Physics MCP Server

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 what the tool does (calculates flow properties), includes the return format (a dict with specific keys), and provides an example with expected results. It does not mention error handling, performance limits, or side effects, but for a calculation tool, this is reasonably comprehensive.

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 well-structured and appropriately sized. It starts with a clear purpose, explains the underlying principles, lists parameters with details, specifies returns, and provides a practical example. Every sentence adds value without redundancy, making it easy to scan and understand.

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 complexity (a physics calculation with 4 parameters), no annotations, and no output schema, the description is highly complete. It covers the tool's purpose, parameters, return values, and includes an example with expected outcomes. This provides all necessary context for an AI agent to use the tool correctly.

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 description coverage is 0%, so the description must fully compensate. It does so by clearly documenting all four parameters with their meanings and units (e.g., 'inlet_diameter: Inlet diameter in meters'), which adds essential semantics beyond the bare schema. This is thorough and necessary given the lack of schema descriptions.

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: 'Calculate Venturi effect (flow through constriction).' It specifies the exact calculation (using continuity equation and Bernoulli's principle) and distinguishes it from siblings like 'calculate_bernoulli' by focusing on the Venturi effect specifically, not just Bernoulli's principle in general.

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 clear context for when to use this tool: for calculating flow through a constriction using the Venturi effect. It includes an example scenario ('Example - Venturi meter:') that illustrates a practical application. However, it does not explicitly state when not to use it or name alternative tools for similar calculations.

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