Learning Hour MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MIRO_CLIENT_ID | No | Your Miro app client ID for OAuth authentication | |
| ANTHROPIC_API_KEY | Yes | Your Anthropic API key for generating Learning Hour content | |
| MIRO_CLIENT_SECRET | No | Your Miro app client secret for OAuth authentication |
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 |
|---|---|
| generate_sessionC | Generate comprehensive Learning Hour content for Technical Coaches |
| generate_code_exampleB | Generate detailed before/after code examples for a learning topic |
| create_miro_boardB | Create a new Miro board OR add frames to an existing board. This tool uses the Miro REST API to create boards with frames, sticky notes, text, and code blocks. It can create standalone boards or add content to existing boards. |
| analyze_repositoryC | Analyze a GitHub repository to find real code examples for Learning Hours |
| analyze_tech_stackC | Analyze a repository's technology stack to create team-specific Learning Hour content |
| list_miro_boardsB | List all Miro boards accessible with the current token |
| get_miro_boardC | Get details about a specific Miro board |
| delete_miro_boardC | Delete a Miro board (use with caution!) |
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 8 tools
Most tools have distinct purposes, but there is some overlap between analyze_repository and analyze_tech_stack, as both analyze GitHub repositories for Learning Hour content. However, their specific focuses (code examples vs. tech stack analysis) help differentiate them, and the other tools target clear, non-overlapping functions like Miro board management and content generation.
All tool names follow a consistent verb_noun pattern using snake_case, such as analyze_repository, create_miro_board, and generate_session. This uniformity makes the tool set predictable and easy for an agent to navigate, with no deviations in naming conventions.
With 8 tools, the count is well-scoped for the server's purpose of supporting Learning Hour creation and management. Each tool serves a specific role, from repository analysis to Miro board operations, without feeling excessive or insufficient for the domain.
The tool set covers key aspects of Learning Hour workflows, including content generation, repository analysis, and Miro board CRUD operations. A minor gap exists in the lack of update tools for Miro boards or generated content, but agents can work around this by deleting and recreating as needed, and core functionalities are well-represented.