Local MCP-Compatible Knowledge Server. A self-hosted context provider for developer documentation.
Mnemos provides three read-only knowledge-base tools with clear verb-noun naming (search_context, list_documents, get_document_info) and functional JSON schemas. Descriptions are adequate (100-150 chars) but lack strategic depth, they state WHAT the tool does without explaining WHEN to use it or how it chains with other tools. Parameter schemas are present with types and defaults, but descriptions are minimal or absent for some params (e.g., 'collection' param in search_context lacks a description explaining its semantics or how to discover valid values). Output schemas are not documented, LLMs cannot infer what fields to expect from these calls. Error handling is absent from tool descriptions, so agents cannot recover from common failures. The tools are well-composed (each does one thing) and read-only (no destructive operations), but they fall short of production-grade quality due to incomplete parameter documentation, missing output schema specifications, and lack of error recovery guidance.
Get detailed information about a specific document.
List all documents in the knowledge base.
Search the knowledge base for relevant context. Returns the most relevant document chunks for a given query.
Output schemas not documented. Tool descriptions do not specify what fields are returned (e.g., does search_context return 'document_id' or 'id'? 'content' or 'text'?). LLMs cannot plan downstream calls without knowing the response structure.
Parameter 'collection' in search_context and list_documents has no description. LLMs cannot infer valid values, the purpose of filtering by collection, or how to discover available collection names.
No error recovery guidance. Tool descriptions do not explain what happens on failure (e.g., 'document not found' for get_document_info, or 'empty search results' for search_context). Agents cannot self-correct or suggest alternatives.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-21 | C | 65 | 2026-07-28+ | v2 |
| 2026-03-09 | C | 63 | - | v1 |
Parameter descriptions are sparse. 'k' in search_context says 'Number of results to return (default: 5)', good. But missing: what do high k values cost (token budget)? Are results ranked by relevance? Does k guarantee k results or is fewer acceptable if fewer are available?
list_documents 'limit' param (default 100) lacks guidance on response size implications. Docs say 'Maximum number of documents to return' but do not explain whether 100 documents will fit in the context window or how to paginate.
No pagination or cursor support documented. list_documents accepts 'limit' but no 'offset', 'page', or 'next_cursor'. For knowledge bases with thousands of documents, LLMs cannot iterate through results without re-calling the tool with different limits.