armavita-originality-ai-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a read-only analysis tool (implied by 'analyze' and 'returns'), mentions performance characteristics ('Fast and cheap'), and specifies what it returns (readability scores and grammar errors). However, it doesn't cover potential limitations like content length restrictions or error handling.

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 efficiently structured in three sentences that each serve distinct purposes: first states core functionality and exclusions, second details return values, third provides usage guidance and performance notes. Every sentence adds value with zero wasted words.

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?

For a tool with no annotations and no output schema, the description does well by specifying what the tool returns and its behavioral characteristics. However, without an output schema, it could benefit from more detail about the return format structure. The description compensates reasonably but doesn't fully bridge the gap for a tool with multiple return metrics.

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 schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain content format requirements or title usage). This meets the baseline for high 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 with specific verbs ('analyze content readability and grammar') and resources ('content'), and distinguishes it from siblings by explicitly stating what it does NOT do ('no AI detection or plagiarism'). It also mentions the specific metrics returned (Flesch Reading Ease, grade level, etc.), making the purpose highly specific and differentiated.

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 explicit guidance on when to use this tool vs alternatives: it states 'no AI detection or plagiarism' (implying use scan_ai or scan_plagiarism for those), and gives a specific target use case ('Use to check if content meets the 8th-9th grade reading level target'). This clearly defines the tool's niche among the sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.