A
licenseNot graded
qualityD
maintenanceEnables AI assistants to search through structured databases and unstructured content (documents, videos, files) using natural language queries with semantic understanding.
MIT
MCP Embedding Storage Server
by ricleedo
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TDQS
C2.9/5.0
Scored across 2 tools
Disambiguation5/5
The two tools have perfectly distinct purposes: one saves content to the vector database, while the other searches for information within it. There is no overlap or ambiguity between these operations, making it impossible for an agent to confuse them.
Naming Consistency5/5
Both tools follow a consistent verb-noun pattern with hyphenated names (save-memory and search-memory). The verbs 'save' and 'search' clearly indicate the action, and 'memory' serves as a consistent noun, creating a predictable and readable naming convention throughout.
Tool Count2/5
With only two tools, this server feels under-scoped for an embedding storage system. While save and search are core operations, typical vector database interfaces would include additional tools like delete, update, list, or manage collections, making the current set appear thin and incomplete for the domain.
Completeness2/5
The tool surface is severely incomplete for an embedding storage server. It lacks essential operations such as deleting or updating stored memories, listing available entries, managing collections or namespaces, and performing advanced searches (e.g., by metadata). This will likely cause agent failures when trying to perform full lifecycle management of stored data.
Maintenance
ActivityInactive
ResponsivenessNo issues