Compute Indian Income Tax under Budget 2025-26 New Tax Regime (with Section 87A rebate & ₹75,000 standard deduction) vs Old Tax Regime.
| Name | Required | Description | Default |
|---|---|---|---|
| ctc | Yes | Annual Cost-to-Company / Gross Salary in INR (₹) | |
| isSalaried | No | Whether taxpayer is salaried (eligible for ₹75k standard deduction) |
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 behavioral burden, and it does name the main computation features (regimes, rebate, standard deduction). It does not disclose whether surcharge/cess is included, how isSalaried=false changes the calculation, or what the output contains, leaving notable gaps for an unannotated 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, information-dense sentence with the verb and key tax details front-loaded. There is no filler or redundant restatement of the tool name.
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 two-parameter calculator the description conveys the core purpose, but with no output schema or annotations it leaves the return shape, surcharge/cess treatment, and edge-case behavior undefined. It is adequate but not complete enough to fully predict the tool's behavior.
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; ctc and isSalaried are already described in the schema. The description adds some context by mentioning the ₹75,000 standard deduction, but it does not introduce meaningfully new parameter semantics.
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 clear verb-resource pair: 'Compute Indian Income Tax' and narrows scope to Budget 2025-26 New Tax Regime vs Old Tax Regime, naming specific features (Section 87A rebate, ₹75,000 standard deduction). This is distinct from the sibling calculators, none of which address income tax.
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: an agent can infer to use this when asked for Indian income tax under new vs old regime. However, the description does not explicitly state when to prefer it over alternatives or list exclusion cases (e.g., non-salary income, surcharges/cess).
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.
