Skip to main content
Glama
viraj43

INDUSS Research Intelligence MCP Server

by viraj43

generate_markdown

Idempotent

Converts structured research reports into GitHub-flavored Markdown, adding a table of contents, per-section confidence and sources, and a consolidated citation list.

Instructions

Renders a structured report (see generate_report's schema) into a GitHub-flavored Markdown document with a table of contents, per-section confidence/sources, and a consolidated citation list.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoCover-page badge pills, e.g. ['Unlisted', 'Credit Assessment']
titleYes
sectionsYes
subtitleNo
brandNameNoReport letterhead name; defaults to the server's own branding
preparedByNoShown on the cover page, e.g. 'INDUSS Research Intelligence Agent'
companyNameNo
generatedAtNo
brandTaglineNoReport letterhead tagline
classificationNoCover-page eyebrow label, e.g. 'CONFIDENTIAL RESEARCH REPORT'

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already mark the tool idempotent; the description adds that the operation renders/transforms rather than mutating, and enumerates output components. It does not disclose whether the result is a returned Markdown string or a written file, which would be valuable since no output schema exists. No contradiction with annotations.

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?

One sentence with the action front-loaded and the output structure compressed into a list of meaningful deliverables. The parenthetical schema pointer is efficient and avoids repeating the nested schema.

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 description captures the core rendering behavior and output features, but with no output schema it should state whether the result is a Markdown string or a file path, and ideally indicate the intended pipeline from generate_report. It is adequate but not fully complete for a 10-parameter tool.

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

Parameters3/5

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

The schema already documents half the parameters, and the description reinforces the semantics of 'sections' by mentioning per-section confidence/sources and consolidated citations. It does not add meaning for tags, subtitle, generatedAt, or other cover-page fields, but those are relatively self-evident and the input schema carries the detail.

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?

The description names a specific action ('Renders'), a clear input ('structured report ... generate_report's schema'), and a concrete output ('GitHub-flavored Markdown document with table of contents, per-section confidence/sources, and a consolidated citation list'). It is unambiguous about what the tool produces, though it does not explicitly contrast with generate_pdf or generate_report.

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

Usage Guidelines3/5

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

The 'see generate_report's schema' reference implies it is meant to consume output produced by generate_report, and the output type differentiates it from PDF generation. However, it never explicitly states when to choose this over generate_pdf or when not to use it, nor does it describe the intended pipeline.

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

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/viraj43/Indus_mcp_latest'

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