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Study Prep MCP Server

Study Prep MCP Server

by tyorus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_corpus_overview

Retrieve categories, file counts, total size, and supported extensions for your study corpus to quickly assess your document collection and plan your preparation.

Instructions

Return categories, file counts, total size, and supported extensions in the corpus.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_corpus_overview"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. While it implies a read-only operation by saying 'return', it does not disclose any behavioral traits such as performance implications, authentication needs, or whether the data is aggregated. This leaves gaps for an AI agent.

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?

The description is a single sentence with 12 words, straight to the point. It front-loads the purpose and contains no extraneous information.

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 the tool has no parameters and no annotations, the description covers the return values adequately for a simple overview. It could mention that it covers the entire corpus, but the context (no filtering) implies that. Overall sufficient for the complexity level.

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 tool has zero parameters, so schema coverage is 100%. The description adds no parameter info, but none is needed. A baseline score of 4 is appropriate for a parameterless 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?

The description clearly states the tool returns specific corpus metrics: categories, file counts, total size, and supported extensions. It distinguishes itself from sibling tools like list_documents (which lists documents) and get_document_outline (which focuses on an individual document).

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?

No explicit guidance on when to use this tool versus alternatives. The context implies it's for getting a high-level overview of the entire corpus, but lacks when-not-to-use or specific scenarios.

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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