deepseek-discuss
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DEEPSEEK_MODEL | No | DeepSeek model ID to use. Priority is tool-call parameter, then this environment variable, then deepseek-flash. | deepseek-flash |
| DEEPSEEK_API_KEY | Yes | Your valid DeepSeek API key. On macOS it can alternatively be stored in the Keychain; if this environment variable is set, it must be valid. |
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 |
|---|---|
| ask_deepseekA | Get DeepSeek's independent answer in this chat; returns provider model metadata. Only prompt is sent as user context. Model defaults to DEEPSEEK_MODEL or deepseek-flash. Network inference consumes the user's DeepSeek API quota. |
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 1 tool
There is only one tool, so there is no possibility of misselection or overlap with a sibling tool. Its purpose (send a prompt to DeepSeek and get an answer back) is unambiguous.
The single tool follows a clear verb_noun snake_case pattern (ask_deepseek), matching the server's domain naming. With one tool there is no convention mixing to penalize.
The server is deliberately a one-function integration, so a single tool is defensible rather than an extreme mismatch. Still, the surface is thin: there is no way to inspect available models or manage configuration separately.
Core capability (one-shot inference) is covered, but the tool mentions model defaults and returned model metadata without offering any way to discover or select models or continue a conversation. Agents can work around this by passing prompts, but the surface has notable gaps for the stated purpose.