Fast Embedding MCP SSE
Related Servers
Alternatives to Fast Embedding MCP SSE
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityCmaintenanceSelf-hosted ML inference MCP server providing embeddings and summarization tools using sentence-transformers and Ollama, with low-latency preloaded models and API-key authentication.-
- AlicenseNot gradedqualityAmaintenanceProvides MCP tools for semantic search over personal knowledge sources using pluggable embeddings and local vector indexing.1MIT
- AlicenseAqualityDmaintenanceLocal-first RAG indexing and semantic search MCP server. Enables document retrieval and context-aware queries using local embedding models.35 npmMIT

@voxell/forge-mcpofficial
AlicenseAqualityCmaintenanceMCP server for Forge's hosted text-embedding API, providing tools to embed text into vectors and list available models.240 npm1MIT- AlicenseNot gradedqualityDmaintenanceA lightweight MCP server that enables intelligent tool management and semantic search for APIs using sentence-transformers. It supports both REST and MCP interfaces across dual transport modes, allowing users to upload, manage, and query API tools with natural language.1MIT
- AlicenseNot gradedqualityCmaintenanceMCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.MIT
TDQS
Scored across 7 tools
Each tool serves a distinct function: embedding, similarity computation, stateless search, and index management. The index_* tools are clearly separated from stateless operations, and search vs index_query are differentiated by whether a persistent index is used.
Naming mixes styles: embed_text uses verb_noun, index_add uses noun_verb, and similarity/index_stats are nouns. While the index_ prefix provides consistency for index operations, the overall pattern is inconsistent.
7 tools is well-scoped for an embedding server, covering core operations without redundancy. Each tool earns its place, and the count is within the ideal range.
Core workflows are covered: text embedding, similarity scoring, stateless search, and a persistent index with add/query/clear. Missing per-document delete/update or retrieval by ID are minor gaps that don't break typical usage.