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Meru-Fin-Tech

HelloBooks AI MCP Server

by Meru-Fin-Tech
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TypeScript
Hybrid

lookup_tax_rate

Look up statutory tax rate slabs by exact ID or by country and category. Returns matched rate, score, and official source URL.

Instructions

Pick a single statutory tax-rate slab — either by exact id (e.g. IN-standard-18, GB-zero-0, CA-hst-13-on) for a deterministic lookup, or by country + free-text category (e.g. "office supplies", "restaurant", "exports", "domestic fuel") for a fuzzy best-match. Returns the matched rate, the match score, and the authoritative source URL. Use this when a user asks "what slab does X fall into in India?" or "what VAT rate applies to children's car seats?". For broader exploration (all slabs in a country / all rates of one scheme), use list_tax_rates. No customer data — public statutory reference only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoExact rate id, e.g. ``IN-standard-18`` or ``GB-zero-0``. When set, country/category are ignored.
countryNoCountry to search within. Required when ``id`` is not provided.
categoryNoFree-text query — "office supplies", "restaurant", "exports", "domestic fuel".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.5.0

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations, description carries full burden. It explains the two lookup modes, confirms it's a deterministic vs fuzzy match, and states returns (rate, score, source URL). Lacks mention of error handling or edge cases like no match.

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?

Well-structured, front-loaded with purpose, then details modes, examples, and sibling differentiation. Every sentence adds value; no redundancy.

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

Completeness4/5

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

Given 3 params, full schema coverage, no output schema, and no annotations, description covers both use cases, examples, and sibling tool. Lacks explicit return format details but lists returned fields. Slightly incomplete for edge cases.

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

Parameters5/5

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

Schema coverage is 100%, baseline 3. Description adds significant value: shows id format examples, explains id overrides country/category, notes category is free-text fuzzy, and provides example queries. This exceeds baseline.

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

Purpose5/5

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

Description clearly specifies the tool picks a single tax-rate slab by exact id or fuzzy country+category match. It uses strong verbs ('pick','lookup') and distinguishes from sibling list_tax_rates for broader exploration.

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

Usage Guidelines5/5

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

Explicitly tells when to use (e.g., 'what slab does X fall into in India?') and when not to (use list_tax_rates for broader exploration). Provides concrete examples and excludes customer data.

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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