This server provides 23 modular AI-powered competitive programming tools for analyzing, planning, generating, verifying, testing, reviewing, and learning from problems and code.
Analyze problems – detect algorithmic patterns, extract constraints, estimate difficulty, and identify topics.
Plan solutions – suggest, compare, and choose the best algorithm, plus estimate runtime feasibility.
Generate code – produce contest-ready solutions in Python/C++/Java/Rust, pseudocode, or multi-language output.
Verify correctness – trace execution with dry runs, prove correctness, and analyze time/space complexity.
Test solutions – generate standard testcases, edge cases, and randomized stress-testing setups.
Review and debug – review code for bugs/TLE risks, locate logical errors, and optimize performance.
Learn and improve – get progressive hints, algorithm explanations, and recommended next problems.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Competitive Programming Mentor MCP Server suggest algorithm for 'Median of Two Sorted Arrays'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
https://m8ven.ai/mcp/sami-codeai-mcp-server-for-competitive-programming-6880al?utm_source=new_listing&utm_medium=email&utm_campaign=new_listing https://glama.ai/mcp/servers/SAMI-CODEAI/MCP-Server-For-Competitive-Programming
Related MCP server: OnlineGDB MCP Server for C++ Code Execution
Traditional AI coding assistants force all reasoning through one massive prompt. This approach suffers from:
Problem | Impact |
Monolithic prompts | Impossible to test, cache, or reuse individual capabilities |
Provider lock-in | Switching from OpenAI → Ollama requires rewriting everything |
No structured output | Raw text responses require fragile regex parsing |
Redundant LLM calls | Identical problems re-analyzed every time |
This MCP server solves all four. Each capability is an independent tool with its own schema, prompt template, cache key, and validation pipeline.
┌──────────────────┐ MCP Protocol ┌──────────────────────────┐
│ Claude Desktop │◄─────────────────────►│ CP Mentor MCP Server │
│ Cursor IDE │ JSON-RPC / stdio │ │
│ Claude CLI │ │ 23 Tools · 3 Resources │
│ Any MCP Client │ │ 3 Prompts · 2-Tier Cache│
└──────────────────┘ └────────────┬─────────────┘
│
┌────────────▼─────────────┐
│ LLM Provider Layer │
│ ┌────────┐ ┌─────────┐ │
│ │ OpenAI │ │ Ollama │ │
│ │gpt-4o │ │llama3.1 │ │
│ └────────┘ └─────────┘ │
└──────────────────────────┘graph TD
Client["MCP Client<br/>(Cursor · Claude Desktop · CLI)"]
Server["FastMCP Server<br/>server.py"]
Cache{"Two-Tier Cache<br/>Memory TTL + SQLite Disk"}
Prompt["Jinja2 Prompt Manager<br/>prompts/*.md"]
Provider["LLM Provider Factory<br/>OpenAI | Ollama"]
Validator{"Self-Correcting Validator<br/>Pydantic v2 Schemas"}
Formatter["Response Formatter<br/>+ _meta tracing"]
Client -->|"tool call (JSON-RPC)"| Server
Server -->|"check cache"| Cache
Cache -->|"HIT"| Formatter
Cache -->|"MISS"| Prompt
Prompt -->|"rendered prompt"| Provider
Provider -->|"raw JSON"| Validator
Validator -->|"schema fail → retry prompt"| Provider
Validator -->|"schema pass"| Cache
Cache -->|"store result"| Formatter
Formatter -->|"structured response"| ClientPrinciple | Implementation |
Tool-Service Decoupling | Tools contain zero business logic — they delegate to |
Provider Independence |
|
Schema-First Validation | Every tool has a dedicated Pydantic |
Two-Tier Caching | In-memory |
Fail-Safe Registration | Each tool module is |
Tool | Description | Schema |
| Identifies algorithmic patterns (DP, Graph, Greedy, Math, etc.) from problem text |
|
| Parses numeric bounds (N, M, K, Q) and system limits (time/memory) |
|
| Approximates competitive programming difficulty rating |
|
| Tags primary/secondary topic categories |
|
Tool | Description | Schema |
| Recommends candidate algorithms based on constraints |
|
| Builds a trade-off comparison matrix across candidates |
|
| Selects the optimal algorithm with justification |
|
| Calculates operation count vs. time budget feasibility |
|
Tool | Description | Schema |
| Produces optimized, contest-ready code in the target language |
|
| Outputs language-agnostic structural pseudocode |
|
| Generates C++, Java, and Rust implementations simultaneously |
|
Tool | Description | Schema |
| Traces variable states step-by-step through sample inputs |
|
| Provides formal correctness proofs (loop invariants, induction) |
|
| Computes asymptotic time/space complexity with justification |
|
Tool | Description | Schema |
| Creates input/output test pairs covering standard scenarios |
|
| Targets boundary conditions, zero-cases, and overflow scenarios |
|
| Generates randomized brute-force stress test configurations |
|
Tool | Description | Schema |
| Full code review with correctness, efficiency, and style feedback |
|
| Pinpoints logical, runtime, or compile-time bugs |
|
| Suggests constant-factor and algorithmic optimizations |
|
Tool | Description | Schema |
| Progressive hint system (nudge → approach → partial solution) |
|
| Educational breakdown with examples, when-to-use heuristics |
|
| Suggests follow-up problems to reinforce learned concepts |
|
Competitive Programming Mentor MCP Server/
│
├── app.py # Entry point — mcp.run()
├── server.py # FastMCP app, tool/resource/prompt registration
├── config.py # Pydantic-settings configuration from .env
├── pyproject.toml # Dependencies & build config
├── .env.example # Environment template
│
├── services/ # Core business logic layer
│ ├── llm/
│ │ ├── base_provider.py # Abstract LLM interface
│ │ ├── openai_provider.py # OpenAI gpt-4o-mini adapter
│ │ ├── ollama_provider.py # Ollama local LLM adapter
│ │ └── provider_factory.py # Factory: .env → Provider instance
│ ├── prompt_manager.py # Jinja2 template renderer
│ ├── validator.py # Pydantic validation + self-correcting retry
│ ├── cache.py # Two-tier cache (TTLCache + diskcache)
│ ├── problem_parser.py # Regex constraint extractor (N, M, K, Q)
│ └── formatter.py # Response normalization + _meta tags
│
├── tools/ # MCP tool implementations (23 tools)
│ ├── analysis/ # detect_patterns, extract_constraints, ...
│ ├── planning/ # suggest_algorithms, compare_algorithms, ...
│ ├── generation/ # generate_solution, generate_pseudocode, ...
│ ├── verification/ # dry_run, prove_correctness, ...
│ ├── testing/ # generate_testcases, generate_edge_cases, ...
│ ├── review/ # review_solution, find_bug, optimize_solution
│ └── learning/ # get_hint, explain_algorithm, recommend_problem
│
├── schemas/ # Pydantic response models (one per tool)
├── prompts/ # Jinja2 markdown templates (one per tool + personas)
├── resources/ # Static knowledge base (Markdown files)
│ ├── algorithms/graphs/ # dijkstra.md
│ ├── data_structures/ # segment_tree.md
│ └── patterns/ # sliding_window.md
│
├── tests/ # Pytest test suite
│ ├── test_parser.py # Constraint parsing tests
│ ├── test_cache.py # Two-tier cache tests
│ └── test_validator.py # Schema validation + retry tests
│
└── docs/
├── ARCHITECTURE.md # System design documentation
└── TOOLS.md # Tool reference catalog# Clone the repository
git clone https://github.com/your-username/competitive-programming-mcp.git
cd competitive-programming-mcp
# Install dependencies with uv
uv sync --all-extras
# Copy and configure environment
cp .env.example .env
# Edit .env with your API keys / Ollama settings# Start the MCP server (stdio transport)
uv run app.py
# Or run in development mode with auto-reload
fastmcp dev app.py
# Run tests
uv run pytestAll settings are managed via .env and loaded through Pydantic Settings:
# ─── LLM Provider ────────────────────────────────────
LLM_PROVIDER=openai # "openai" or "ollama"
# ─── OpenAI (when LLM_PROVIDER=openai) ───────────────
OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-4o-mini # ~$0.15/1M input tokens
OPENAI_MAX_TOKENS=4096
OPENAI_TEMPERATURE=0.2 # Low = deterministic code
# ─── Ollama (when LLM_PROVIDER=ollama) ────────────────
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=llama3.1:8b # Run: ollama pull llama3.1:8b
# ─── Cache ────────────────────────────────────────────
CACHE_ENABLED=true
CACHE_TTL_SECONDS=3600 # 1-hour TTL
CACHE_DISK_DIR=.cache # SQLite-backed persistent cache
# ─── Server ──────────────────────────────────────────
LOG_LEVEL=INFO # DEBUG | INFO | WARNING | ERRORAdd the following to your claude_desktop_config.json:
{
"mcpServers": {
"cp-mentor": {
"command": "uv",
"args": [
"--directory",
"YOUR_ABSOLUTE_PATH\\Competitive Programming Mentor MCP Server",
"run",
"app.py"
]
}
}
}Note on Config Locations:
Standard Windows:
%APPDATA%\Claude\claude_desktop_config.jsonWindows Store App:
C:\Users\YOUR_NAME\AppData\Local\Packages\Claude_pzs8sxrjxfjjc\LocalCache\Roaming\Claude\claude_desktop_config.json
Restart Claude Desktop completely. Note: In the latest versions of Claude Desktop, the plug (🔌) icon has been removed from the UI. MCP tools are simply loaded silently in the background. Just ask Claude to "Use your cp-mentor tools to solve X" and you will see an approval pop-up!
If you use the claude terminal tool, run:
claude mcp add cp-mentor uv --directory "/path/to/project" run app.py
claude # Start a sessionImportant for local models: If you are using
ollamawith the Claude CLI, make sure you launch it with a large enough model (e.g., 9B+ parameters) that supports tool calling. Small models (like 4B or 1B) will crash or fail to invoke MCP tools properly. Example:ollama launch claude --model qwen2.5:14b
Open Cursor → Settings → Features → MCP
Click + Add New MCP Server
Set Name: cp-mentor
Set Type: command
Set Command: uv --directory "/path/to/project" run app.py
User provides a problem (e.g., LeetCode / Codeforces)
│
▼
┌─────────────┐
│ Step 1 │ detect_patterns(problem)
│ Analyze │ extract_constraints(problem)
│ │ identify_topics(problem)
└──────┬──────┘
│
▼
┌─────────────┐
│ Step 2 │ suggest_algorithms(problem)
│ Plan │ compare_algorithms(problem, candidates)
│ │ choose_best_algorithm(problem, candidates)
└──────┬──────┘
│
▼
┌─────────────┐
│ Step 3 │ generate_solution(problem, approach, language)
│ Generate │ generate_pseudocode(problem)
└──────┬──────┘
│
▼
┌─────────────┐
│ Step 4 │ dry_run(problem, code)
│ Verify │ prove_correctness(problem)
│ │ analyze_complexity(problem, code)
└──────┬──────┘
│
▼
┌─────────────┐
│ Step 5 │ generate_testcases(problem)
│ Test │ generate_edge_cases(problem)
│ │ stress_testing(problem)
└──────┬──────┘
│
▼
┌─────────────┐
│ Step 6 │ review_solution(problem, code)
│ Review │ optimize_solution(problem, code)
└─────────────┘When a client invokes detect_patterns(problem="..."), the following pipeline executes:
1. FastMCP receives JSON-RPC call via stdio transport
2. Tool function in tools/analysis/detect_patterns.py is invoked
3. CacheService.get("detect_patterns", {problem: "..."}) → checks memory, then disk
4. On MISS: PromptManager renders prompts/detect_patterns.md with Jinja2
5. ProviderFactory returns OpenAIProvider or OllamaProvider
6. Provider.structured_output(prompt, PatternResponse) calls the LLM API
7. Validator checks response against PatternResponse Pydantic schema
8. On validation failure: auto-correcting retry with error feedback prompt
9. CacheService.set() stores result in both memory and disk tiers
10. Formatter wraps response with _meta (tool name, model, cache status, latency)
11. Structured JSON returned to clientThe validator implements a retry loop that feeds Pydantic validation errors back to the LLM:
# Simplified flow
for attempt in range(max_retries):
raw_json = await provider.structured_output(prompt, schema)
try:
return schema.model_validate_json(raw_json) # Success
except ValidationError as e:
prompt = f"Fix these errors: {e.errors()}\nOriginal: {raw_json}"
# Retry with corrective context ┌─────────────────┐
get(key) ────────►│ Memory (TTL) │──── HIT ────► return value
│ ~256 entries │
│ μs latency │
└───────┬─────────┘
│ MISS
┌───────▼─────────┐
│ Disk (SQLite) │──── HIT ────► promote to memory
│ Persistent │ + return value
│ ms latency │
└───────┬─────────┘
│ MISS
▼
Call LLM ProviderCache keys are deterministic: f"{tool_name}:{sha256(sorted_json(inputs))[:16]}"
class BaseLLMProvider(ABC):
@abstractmethod
async def generate(self, prompt: str, system: str = "") -> str: ...
@abstractmethod
async def structured_output(self, prompt: str, response_model: type[T], system: str = "") -> T: ...
@property
@abstractmethod
def model_name(self) -> str: ...OpenAIProvider: Uses client.beta.chat.completions.parse() for native JSON schema generation
OllamaProvider: Uses openai-compatible endpoint with regex JSON extraction fallback
# Run all tests
$env:PYTHONPATH="." # PowerShell
uv run pytest
# Run with verbose output
uv run pytest -v
# Test specific module
uv run pytest tests/test_parser.py
uv run pytest tests/test_cache.py
uv run pytest tests/test_validator.pyModule | Tests | What's Verified |
| 2 | Constraint regex parsing (including |
| 2 | Memory/disk hit/miss, tier promotion, Windows file lock cleanup |
| 2 | Schema compliance on first try, self-correcting retry on malformed JSON |
Component | Technology | Purpose |
MCP Framework |
| Server SDK, tool registration, stdio transport |
LLM Client |
| OpenAI + Ollama-compatible API calls |
Validation |
| Typed schemas for all 23 tool outputs |
Configuration |
|
|
Templating |
| Prompt template rendering |
Memory Cache |
| In-memory TTL cache |
Disk Cache |
| SQLite-backed persistent cache |
Retry Logic |
| Exponential backoff for LLM API calls |
Testing |
| Async-compatible test framework |
This project is provided as-is for educational and competitive programming purposes.
Rigorously calculate time and space complexity of code.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | User submitted code. | |
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavioral traits. It does not mention any side effects, permissions, or limitations, leaving the agent uninformed about safe usage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence with no redundant words. Every word serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and the presence of an output schema (not shown), the description is adequate but lacks details like whether it handles all types of complexity or edge cases. Could be more informative.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds no further meaning to the parameters 'code' and 'problem' beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('calculate') and resource ('time and space complexity of code'), clearly distinguishing it from siblings like 'estimate_runtime' or 'compare_algorithms'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives such as 'estimate_runtime' or 'optimize_solution'. The description implies it is for complexity analysis but offers no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Select the single absolute best algorithm to implement for a problem.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. | |
| candidates | Yes | List of algorithm candidate names to choose from. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only states the basic operation. It fails to disclose behavioral details such as how the best algorithm is determined, whether it requires evaluation metrics, or what the return value looks like.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but lacks necessary detail. While it is front-loaded, it is too minimal given the complexity and presence of sibling tools.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (not shown), the description does not hint at what the tool returns. It also does not address when to use this tool over other selection or suggestion tools, leaving gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the schema already provides clear descriptions for both parameters ('problem' and 'candidates'). The tool description does not add any meaning beyond what the schema provides, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Select the single absolute best algorithm to implement for a problem.' It uses a specific verb ('select') and resource ('best algorithm'), and distinguishes from siblings like 'compare_algorithms' (which compares multiple) and 'suggest_algorithms' (which suggests without selecting).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'suggest_algorithms' or 'compare_algorithms'. The description does not mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare multiple candidate algorithms in a detailed pros/cons comparison.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. | |
| candidates | Yes | List of candidate algorithm names to compare. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior, but it only says 'compare' and 'detailed pros/cons comparison' without specifics on output format, limitations, or edge cases.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, but lacks structure or subsections. It could be more informative without being overly long.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite an output schema existing, the description does not hint at what the output looks like (e.g., a table, text). For a comparison tool, it is incomplete in guiding the agent on expected results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already describes both parameters. The description adds 'multiple candidate algorithms' but no further meaningful detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool compares multiple candidate algorithms with a pros/cons approach. It distinguishes from siblings like choose_best_algorithm (selects one) and analyze_complexity (focuses on complexity).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives like choose_best_algorithm or suggest_algorithms. The description only states what it does, not when it is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze problem text to detect patterns, difficulty, and complexity hints.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Optional extra context (e.g., contest name or hints). | |
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose any behavioral traits such as whether it requires auth, is read-only, or if it stores data. For an analysis tool, more transparency about side effects is expected.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, concise and front-loaded. However, it could benefit from breaking out details about output or usage without adding length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of 22 sibling tools and no annotations, the description is too sparse. It does not mention return values or output format, even though an output schema exists. The tool's purpose is underspecified compared to alternatives.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for both parameters, and the description does not add meaning beyond what the schema already provides (e.g., 'problem' is described as 'The full text of the problem description'). Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Analyze problem text to detect patterns, difficulty, and complexity hints.' This gives a verb and resource but is vague about what kinds of patterns are detected. Among siblings like analyze_complexity and estimate_difficulty, it lacks differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus the many sibling tools (e.g., analyze_complexity, identify_topics, estimate_difficulty). The description implies general analysis but does not set boundaries or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Perform a step-by-step trace execution of the code against test cases.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The code to execute the dry run on. | |
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It only states the action (trace execution) but does not clarify side effects, safety, output format, or whether it actually runs code. The term 'dry_run' implies non-destructive, but not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler. However, it could be slightly more informative without increasing length much, but overall it is well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (not shown), the description fails to explain the relationship between test cases, code, and problem. For a trace execution tool, more context on expected output or behavior is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for both parameters. The description adds no extra parameter-level detail beyond the schema, so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'perform' and the resource 'step-by-step trace execution', but it mentions 'against test cases' without explaining how test cases are provided (not in schema). This causes slight ambiguity but overall purpose is understandable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings like find_bug, stress_testing, or generate_testcases. The description does not specify prerequisites or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Estimate target audience and difficulty rating for the problem.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It does not disclose what behaviors the tool has (e.g., whether it uses machine learning, requires high-quality input, or has any side effects). The output schema exists but the description does not mention it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the action. Every word is necessary and there is no superfluous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one simple parameter and an output schema (implied), the description is minimally sufficient. However, it could provide more context on what the output rating looks like or examples of target audiences.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already fully describes the only parameter. The tool description adds no additional meaning beyond the schema, meeting the baseline expectation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool estimates 'target audience and difficulty rating for the problem,' using a specific verb and resource. It distinguishes from siblings like 'estimate_runtime' and 'recommend_next_problem' which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives, such as when to estimate difficulty vs. analyze complexity. The description lacks context for optimal usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Estimate runtime safety by validating loops/nodes against constraints.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. | |
| algorithm | Yes | Name or description of the selected algorithm. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully cover behavioral traits. It mentions validating loops/nodes against constraints but does not disclose whether the tool is read-only, what constraints are considered, or what the output format is. This is insufficient for an agent to understand side effects or requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 8 words, front-loading the verb 'Estimate'. It is concise but may sacrifice necessary detail; however, it is not verbose and efficiently states the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (validating runtime safety) and the presence of many siblings, the description is too sparse. It doesn't clarify what 'runtime safety' means or how constraints are applied. The output schema exists but the description does not integrate with it. The description leaves significant gaps for an agent to understand when and how to use this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already explains the two parameters. The description does not add meaning beyond stating 'algorithm' and 'problem' as inputs; it briefly mentions loops/nodes which hints at parameter expectations but not explicitly. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Estimate' and resource 'runtime safety', and mentions validating loops/nodes, which gives a clear but somewhat vague purpose. It can be distinguished from siblings like analyze_complexity, but could be more precise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like analyze_complexity or dry_run. The description lacks any context about prerequisites, exclusions, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Explain the mechanics of a specific algorithm / data structure.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. | |
| algorithm | Yes | Name of the algorithm (e.g. 'Dijkstra', 'Trie'). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description bears full responsibility for behavioral disclosure. It only says 'explain the mechanics', without indicating whether the output includes time/space complexity, code examples, or constraints. The behavior is under-specified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys the core purpose efficiently. It is concise, but could benefit from a brief suffix clarifying the output scope without adding significant length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the description need not detail return values. However, given the absence of annotations and the large sibling set, the description lacks guidance on prerequisites (e.g., valid algorithm name) and fails to set expectations for the output format or level of detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes both parameters ('problem' and 'algorithm') with clear descriptions, achieving 100% coverage. The description adds no extra meaning beyond what the schema provides, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool explains the mechanics of an algorithm or data structure. The verb 'explain' and noun 'mechanics' specify the tool's action and subject. It distinguishes from siblings like 'compare_algorithms' or 'dry_run', but could be more precise about what 'mechanics' includes (e.g., step-by-step, complexity).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'dry_run', 'generate_pseudocode', or 'analyze_complexity'. With 22 sibling tools, explicit usage context is crucial but missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract variable constraints and time/memory limits.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description discloses no behavioral traits (e.g., output format, failure modes, or edge cases). The bare description does not help the agent understand side effects or constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It is appropriately concise for a tool with one parameter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (one parameter, no annotations, output schema exists), the description adequately conveys the tool's purpose for selection among many sibling tools. It does not need to explain return values as the output schema is present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the parameter is well-documented. The description adds context about what is extracted, but does not provide additional semantic details beyond the schema's description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Extract') and resource ('variable constraints and time/memory limits'), clearly distinguishing it from siblings like 'estimate_runtime' or 'analyze_complexity'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided—no indication of when to use vs. alternatives, prerequisites, or when not to use. It simply describes the action.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for logical errors, boundary flaws, or runtime bugs in the code.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | User code to debug. | |
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It only states what it searches for but not how it operates (e.g., static analysis or execution), safety implications, or output format. This is insufficient for an agent to understand side effects or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence covering the core purpose. Every word is necessary, and there is no extraneous information. It is front-loaded with the action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters) and the existence of an output schema, the description is minimally adequate. However, it lacks context on how this tool integrates with siblings or what makes it unique, leaving some gaps for an agent deciding between similar tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Both parameters are documented in the schema with descriptions ('User code to debug.' and 'The full text of the problem description.'). The description adds no additional semantic meaning beyond what the schema provides, so a baseline score of 3 is appropriate given 100% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: searching for logical errors, boundary flaws, or runtime bugs. The verb 'search' and specific resource types provide a precise action, distinguishing it from sibling tools like 'review_solution' or 'stress_testing' which focus on different aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not indicate when to use this tool versus alternatives such as 'review_solution' or 'stress_testing', nor does it specify prerequisites or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Identify critical edge case configurations and remedies.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It implies a read-only analysis but does not disclose any behavioral traits like whether it modifies state, authentication needs, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and to the point. It efficiently conveys the core function without unnecessary words, though it could benefit from slightly more detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present (not detailed here) and only one parameter, the description is minimally adequate. It lacks context on what the output looks like or how it fits with sibling tools, but the presence of output schema reduces the burden.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single 'problem' parameter with a clear description. The tool description does not add additional meaning beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool identifies 'critical edge case configurations and remedies', which combined with the name indicates its purpose. However, it is somewhat generic and doesn't explicitly distinguish from siblings like 'generate_testcases', but the context of algorithm-related siblings helps narrow down.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as 'generate_testcases' or 'detect_patterns'. No when-not-to-use or prerequisite conditions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate code solutions in C++, Java, and Rust.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only mentions languages but lacks details on output format, ordering, or side effects. Adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded, no wasted words. Highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Output schema exists but is not shown; description is minimal. Could benefit from explaining how solutions are structured, but given schema coverage, it is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameter with description; the tool's description adds value by specifying the target languages beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb (generate), resource (code solutions), and specific languages (C++, Java, Rust), distinguishing it from siblings like generate_solution.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage for multi-language generation, but no explicit when-to-use or when-not-to-use guidance, nor mention of alternatives like generate_solution.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate language-agnostic pseudocode for the problem.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It only restates the purpose and does not address side effects, authentication needs, rate limits, or output characteristics beyond the existence of an output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no wasted words. It efficiently conveys the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, clear output), the description is minimally adequate. However, it lacks guidance for tool selection among 22 siblings and does not leverage the existing output schema to clarify expected results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'problem', which is well-described in the schema itself. The tool description adds no additional meaning or context beyond the schema, resulting in a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Generate', the resource 'language-agnostic pseudocode', and the scope 'for the problem'. It effectively distinguishes from sibling tools like 'generate_solution' or 'generate_multi_language' which focus on actual code.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention context, prerequisites, or exclusions, leaving the agent without direction for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate an optimal solution for the problem in the requested language.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Optional extra constraints, details, or hints. | |
| problem | Yes | The full text of the problem description. | |
| language | No | Programming language (python, cpp, java, rust, etc.). | python |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It does not disclose any behavioral traits such as whether it executes code, requires permissions, or is destructive. Only states it generates a solution.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, front-loaded with key information. Efficient, but could be slightly more structured without losing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters with full schema descriptions and an output schema, the description is adequate. However, it lacks any additional context about prerequisites or typical use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds no extra meaning beyond what the schema already provides (e.g., 'Optional extra constraints' for context).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'generate' and the resource 'optimal solution for the problem in the requested language'. It is specific and differentiates from siblings like generate_pseudocode or generate_multi_language.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., generate_pseudocode, generate_testcases). The description is minimal and provides no usage context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate sample test cases (input/output/explanation) for the problem.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It lacks disclosure of behavioral traits such as whether the generation is deterministic, AI-driven, or has limitations. It doesn't mention side effects, permissions, or performance.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It efficiently conveys the core function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the large number of sibling tools, the description fails to differentiate usage contexts. It does not specify what 'sample' means, the expected format of the problem, or provide enough context for the agent to choose correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, providing baseline of 3. The description adds no new meaning beyond the schema's description of the 'problem' parameter; it just restates that the problem is the full text.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates sample test cases with input/output/explanation for a problem. It uses a specific verb 'generate' and resource 'sample test cases'. It distinguishes from sibling tools like 'generate_edge_cases' which focus on edge cases, though not explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'generate_edge_cases' or 'dry_run'. The description does not mention prerequisites, when-not to use, or provide comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Provide progressive hints for the problem.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. | |
| hint_level | No | Depth of the hint (1 = minor nudge, 2 = connection tag, 3 = key hint). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It mentions 'progressive hints' but does not explain what each hint level entails, any side effects, or whether the operation is read-only. The schema adds depth descriptions, but the tool's behavior remains opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is concise but overly minimal. It lacks structure or front-loading of key information. Some verbosity to clarify 'progressive' would be beneficial.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the existence of an output schema (not shown) and no annotations, the description is adequate for a simple hint tool. However, it does not fully explain the progression of hints or integrate with sibling tool context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description adds minimal value beyond what the schema already provides. The 'hint_level' parameter has a description and default, but the tool description does not elaborate on usage specifics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides progressive hints for a problem, specifying the core function with a verb and resource. It is distinct from siblings like generate_solution, but does not explicitly differentiate itself beyond naming.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives like explain_algorithm or generate_pseudocode. The description lacks any when-to-use or when-not-to-use context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Identify topics, tags, and prerequisites for the problem.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and description lacks behavioral details such as return format, side effects, or constraints. Only states what it identifies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no wasted words, efficiently communicating the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate given output schema exists, but could benefit from more detail on the types of topics/tags identified considering the rich set of sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one parameter 'problem', and description adds minimal meaning beyond 'for the problem'. Baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Identify' and resources 'topics, tags, and prerequisites' for the problem, distinguishing from siblings like 'detect_patterns' or 'extract_constraints'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not guidance, but the purpose implies use for conceptual analysis, differentiating from siblings like 'analyze_complexity'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Refactor solutions to reduce runtime complexity and improve performance.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The suboptimal source code. | |
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only says 'refactor' and 'improve performance.' It does not disclose any behavioral traits like whether it directly modifies code, potential side effects, or output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one sentence, concise but lacks structure. It is front-loaded but omits critical details, making it minimally adequate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (optimizing code) and lack of annotations, the description is severely incomplete. It does not address the output schema, error cases, or guarantee of improvement, leaving the agent with insufficient information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions. The description adds no extra meaning beyond the schema, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states it refactors solutions for performance improvement, which is a clear verb+resource. However, it does not distinguish from siblings like 'review_solution' or 'suggest_algorithms' that might also aim to improve code, leading to some ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus its many siblings. There are no use cases, prerequisites, or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify correctness of an approach using loop invariants or mathematical proofs.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral traits. It states it uses 'loop invariants or mathematical proofs' but does not disclose side effects, return format, required permissions, or whether it is read-only. For a correctness verification tool, more context is needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, efficient at 12 words, verb front-loaded. Slightly sparse but not wasteful; could be improved with brief context without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given presence of an output schema, description needn't detail return values. However, it lacks usage guidelines and behavioral transparency, making it barely adequate for a complex reasoning tool. Covers purpose but misses important decision-making context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter 'problem', with a clear description. The tool description adds no extra meaning beyond the schema, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Verify correctness') and resource ('an approach'), and names methods ('loop invariants or mathematical proofs'). It clearly distinguishes from siblings like find_bug (bug detection) or review_solution (code review) by focusing on formal proof techniques.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance. Does not mention alternatives or contrast with sibling tools like find_bug or generate_solution. The description is too generic to help an agent decide between this and other verification-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recommend next problems that build upon this problem.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided. The description does not disclose what 'build upon' means, what the output format is (e.g., list of problem IDs or descriptions), or any behavioral traits like whether it considers difficulty or topics. The output schema exists but is not referenced.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 9 words, very concise. No wasted words. However, it could benefit from more structure (e.g., mentioning output or scope) without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's recommendation purpose and the existence of an output schema, the description is too sparse. It lacks context about how recommendations are generated, what 'build upon' means, and how it relates to sibling tools. Minimal viable would require more detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the single parameter 'problem', which is clearly documented. The description adds no additional meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Recommend' and clearly states the resource 'next problems that build upon this problem'. It distinguishes from sibling tools like 'suggest_algorithms' which suggest algorithms, not problems. The purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, context, or when not to use it. For a recommendation tool, this is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Review user code for correctness, time complexity, bugs, TLE risk, etc.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The code to review. | |
| problem | Yes | The full text of the problem description. | |
| language | No | Language of the code (python, cpp, java, rust, etc.). | python |
| error_message | No | Optional compiler/runtime error message from the judge. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It implies a read-only analysis operation, but does not explicitly state that it is non-destructive or detail any side effects, which is adequate but not exemplary.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It could be slightly more structured (e.g., bullet points) but is appropriately concise for the information conveyed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity, the description covers the main aspects of code review but lacks details on language support, the optional error_message parameter, and what the output contains. The presence of an output schema mitigates the need for output details, but behavioral context is still somewhat incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for all 4 parameters, so the description adds no additional meaning beyond the schema. The baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reviews code for correctness, time complexity, bugs, and TLE risk, which is a specific verb+resource. However, it does not differentiate itself from more specific sibling tools like 'find_bug' or 'analyze_complexity', so it loses a point for lack of distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives or when not to use it. It only implies usage for general code review, but no explicit context or exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate stress testing script, random generator, and brute-force checker.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears full responsibility for disclosing behavior. It only states what the tool generates but does not mention side effects, dependencies, determinism, or whether it performs actions beyond generating output (e.g., running scripts or modifying state). The output schema exists but is not referenced, leaving key behavioral traits unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the core purpose. Every word contributes to understanding, and there is no redundant or extraneous information. The structure is efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description is minimally sufficient: it specifies the tool takes a problem description and generates three components. However, it does not explain what a 'stress testing script' entails, how the random generator and brute-force checker relate, or any return format details not covered by the schema. An agent might need more context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for the 'problem' parameter. The tool description adds no additional meaning or constraints beyond what the schema provides, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a stress testing script, random generator, and brute-force checker. The verb 'generate' and resource 'stress testing script' are specific, and the tool differentiates well from siblings like 'generate_testcases' and 'generate_edge_cases' which focus on test case generation rather than stress testing infrastructure.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description lacks explicit context, such as prerequisites, when-not-to-use, or comparisons with sibling tools like 'generate_testcases' or 'dry_run'. An agent must infer usage solely from the name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Suggest multiple viable candidate algorithms or structures for the problem.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The full text of the problem description. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only states 'suggest multiple viable candidate algorithms', but does not explain output format, ranking, or how suggestions are derived.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no wasted words, but it is on the edge of being too brief for complex behavior. Still, it is efficiently structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description lacks context on what the suggestions look like (e.g., list of names, explanations). It is insufficient for an agent to understand the tool's full behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for the single parameter 'problem'. The description adds no extra meaning beyond the schema, meeting baseline expectations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'suggest' and resource 'candidate algorithms or structures', clearly indicating the tool generates options. It distinguishes from siblings like 'choose_best_algorithm' and 'compare_algorithms' by implying a generation phase.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as 'detect_patterns' or 'generate_solution'. The context of sibling tools is provided but not referenced in the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0analyze_complexitychoose_best_algorithmcompare_algorithmsdetect_patternsdry_runestimate_difficultyestimate_runtimeexplain_algorithmextract_constraintsfind_buggenerate_edge_casesgenerate_multi_languagegenerate_pseudocodegenerate_solutiongenerate_testcasesget_hintidentify_topicsoptimize_solutionprove_correctnessrecommend_next_problemreview_solutionstress_testingsuggest_algorithmsEach tool has a clear, distinct purpose, covering various aspects of competitive programming from analysis to code generation and debugging. No two tools appear to do the same thing.
All tool names follow a consistent snake_case verb_noun pattern (e.g., analyze_complexity, generate_solution, recommend_next_problem), making it easy for an agent to predict functionality.
23 tools is slightly above the typical ideal range, but given the broad scope of competitive programming mentoring (analysis, generation, testing, debugging, learning), it is justified and not excessive.
The tool set covers the full lifecycle of problem solving: understanding constraints, detecting patterns, selecting algorithms, generating solutions, testing, debugging, optimizing, and even recommending further practice. No obvious gaps.
Code intelligence platform for AI agents. 20 tools for architecture, security & impact analysis.
33 tools that make AI write, implement, and verify intent against explicit, testable constraints.
Search Luogu problems, fetch statements, explore problem sets and get practice recommendations.
AI-native git hosting — repos, PRs, issues, CI gates, and AI code review over MCP (60 tools).
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