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itr-agent

itr-agent

by panchasarapavan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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compute_deductions

Calculate Chapter VI-A income-tax deduction breakup and enforce statutory caps so you can see eligible deduction amounts under applicable limits.

Instructions

Return the Chapter VI-A deduction breakup with statutory caps enforced.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "compute_deductions"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden, and "statutory caps enforced" is a genuinely useful disclosure about what the computation guarantees. However, it says nothing about data prerequisites, whether the result is deterministic/pure, which assessment year's rules apply, or failure behavior on missing inputs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler, hedging, or redundancy. Every phrase (Chapter VI-A, breakup, statutory caps) carries information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The output schema covers the return shape and the zero-parameter schema is simple, so the description need not explain results. What is missing is pipeline context: what data must already be ingested and how this relates to compute_tax and validate_itr, which matters for a calculator in a 15-tool workflow.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters and the schema is trivially complete, so there is no parameter meaning for the description to add. Baseline applies; the implicit assumption of pre-ingested data could have been stated but is not a parameter-semantics gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

"Return the Chapter VI-A deduction breakup" names a specific verb and resource, and "Chapter VI-A" cleanly separates it from compute_tax and compute_capital_gains among the siblings. It stops short of explicitly naming those alternatives, but the domain term does most of the differentiation work.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use statement, no ordering guidance (e.g. whether ingestion or compute_tax must run first), and no mention of when to prefer this over compute_tax. Usage is only inferable from the Chapter VI-A reference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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