Aerospace & orbital mechanics: calculate Tsiolkovsky rocket equation delta-v budget, mass ratio, and propellant consumption.
| Name | Required | Description | Default |
|---|---|---|---|
| gravityMs2 | No | Standard gravity g0 in m/s^2 | |
| finalMassKg | Yes | Dry burnout mass mf in kg | |
| initialMassKg | Yes | Wet launch mass m0 in kg | |
| specificImpulseSeconds | Yes | Engine specific impulse Isp in seconds |
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?
With no annotations, the description must carry the behavioral burden; it clearly states that this is a calculation, not a mutation, and lists computed quantities. It does not disclose assumptions (e.g., ideal rocket equation) or output formatting, but for a pure math tool this is minimally adequate.
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?
One front-loaded sentence with no filler; every phrase adds meaning.
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?
The schema documents all four inputs with defaults and units, and the description names the calculated outputs (delta-v, mass ratio, propellant consumption). Without an output schema, a bit more detail about result units or shape would be ideal, but the tool can be invoked correctly as described.
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 baseline is 3 and the schema already documents all parameters and their units. The description adds no parameter-level detail such as how gravityMs2 modifies the calculation.
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 ('calculate') and names the exact resource: Tsiolkovsky rocket equation delta-v, mass ratio, and propellant consumption. The 'Aerospace & orbital mechanics' label distinguishes it from the other calculators in the sibling list.
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?
It gives a clear domain context ('Aerospace & orbital mechanics') and the exact equation/quantity, so an agent can tell when to reach for it. It does not explicitly name alternatives or exclusions, but the context is sufficient for this calculator set.
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.
