Long-term memory MCP server for persistent AI assistants
Server has 10 tools with basic naming and descriptions, but significant gaps in schema completeness and parameter documentation. Most tools lack visible input schemas in the provided source code. Descriptions are present but generic (avg ~80 chars, below 194-char baseline). No parameter-level descriptions visible for most tools. Error handling and output schemas are not documented. Only memory_save, memory_recall, and memory_approve show explicit schema definitions; others are inferred from file references without visible implementation details.
Manage pending memories. Supported actions: list, approve, dismiss.
Compact and consolidate memories to optimize storage and recall
Delete a memory record
Manage and retrieve memory sensitivity policies
Recall memories from the long-term memory system based on a query
Save a memory to the long-term memory system
Summarize memories or sessions into concise memory records
Update an existing memory record
7 of 10 tools have no visible input schema definitions in source code. Only memory_save, memory_recall, and memory_approve show explicit JSON Schema. Others (memory_summarize, session_digest, memory_compact, memory_update, memory_delete, topic_lookup, memory_policy) are referenced by file path only, with no schema visible.
Descriptions are generic and lack actionable context. Most are 40-70 chars (below 194-char baseline). None explain WHEN to use the tool vs alternatives, what it returns, or prerequisites. E.g., 'Summarize memories or sessions into concise memory records' lacks guidance on when to call memory_summarize vs session_digest.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 40 | 2026-07-28+ | v2 |
Create a digest of a conversation session instead of saving raw conversation text
Look up and retrieve topic information from the memory system
No parameter-level descriptions visible for 7 tools. memory_save and memory_recall show param descriptions, but memory_summarize, session_digest, memory_compact, memory_update, memory_delete, topic_lookup, and memory_policy have no visible parameter documentation. LLMs cannot infer what 'context' or 'entities' mean without explicit descriptions.
No output schemas documented for any tool. LLMs cannot plan downstream calls or extract required fields. E.g., does memory_recall return a list of memory objects with IDs? Does memory_approve return a count of approved items? Undocumented outputs force LLMs to guess.
No error handling guidance. Tools like memory_delete (DESTRUCTIVE) and memory_update (WRITE) lack confirmation patterns or recovery instructions. No documentation of what errors are retryable, user-fixable, or fatal. Agents cannot self-correct on failures.