Competitive Programming Mentor MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LOG_LEVEL | No | Log level | INFO |
| LLM_PROVIDER | No | LLM provider: 'openai' or 'ollama' | openai |
| OLLAMA_MODEL | No | Ollama model | llama3.1:8b |
| OPENAI_MODEL | No | OpenAI model name | gpt-4o-mini |
| CACHE_ENABLED | No | Enable cache | true |
| CACHE_DISK_DIR | No | Cache disk directory | .cache |
| OPENAI_API_KEY | No | OpenAI API key | |
| OLLAMA_BASE_URL | No | Ollama base URL | http://localhost:11434 |
| CACHE_TTL_SECONDS | No | Cache TTL in seconds | 3600 |
| OPENAI_MAX_TOKENS | No | Max tokens for OpenAI | 4096 |
| OPENAI_TEMPERATURE | No | Temperature for OpenAI | 0.2 |
Capabilities
Features and capabilities supported by this server
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| detect_patternsC | Analyze problem text to detect patterns, difficulty, and complexity hints. |
| extract_constraintsB | Extract variable constraints and time/memory limits. |
| estimate_difficultyB | Estimate target audience and difficulty rating for the problem. |
| identify_topicsA | Identify topics, tags, and prerequisites for the problem. |
| suggest_algorithmsB | Suggest multiple viable candidate algorithms or structures for the problem. |
| compare_algorithmsB | Compare multiple candidate algorithms in a detailed pros/cons comparison. |
| choose_best_algorithmB | Select the single absolute best algorithm to implement for a problem. |
| estimate_runtimeC | Estimate runtime safety by validating loops/nodes against constraints. |
| generate_solutionB | Generate an optimal solution for the problem in the requested language. |
| generate_pseudocodeB | Generate language-agnostic pseudocode for the problem. |
| generate_multi_languageA | Generate code solutions in C++, Java, and Rust. |
| dry_runC | Perform a step-by-step trace execution of the code against test cases. |
| prove_correctnessB | Verify correctness of an approach using loop invariants or mathematical proofs. |
| analyze_complexityB | Rigorously calculate time and space complexity of code. |
| generate_testcasesC | Generate sample test cases (input/output/explanation) for the problem. |
| generate_edge_casesC | Identify critical edge case configurations and remedies. |
| stress_testingB | Generate stress testing script, random generator, and brute-force checker. |
| review_solutionB | Review user code for correctness, time complexity, bugs, TLE risk, etc. |
| find_bugB | Search for logical errors, boundary flaws, or runtime bugs in the code. |
| optimize_solutionC | Refactor solutions to reduce runtime complexity and improve performance. |
| get_hintC | Provide progressive hints for the problem. |
| explain_algorithmC | Explain the mechanics of a specific algorithm / data structure. |
| recommend_next_problemB | Recommend next problems that build upon this problem. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| hints_only | Competitive programming coach persona that only gives progressive hints. No code. |
| contest_mode | Contest mode persona: fast, clean, terse, and hyper-optimized code output. |
| interview_mode | Mock interviewer persona: explains trade-offs, edge cases, and design choices. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| dijkstra_resource | Dijkstra's shortest path algorithm reference. |
| segment_tree_resource | Segment Tree data structure reference. |
| sliding_window_resource | Sliding Window pattern reference. |
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