Skip to main content
Glama
Vaquill-AI

Vaquill-AI/vaquill-mcp

Official

search_legislation

Search 23,000+ Indian acts and regulations using semantic queries. Find specific provisions, definitions, and penalties filtered by category, state, department, or year.

Instructions

Search 23,000+ Indian acts, regulations, and legislation using semantic search. Find specific statutory provisions, definitions, penalties, and procedures. Filter by category (central, state, regulatory), state, department (SEBI, RBI, TRAI, etc.), and year range. Returns relevant act sections with text excerpts, section numbers, provision type, and PDF links. Cost: 1 credit. Use for questions like 'What is the penalty for insider trading under SEBI Act?' or 'Definition of goods under GST Act'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query
categoryNoFilter: central, state, regulatory, repealed, spent
stateNoFilter by state slug (e.g. 'maharashtra', 'delhi')
departmentNoFilter by regulatory body (e.g. 'sebi', 'rbi')
yearFromNoMinimum year (inclusive)
yearToNoMaximum year (inclusive)
sectionNumberNoFilter by exact section number (e.g. '23', '302', '498A')
actTitleNoFilter by act title substring (e.g. 'Indian Penal Code', 'BNSS')
pageSizeNoResults per page (1-50)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Discloses semantic search behavior, return structure (sections, excerpts, PDF links), and cost. No annotations provided, but description adequately covers key behavioral aspects beyond defaults.

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?

Three concise sentences cover purpose, filters, return type, and cost. No fluff; each sentence adds value, front-loaded with key information.

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

Completeness5/5

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

Despite high parameter count and output schema existence, description fully captures tool's capability, filters, and typical use cases. Includes cost and example queries, making it complete for agent invocation.

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?

All 9 parameters documented in schema (100% coverage). Description adds meaning with examples (e.g., 'central, state, regulatory' for category, 'SEBI, RBI' for department) and demonstrates usage via query examples.

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 states it searches 23,000+ Indian acts/legislation using semantic search, returns relevant sections with excerpts, and contrasts with siblings like get_act_text and search_us_statutes.

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

Usage Guidelines4/5

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

Provides example queries, lists filter options, and mentions cost. Does not explicitly state when not to use, but context from sibling tools implies it's for search, not full-text retrieval.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Vaquill-AI/vaquill-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server