MCP Tooling Lab
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| CHROMA_URL | No | The URL of the Chroma vector database. | http://localhost:8000 |
| OPENAI_API_KEY | Yes | Your OpenAI API key used for generating embeddings. | |
| CHROMA_COLLECTION | No | The name of the Chroma collection to use. | mcp_tooling_lab |
| OPENAI_EMBED_MODEL | No | The OpenAI embedding model to use (e.g., text-embedding-3-small). | text-embedding-3-small |
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| embed_textC | Generate OpenAI embeddings for an array of texts. |
| index_documentsC | Embed and index documents into Chroma. |
| vector_searchB | Semantic search using embeddings + Chroma. Optional metadata filter via |
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 3 tools
Each tool has a clearly distinct purpose with no overlap: embed_text handles raw embedding generation, index_documents combines embedding with indexing, and vector_search performs semantic search. The descriptions clearly differentiate their roles in the embedding/indexing/search pipeline.
All three tools follow a consistent verb_noun pattern with snake_case: embed_text, index_documents, and vector_search. The naming is predictable and follows the same convention throughout.
With only 3 tools, the count feels thin for a 'Tooling Lab' server, which might imply broader capabilities. However, for a focused embedding/indexing/search domain, the minimal set is functional but could benefit from additional utilities like document management or configuration tools.
The tools cover the core embedding-to-search pipeline well: create embeddings, index them, and search. Minor gaps include lack of document deletion/update operations and no direct embedding storage management, but agents can work around these with the existing tools.