Calculate 2D physics projectile kinematics: max height, horizontal range, flight time, and velocity components.
| Name | Required | Description | Default |
|---|---|---|---|
| gravityMs2 | No | Gravitational acceleration in m/s^2 | |
| initialVelocityMs | Yes | Initial launch velocity v0 in m/s | |
| launchAngleDegrees | Yes | Launch angle in degrees (0 to 90) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It lists what is calculated but does not mention underlying assumptions (e.g., no air resistance, flat ground), output format, or edge-case behavior. This is a noticeable gap for a physics calculator.
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 a single, efficient sentence that front-loads the action and resource, then lists the calculated outputs. There is no filler or redundancy.
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 simple calculator with three well-documented parameters, the description covers the main purpose and outputs. However, with no output schema, it omits return structure and physical assumptions, so the agent still has unanswered questions about the exact response format.
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 documents all three parameters and the default for gravityMs2. The description adds no parameter-level detail, so the baseline score of 3 is appropriate.
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 uses a specific verb and resource: 'Calculate 2D physics projectile kinematics' and explicitly lists the outputs (max height, horizontal range, flight time, velocity components). This clearly distinguishes it from siblings like black_scholes or rocket_deltav even without a title.
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 intended use case is implied by '2D physics projectile kinematics,' and the sibling tools are mostly unrelated calculators, so an agent can infer when to select it. However, there is no explicit statement of when to use this tool versus alternatives or any exclusions.
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.
Most tools are clearly separated by domain and target calculation, such as rocket_deltav versus projectile_motion or black_scholes versus compound_wealth. A few pairs like home_loan_emi/mortgage_piti and contractor_parity/billable_floor could be initially confused, but the descriptions resolve the intended use cases.
All tool names are lowercase snake_case and generally follow a topic-plus-suffix pattern, which is readable and consistent. The pattern is not a strict verb_noun convention, and acronym-heavy names like feie_nomad_tracker, scorp_optimizer, and casio_991_solve introduce stylistic variance.
At exactly 25 tools, this is at the heavy but still usable end of the scale. The broad spread across tax, finance, engineering, physics, math, and cloud cost makes the server feel more like several domain calculators merged into one service.
Each tool is a self-contained calculation with no missing follow-up operations, so there are no obvious dead ends for the workflows it targets. The main gaps are minor adjacent calculators—such as NPV, depreciation, or broader statistical inference—that agents could work around or obtain elsewhere.
