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rajyash205

INDUSS Research Intelligence MCP Server

by rajyash205

negative_news

Read-only

Identify adverse media, complaints, and public-sentiment red flags about a company to support due-diligence and risk screening.

Instructions

Screens news and public-sentiment sources (Glassdoor, Reddit) for adverse media and complaints about a company (fraud, layoffs, defaults, employee/public controversy) for due-diligence / risk-screening purposes. For hard regulatory/legal records (SEBI, NCLT, court cases), use litigation_history instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, covering safety and scope. The description adds value by specifying the exact sources (Glassdoor, Reddit) and the categories of adverse media (fraud, layoffs, defaults, controversy), which are behavioral traits beyond the annotations. However, it doesn't describe return format or pagination, so it doesn't fully exhaust the behavior.

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 zero waste. The purpose is front-loaded, and the alternative is given in a single clear clause. No redundancy or filler.

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

Completeness2/5

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

The tool has a nested object parameter with multiple fields and no output schema, so the description must guide input construction. It does not mention required fields (like company), defaults (like date or country), or what the tool returns. The only useful guidance is the purpose and the sibling routing, but not enough for correct invocation.

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

Parameters1/5

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

Schema description coverage is 0%, so the description must compensate by explaining the context parameter. It does not mention that context requires a company name or any other field. The description only explains the tool's purpose, not how to fill its input. This is a critical gap for an agent to correctly invoke the tool.

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?

States a specific verb ('screens') and resource ('news and public-sentiment sources' - Glassdoor, Reddit) and clearly distinguishes from litigation_history for hard legal records. The purpose is precise and unambiguous.

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 names the alternative tool (litigation_history) and the condition that selects it ('hard regulatory/legal records'). This is direct routing guidance with no inference needed.

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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