Fast Embedding MCP SSE
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| embed_textA | Embed text(s) into vectors. Args: texts: One or more strings to embed. dim: Matryoshka truncation dimension; one of 512, 256, 128, 64, 32. normalize: L2-normalize the output (recommended for cosine similarity). Returns a dict with the embeddings (list of float lists) and dimension. |
| similarityA | Cosine similarity matrix between two sets of texts. Args: texts_a: First set of strings (rows of the result matrix). texts_b: Second set of strings (columns of the result matrix). dim: Matryoshka truncation dimension; one of 512, 256, 128, 64, 32. Returns a dict with a 2D similarity matrix of shape (len(a), len(b)). |
| searchA | Rank documents by relevance to a query (stateless semantic search). Args: query: The search query. documents: Candidate documents to rank. top_k: Return only the top K results (default: all). dim: Matryoshka truncation dimension; one of 512, 256, 128, 64, 32. Returns a dict with results sorted by score (descending). |
| index_addA | Add documents to the persistent in-memory index for later querying. Args: documents: Documents to embed and store. ids: Optional ids (auto-generated if omitted). metadatas: Optional metadata dict per document. Returns the number added and their ids. |
| index_queryC | Query the in-memory index for the most similar stored documents. Args: query: The search query. top_k: Number of results to return. |
| index_statsA | Return the number of documents currently in the index. |
| index_clearA | Remove all documents from the in-memory index. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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.