MCP server for storing and retrieving personalized episodic agent memories with decay-weighted recall scoring and cross-session persistence
This server has a single tool 'execute_skill_action' with critical definition gaps. The tool description is generic (58 chars) and provides no actionable guidance on what the tool does, when to use it, or what it returns. No input schema is visible in the source code, the tool appears to be declared only in a JSON status message with no formal MCP tool registration. No parameter descriptions, types, or constraints are defined. The output schema is completely undocumented. The implementation is a stub that prints a status message and exits; there is no actual tool handler or request/response processing. This server does not implement the MCP protocol correctly.
Execute a skill action for the personalized episodic agent memory store
No input schema visible for execute_skill_action. Tool definition appears only in a JSON status message, not in formal MCP tool registration with JSON Schema.
Tool description is generic and uninformative (58 chars). Does not explain what the tool does, when to use it, what parameters it accepts, or what it returns. LLMs cannot determine tool purpose from this description.
No parameter definitions visible. Tool name contains 'and' (execute_skill_action) which suggests multiple responsibilities. No clarity on what 'skill_action' means or what parameters are required.
No output schema documented. LLMs cannot plan downstream calls or extract relevant data without knowing what fields the tool returns.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 19 | 2026-07-28+ | v2 |
Server does not implement MCP protocol request/response handling. mcp_server.py only prints a status message and exits. No tool invocation handler, no JSON-RPC message processing, no proper transport implementation.