Data Engineering Tutor MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENROUTER_API_KEY | Yes | Your OpenRouter API key for fetching live Data Engineering updates via Perplexity |
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 |
|---|---|
| de_tutor_read_memoryB | Reads the user's current Data Engineering knowledge from memory. |
| de_tutor_write_memoryC | Updates the user's Data Engineering knowledge memory for a specific concept. |
| de_tutor_get_updatesB | Fetches recent news and updates about Data Engineering concepts, patterns, and technologies using Perplexity Sonar via OpenRouter. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| data-engineering-tutor-guidance | Provides guidance on how to use the Data Engineering tutor tools and resources. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| data_engineering_knowledge_memory |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: fetching external updates, reading internal memory, and writing to internal memory. There is no overlap in functionality, making it easy for an agent to select the correct tool without confusion.
All tools follow a consistent 'de_tutor_verb_noun' pattern with snake_case, using descriptive verbs like 'get', 'read', and 'write'. This predictability enhances usability and reduces cognitive load for agents.
With only 3 tools, the set feels thin for a tutoring server, as it lacks interactive or instructional tools (e.g., explain concepts, quiz, or provide feedback). While the tools cover basic memory and update operations, the scope seems limited for effective tutoring.
The toolset is severely incomplete for a Data Engineering Tutor. It includes memory management and update fetching but misses core tutoring functions like explaining concepts, answering questions, or assessing knowledge. This will likely cause agent failures in delivering comprehensive tutoring.