AI-powered security and quality code review. Analyzes for vulnerabilities, anti-patterns, performance issues, and best practices. Returns issues with severity, suggestions, and an overall score.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Source code to review | |
| focus | No | Focus area: security, quality, performance, or all (default: all) | |
| language | No | Programming language (auto-detected if omitted) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it states what the tool does and what it returns: 'Returns issues with severity, suggestions, and an overall score.' It does not mention side effects or limitations, but for a read-only analysis tool this is sufficient disclosure.
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 two sentences, front-loaded with the core purpose in the first sentence, and every sentence adds value. The only minor filler is 'AI-powered,' but it does not hurt clarity or efficiency.
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 no output schema, the description compensates by explaining what the tool returns (issues with severity, suggestions, score). It covers the tool's scope and parameters sufficiently for a medium-complexity tool. It could mention edge cases like unsupported languages, but the core context is complete.
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 baseline is 3. The description mentions focus areas (vulnerabilities, performance, etc.) which maps to the 'focus' parameter but does not add substantial detail beyond the schema. No further parameter clarification is needed.
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 a specific verb ('review') and resource ('code') with a detailed scope: 'Analyzes for vulnerabilities, anti-patterns, performance issues, and best practices.' It is unambiguous and well-distinguished from sibling tools like analyze_sentiment or scrape_url, which handle non-code content.
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 gives clear context that this tool is for code review, and the sibling tools are obviously unrelated, so an agent can infer when to use it. However, it does not explicitly state exclusions or mention alternatives, though the context makes the choice straightforward.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: sentiment analysis, structured data extraction, content generation, health check, tool listing, code review, web scraping, screenshot, and web search. There is minor overlap between scrape_url and extract_structured_data, but the different outputs (raw text vs. structured JSON) make them easily distinguishable.
Most tool names follow the verb_noun snake_case pattern (e.g., analyze_sentiment, scrape_url). The only outlier is 'health', which is a single noun rather than a verb_noun like 'check_health'. This minor inconsistency slightly reduces coherence.
With 9 tools, the server is well-scoped for a general-purpose utility toolkit. Each tool serves a useful function without redundancy, and the count is within the ideal range (3-15) for clarity and manageability.
The tool set covers a broad range of common AI/automation tasks such as text analysis, web scraping, content generation, search, and code review. While some potential utilities (e.g., translation or file conversion) are missing, the lack of a specific domain makes the set feel reasonably complete for a general-purpose toolkit.