DeepSeek MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DEEPSEEK_API_KEY | Yes | Your DeepSeek API key |
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 |
|---|---|
| chat_completionD | – |
| multi_turn_chatD | – |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| DeepSeek Chat | General-purpose chat model optimized for dialogue |
| DeepSeek Reasoner | Model optimized for reasoning and problem-solving |
| Temperature | Controls randomness in the output (0.0 to 2.0) |
| Maximum Tokens | Maximum number of tokens to generate |
| Top P | Controls diversity via nucleus sampling (0.0 to 1.0) |
| Frequency Penalty | Reduces repetition by penalizing frequent tokens (-2.0 to 2.0) |
| Presence Penalty | Reduces repetition by penalizing used tokens (-2.0 to 2.0) |
TDQS
Scored across 2 tools
The two tools have overlapping purposes with unclear boundaries. 'chat_completion' and 'multi_turn_chat' both appear to handle chat interactions, making it difficult for an agent to distinguish when to use each. Without descriptions, the ambiguity is significant, though the small tool count limits confusion.
The tool names follow a consistent snake_case pattern with clear, descriptive terms. Both 'chat_completion' and 'multi_turn_chat' use similar naming conventions, making them readable and predictable, though the lack of descriptions reduces clarity.
With only 2 tools, the server feels thin and under-scoped for a general-purpose 'DeepSeek MCP Server'. This minimal set suggests incomplete coverage of potential functionalities, such as model management or configuration, making it borderline inadequate.
Inferring the domain as AI chat interactions, the surface is severely incomplete. It lacks essential operations like model selection, parameter tuning, or context management. The two tools provide basic chat functions but leave significant gaps that could cause agent failures in complex tasks.