algo-coach-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| pick_problemB | Pick a random problem by topic and/or difficulty |
| get_solutionB | Get the solution code and key points for a problem |
| get_theoryC | Get the theory/fundamentals article for a topic |
| get_real_world_casesC | Get real-world engineering application cases for an algorithm |
| generate_test_casesC | Generate test cases (including edge cases) for a problem |
| run_user_codeC | Run user-submitted Python code against test cases locally |
| get_topic_roadmapA | Get the ordered topic roadmap for learning progression |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Topic list with ordering |
TDQS
Scored across 7 tools
Each tool clearly targets a distinct action: picking problems, retrieving solutions, theory, real-world cases, test generation, code execution, and roadmap. No two tools overlap in purpose, making misselection unlikely.
All tool names follow a consistent verb_noun snake_case pattern (pick_problem, get_solution, generate_test_cases, etc.). The naming is predictable and clearly indicates each tool's function.
Seven tools is a well-scoped set for an algorithm coaching server. Each tool addresses a distinct part of the learning workflow without redundancy or bloat, fitting the typical ideal range.
The tool surface covers the essential learning loop: selecting problems, accessing theory/solutions, generating tests, running user code, and following a roadmap. There are no obvious gaps or dead ends for the stated coaching purpose.