MCP Sage
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GEMINI_API_KEY | No | Your Google Gemini API key (for Gemini 2.5 Pro) | |
| OPENAI_API_KEY | No | Your OpenAI API key (for GPT-5 and GPT-4.1 models) | |
| ANTHROPIC_API_KEY | No | Your Anthropic API key (for Claude Opus 4.1) |
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 |
|---|---|
| sage-opinionA | Send a prompt to sage-like model for its opinion on a matter. |
| sage-reviewA | Send code to the sage model for expert review and get specific edit suggestions as SEARCH/REPLACE blocks. |
| sage-planA | Generate an implementation plan via multi-model debate. |
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
Each tool has a clearly distinct purpose: sage-opinion provides opinions on matters, sage-plan generates implementation plans through debate, and sage-review offers code review with edit suggestions. There is no overlap in functionality, and the descriptions clearly differentiate their roles.
All tool names follow a consistent 'sage-' prefix with a descriptive suffix (opinion, plan, review), using kebab-case throughout. This pattern is predictable and enhances readability, making it easy to identify the tool's function at a glance.
With 3 tools, the count is appropriate for a server focused on AI-assisted development tasks, as it covers key areas like opinion generation, planning, and code review. It is slightly lean but reasonable, as each tool serves a distinct and valuable purpose without redundancy.
The tool set covers core AI-assisted development workflows: opinion generation, planning, and code review. Minor gaps exist, such as the lack of tools for executing plans or managing project states, but agents can work around these by combining tools or using external methods.