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tyorus

Study Prep MCP Server

by tyorus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
MD_MCP_NAMENoFastMCP server namestudy-md
MD_DOCS_PATHNoRoot folder for study documents.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
get_corpus_overviewA

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

list_documentsA

List study documents with relative path, size, and type.

Args:
    category: Optional subfolder name (e.g. lpdp, uwa, learn)
    pattern: Optional glob matched against relative path (e.g. *.pdf, lpdp/*)
read_documentA

Read or extract text from a study document.

Args:
    relative_path: Path relative to docs root, e.g. lpdp/essay.pdf
    max_chars: Maximum characters to return (default 50000)
    page_start: PDF only — 1-based start page (0 = from beginning)
    page_end: PDF only — 1-based end page inclusive (0 = through last page)
search_documentsA

Search document text with snippet context around each match.

Args:
    query: Case-insensitive search string
    category: Optional subfolder filter
    max_results: Maximum number of matching files (default 10)
extract_key_termsB

Extract keywords from one document or an entire category using YAKE.

Args:
    relative_path: Optional single file path relative to docs root
    category: Optional category folder when relative_path is not set
    top_n: Number of terms to return (default 20)
get_document_outlineA

Return document outline from markdown headings or heading-like lines.

Args:
    relative_path: Path relative to docs root
get_study_chunksA

Split a document into numbered study-sized chunks.

Args:
    relative_path: Path relative to docs root
    chunk_chars: Target chunk size in characters (default 3000)
    overlap: Overlap between consecutive chunks (default 200)
prepare_study_sessionB

Bundle excerpts, key terms, and suggested AI prompts for a study topic.

Args:
    topic: Study topic or search query
    categories: Comma-separated category folders to search (empty = all)
    paths: Comma-separated relative file paths to include directly
    max_chars: Maximum total characters in the bundle (default 12000)
get_quiz_source_materialB

Return labeled excerpts and instructions for generating practice Q&A.

Args:
    relative_path: Optional single file path relative to docs root
    topic: Optional search topic when relative_path is not set
    num_excerpts: Number of excerpts to include (default 5)

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4/5.0

Scored across 9 tools

Disambiguation5/5

Each tool targets a unique aspect of study preparation, such as document overview, reading, chunking, keyword extraction, quiz generation, and session bundling. There is no overlap in functionality, making it easy for an agent to select the correct tool.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case, e.g., 'extract_key_terms', 'get_document_outline', 'list_documents'. The naming is predictable and clear throughout.

Tool Count5/5

With 9 tools, the set is well-scoped for study preparation. Each tool serves a distinct purpose without redundancy, covering corpus management, document processing, and content generation.

Completeness5/5

The tool set covers all common study needs: browsing documents, reading with pagination, extracting outlines, chunking, keyword extraction, quiz material generation, and full session preparation. There are no obvious missing operations like adding or deleting documents, but the server appears to be a read-only study aid, so this is appropriate.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources