Example MCP Server

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 but only states the basic operation. It doesn't mention error handling (e.g., overflow, non-integer inputs), performance characteristics, side effects, or return format. For a mathematical tool with zero annotation coverage, this leaves significant behavioral gaps.

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 perfectly concise at three words, front-loading the core functionality with zero wasted language. Every word earns its place, making it immediately scannable and efficient for an AI agent.

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 mathematical nature and lack of annotations/output schema, the description is incomplete. It doesn't address return values, error conditions, or how this tool fits within the broader mathematical operation context provided by sibling tools. For even a simple tool, more contextual information would be helpful.

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 description mentions 'two numbers' which aligns with the two parameters (a and b) in the schema, but adds no semantic meaning beyond what's evident from parameter names. With 0% schema description coverage, the description doesn't compensate by explaining parameter roles, constraints, or examples. The baseline is appropriate given the simple nature.

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 ('Add two numbers') with a specific verb ('Add') and resource ('two numbers'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'add' vs 'sum' or explain why this specific addition tool exists among mathematical operations, which prevents a perfect score.

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 like 'multiply', 'subtract', or 'divide' from the sibling list. There's no mention of use cases, prerequisites, or comparisons to other mathematical operations, leaving the agent with minimal context for tool selection.

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