TinyRAG
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | No | Optional API key for OpenAI-compatible embedding API to enable semantic retrieval. | |
| EMBEDDING_MODEL | No | Optional embedding model name (e.g., text-embedding-3-small). | text-embedding-3-small |
| OPENAI_BASE_URL | No | Optional base URL for the embedding API (e.g., https://api.openai.com/v1). Default is the same. | https://api.openai.com/v1 |
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 |
|---|---|
| query_knowledge_baseA | 从本地知识库中查找与问题最相关的内容并生成答案。 |
| list_documentsA | 列出知识库中的所有文档。 |
| search_relevant_chunksA | 搜索与问题最相关的知识片段（不经过生成器，返回原文）。 |
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
query_knowledge_base and search_relevant_chunks both search the knowledge base for relevant content, differing only in whether the response is generated or raw chunks. An agent might select the wrong one if not attentive. list_documents is clearly distinct.
All tool names follow a consistent verb_noun pattern using snake_case: query_, list_, search_. The naming is predictable and clear.
Three tools is an appropriate size for a focused RAG server, covering retrieval and listing without unnecessary bulk.
The tools cover querying and listing but lack document management (add, update, delete). This is a notable gap for a knowledge base server, though the core retrieval workflow is present.