Professional Python MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GEMINI_API_KEY | Yes | Gemini API Key required for model discovery and agent interaction. |
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 |
|---|---|
| read_codeB | Reads the content of a file. Supported extensions: .py, .ts, .tsx, .js, .json, .md. |
| write_codeB | Writes or overwrites content to a file. |
| run_terminalC | Executes a shell command and returns stdout and stderr. Example: npm run dev, pytest, etc. |
| list_models_with_limitsA | Lists available Gemini models sorted by input token limit (context window). Provides a proxy for 'capacity' since exact quota is not available via API. |
| notify_devB | Sends a desktop notification. Supports Linux (notify-send). |
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 5 tools
Each tool has a clearly distinct purpose with no overlap: list_models_with_limits handles model information, notify_dev manages notifications, read_code and write_code handle file I/O (one for reading, one for writing), and run_terminal executes shell commands. The descriptions reinforce these distinct roles, making misselection unlikely.
The tool names follow a consistent verb_noun pattern (e.g., list_models_with_limits, read_code, write_code, run_terminal), which is predictable and readable. The only minor deviation is notify_dev, which uses 'notify' as the verb instead of a more standard action like 'send_notification', but it still fits the overall pattern well.
With 5 tools, the count is reasonable for a server focused on Python development utilities, covering model listing, notifications, file operations, and terminal execution. It feels slightly thin but well-scoped, as each tool serves a clear purpose without unnecessary bloat.
The server covers basic development tasks like file I/O, command execution, and notifications, but there are notable gaps for a 'Professional Python' scope, such as missing tools for code analysis (e.g., linting, testing), dependency management, or environment handling. The surface is functional but incomplete for advanced workflows.