeting participants with their roles' but does not clarify format (is it 'Alice (Product Lead)' or {'name': 'Alice', 'role': 'Product Lead'}?), required count, or how roles map to responsibility assignment in action items.
Parameter 'audio_recording_url' accepts only URLs but does not specify supported formats, maximum file size, timeout duration, or what happens if the URL is unreachable. LLM has no constraints to validate input.
The MCP server implementation in mcp_server.py is non-functional. It only prints a static JSON response and does not implement the MCP protocol message loop, tool handler routing, or any connection to the actual skill logic in client.py. The server cannot receive requests, parse tool invocations, or return results in valid MCP format.
client.py and example_usage.py are standalone Python utilities with hardcoded example data (audio URL 'board_strategy_q3.mp4', participant roster, mock results). They do not connect to or call the MCP server, and they mock results rather than integrating with a real service.
execute_skill_action
Add constraints to 'audio_recording_url': specify supported formats (MP4, MP3, WAV, M4A), maximum file size (e.g., '500 MB'), and timeout expectation (e.g., 'Processing typically takes 1-5 minutes'). Add description: 'Must be a publicly accessible HTTP(S) URL. Protocol will retry once if initial connection fails.'
Implement the MCP server properly in mcp_server.py: add a message parsing loop that handles MCP requests, routes 'execute_skill_action' invocations to actual skill logic (either import from client.py or call an external API), and returns results in valid MCP tool result format.
Remove mock/example data from client.py or refactor it as a test fixture separate from the MCP server implementation. The server should call a real skill provider (API, microservice, or local implementation), not return hardcoded results.
Add a per-item failure mode in the output: if some action items fail to extract (e.g., due to audio garbling), return a 'success' boolean or 'extraction_status' field per item so LLMs can log partial results rather than treating the entire response as failed.
Document timeout and retry behavior: 'If extraction takes >10 minutes, the server will return a 'pending' status with a job ID. Poll using the job ID to retrieve results when ready' (if async polling is supported), or state a hard timeout and suggest chunking long recordings.