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SAMI-CODEAI

Competitive Programming Mentor MCP Server

by SAMI-CODEAI

review_solution

Review competitive programming code for correctness, time complexity, bugs, and TLE risk. Accepts problem description and code in multiple languages.

Instructions

Review user code for correctness, time complexity, bugs, TLE risk, etc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesThe code to review.
problemYesThe full text of the problem description.
languageNoLanguage of the code (python, cpp, java, rust, etc.).python
error_messageNoOptional compiler/runtime error message from the judge.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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.

Conciseness4/5

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.

Completeness3/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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.

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