Deployable MCP Server

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

No annotations are provided, so the description carries full burden. While it states the basic operation and return value, it doesn't disclose behavioral traits like error handling (e.g., for non-numeric inputs), performance characteristics, or any constraints. The description is minimal but doesn't contradict annotations.

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 appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured sections for Args and Returns. Every sentence earns its place with no 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?

Given the tool's low complexity (simple arithmetic) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers purpose, parameters, and return semantics, though it lacks behavioral details like error 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 0%, so the description must compensate. It adds meaningful semantics by explaining that 'a' is the 'First number' and 'b' is the 'Second number', which clarifies their roles beyond the schema's generic titles. However, it doesn't specify format details like integer vs. float.

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 specific action ('Add two numbers together') and identifies the resource (numbers). It distinguishes from sibling tools like 'divide', 'multiply', and 'subtract' by specifying the exact mathematical operation.

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 implies usage through its mathematical context but doesn't explicitly state when to use this tool versus alternatives like 'subtract' or 'multiply'. No explicit guidance on when-not-to-use or prerequisites is provided.

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

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 explains the basic mathematical operation and return value, but doesn't mention edge cases like division by zero, floating-point precision, or error handling. It provides the minimum viable behavioral information.

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 structured with a clear purpose statement, labeled parameter explanations, and return value specification. Every sentence earns its place with no wasted words, and information is front-loaded appropriately.

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 (basic arithmetic), 2 parameters, no annotations, but with an output schema, the description is nearly complete. It explains the operation, parameters, and return value. The main gap is lack of edge case handling information, but for a simple division tool, this is reasonably 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?

The schema has 0% description coverage, so the description fully compensates by clearly explaining both parameters: 'a' as the numerator and 'b' as the denominator. This adds essential meaning beyond the bare schema types, making the parameter purposes unambiguous.

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 specific mathematical operation ('divide the first number by the second') and distinguishes it from sibling tools like add, multiply, and subtract by specifying division rather than other arithmetic operations.

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 implies usage context through the mathematical terminology (numerator/denominator) but doesn't explicitly state when to use this tool versus alternatives like multiply or add. However, the mathematical operation itself provides clear inherent context for when division is appropriate.

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

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 only states the basic function ('Multiply two numbers together') and return value, but doesn't cover important aspects like error handling (e.g., overflow, invalid inputs), performance characteristics, or side effects. This leaves significant gaps for an AI agent to understand the tool's behavior fully.

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 highly concise and well-structured, with a clear purpose statement followed by labeled sections for 'Args' and 'Returns'. Every sentence earns its place by providing essential information without redundancy, making it easy to parse quickly.

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 low complexity (simple arithmetic), the presence of an output schema (which handles return values), and the description's coverage of parameters and purpose, it is mostly complete. However, the lack of usage guidelines and limited behavioral transparency (e.g., no error handling details) prevents a perfect score, as these could aid an AI agent in more nuanced scenarios.

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 adds meaningful semantics beyond the input schema, which has 0% description coverage. It explicitly defines 'a' as 'First number' and 'b' as 'Second number', clarifying their roles. However, it doesn't specify constraints like number types (e.g., integers vs. decimals) or ranges, which could be useful for more complex use cases.

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 a specific verb ('Multiply') and resource ('two numbers'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'add' or 'divide', which would require mentioning it performs multiplication specifically rather than other arithmetic operations.

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 'add' or 'divide'. It lacks context about scenarios where multiplication is appropriate, such as calculating areas or scaling values, and doesn't mention any prerequisites or exclusions.

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

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

No annotations are provided, so the description carries full burden. While it describes the basic operation, it doesn't disclose important behavioral traits like error handling (e.g., what happens with non-numeric inputs), precision/rounding behavior, or performance characteristics. The description is minimal and lacks operational context.

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 structured and concise: a clear purpose statement followed by well-organized Arg and Return sections. Every sentence earns its place, with zero wasted words. The information is front-loaded with the core operation first.

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 (basic arithmetic), 2 parameters, and the presence of an output schema, the description is reasonably complete. It explains the operation and parameters clearly. However, it could benefit from mentioning the tool's mathematical nature relative to siblings for better contextual understanding.

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?

With 0% schema description coverage, the description fully compensates by clearly explaining both parameters ('a: Number to subtract from' and 'b: Number to subtract'). It adds essential meaning beyond the bare schema, clarifying the role and order of parameters in the subtraction operation.

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 specific action ('Subtract the second number from the first') and distinguishes it from sibling tools (add, divide, multiply) by specifying the mathematical operation. It goes beyond just restating the name by explaining what subtraction means in this context.

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 implies when to use this tool (when you need to subtract numbers) but doesn't explicitly state when to choose it over alternatives like 'add' or 'divide'. There's no guidance about edge cases or prerequisites, though the mathematical context makes the usage fairly self-evident.

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