Calculate restaurant bill tipping, tax inclusion, and per-guest itemized bill split.
| Name | Required | Description | Default |
|---|---|---|---|
| numPeople | No | Number of guests dining | |
| billAmount | Yes | Subtotal or total bill before tip | |
| tipPercent | No | Tip percentage (e.g. 15, 18, 20, 25) |
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 carries the burden of behavioral disclosure. It usefully states that the tool calculates tip, handles tax inclusion, and produces a per-guest split. However, it does not clarify whether the tip is computed pre-tax or post-tax, what the returned itemized split looks like, or how the default values behave, which is meaningful ambiguity for a financial calculation tool.
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, well-structured sentence that front-loads the primary verb and packs in the key aspects: tipping, tax, and per-guest split. There is no filler or redundancy, and every phrase contributes to the agent's understanding.
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 tool is simple—3 flat parameters, no output schema—and the input schema covers the parameters well. However, the description leaves important gaps for an agent: it does not specify the shape of the returned per-guest itemized split, nor does it resolve the ambiguity around how tax is treated, which could lead to incorrect invocation for tax-related edge 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 100%, so the baseline is 3. The description adds some contextual meaning by linking 'per-guest' to numPeople and 'tax inclusion' to billAmount, but it does not provide additional detail beyond the schema about value formats, precedence, or defaults.
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 identifies the tool's function with a specific verb ('Calculate') and a concrete resource ('restaurant bill'), and distinguishes it from the unrelated sibling tools by mentioning tipping, tax inclusion, and per-guest split. However, 'tax inclusion' is slightly ambiguous—it could mean adding tax to the bill or estimating a tip based on a tax-inclusive amount—so it stops short of a perfect score.
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 phrase 'restaurant bill tipping... per-guest itemized bill split' gives a clear context for when an agent should use this tool: restaurant dining scenarios requiring tip calculation and bill splitting. It does not explicitly state when not to use it or name alternative tools, but the sibling tools are largely unrelated, so no strong exclusion is needed.
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.
