Scientific solver: polynomial quadratic root solver (ax^2 + bx + c = 0) and 2-unknown simultaneous linear equations.
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | ||
| b | Yes | ||
| c | Yes | ||
| a2 | No | ||
| b2 | No | ||
| c2 | No | ||
| type | No | quadratic |
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 does not mention how the mode is selected via the 'type' parameter, what output shape is returned, or how edge cases like negative discriminants or singular systems are handled.
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 one sentence with no filler, front-loading the core purpose. The generic opening phrase 'Scientific solver' adds little value, but the rest is tightly packed.
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?
Without an output schema and with 7 parameters, the description is under-specified. It does not explain how to invoke the simultaneous mode or what result to expect, so an agent is unlikely to call the tool correctly for the non-default path.
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. The quadratic formula defines a, b, and c as coefficients, but a2, b2, c2, and the 'type' parameter are never explained, leaving the simultaneous-equation mode ambiguous.
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 names specific mathematical operations: solving a quadratic equation ax^2 + bx + c = 0 and 2-unknown simultaneous linear equations. This is a clear resource-and-operation mapping, though it does not explicitly contrast the tool with any sibling calculators.
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?
Usage is implied by the mathematical formulas: an agent can infer that this tool is for quadratic roots or simultaneous linear equations. However, there is no explicit guidance about when to choose this tool over alternatives, nor any exclusions or prerequisites.
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.
