INDUSS Research Intelligence MCP Server

market_size

Read-only

Retrieve market size and CAGR figures for any industry from analyst research sources, extracting numeric estimates via pattern matching to support investment and market analysis.

Instructions

Finds market size and CAGR figures for an industry from analyst/research sources (IMARC, Statista, McKinsey, NASSCOM, etc.) and extracts numeric estimates via pattern matching. Pass context.sector when known; if omitted, falls back to searching around context.company's own market.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "context": "<context>"
    },
    "name": "market_size"
  }
}

Input Schema

Name	Required	Description	Default
`context`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 8, 2026

TDQS

A4.4/5.0

Behavior4/5

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

The annotations already provide readOnlyHint and openWorldHint. The description adds useful behavioral context: results are numeric estimates extracted via pattern matching from analyst/research sources, and the sector-to-company fallback behavior. It does not mention no-result behavior, but it goes beyond what the annotations convey.

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?

Two sentences with no filler. The first sentence states the tool's purpose, method, and sources; the second gives parameter guidance. Information is front-loaded and every sentence earns its place.

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?

For a read-only extraction tool with no output schema, it communicates the purpose, sources, extraction method, and parameter fallback. It does not specify the exact return shape, units, or behavior when no figures are found, but these are minor gaps given the tool's narrow scope and simple input context.

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 description explains the relationship between context.sector and context.company, which is not evident from the schema alone, and it tells the agent which to set. The nested schema fields have their own descriptions, though the top-level context parameter lacks one; a little more about expected units or country defaults would round this out.

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?

The description clearly identifies the action ('Finds') and the resource ('market size and CAGR figures for an industry'), and it names concrete analyst/research sources. It also explicitly distinguishes the company-fallback behavior, so an agent can tell this tool apart from broader industry-overview or company-profile tools.

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?

It provides explicit parameter guidance: pass context.sector when known, otherwise the tool falls back to searching around context.company's own market. It does not name sibling alternatives or state when not to use it, but the intended usage context is clear.

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