SportSciMCP

v0.4 · Your sports-science research desk inside any AI assistant.

SportSciMCP is a Model Context Protocol (MCP) server for research, performance, and injury science. Search literature and datasets across PubMed, OSF, Figshare, PhysioNet, Kaggle, and more — run training-load analysis, RTP checklists, and pipe YouTube lectures into NotebookLM — all from one conversation.

Works with Cursor, Claude Desktop, Gemini, GitHub Copilot, OpenAI Codex, and Cursor Agent.

Pick what you use. Enable sources in config/sources.yaml. Add API keys only for the services you turn on. Everything else keeps working.

Repository: https://github.com/The-TechLab/SportSciMCP

Why SportSciMCP?

Without it	With SportSciMCP
Tab-hopping PubMed, OSF, Zenodo, Kaggle	`search_all` — one prompt, many sources
Manual abstract copy-paste	`save_research_brief` → markdown on disk
Guesswork on return-to-play	`rtp_checklist` (ACL, hamstring, ankle)
Spreadsheet load math by hand	`parse_session_csv` + `calc_training_load` (ACWR)
YouTube lecture → notes by hand	`ingest_youtube_research` → transcript → NotebookLM

Compatible AI clients

SportSciMCP uses MCP over stdio. Configure once per client:

Client	Config location
Cursor / Cursor Agent	`~/.cursor/mcp.json`
Claude Desktop	`~/Library/Application Support/Claude/claude_desktop_config.json` (macOS)
Gemini	MCP-capable Gemini tooling that accepts stdio servers
GitHub Copilot	VS Code MCP settings (when enabled)
OpenAI Codex	Agent / IDE MCP server list

{
  "mcpServers": {
    "sportsci": {
      "command": "/path/to/.cursor/mcp-wrappers/sportscience.sh",
      "args": []
    }
  }
}

Restart your client after adding the server.

Quick start

1. Clone & install

git clone https://github.com/The-TechLab/SportSciMCP.git
cd SportSciMCP

python3 -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
pip install -e .

2. Optional — API keys

cp config/secrets.example.env ~/.cursor/mcp-secrets.env
# Edit: uncomment only the keys you need

3. Wrapper script (Cursor example)

~/.cursor/mcp-wrappers/sportscience.sh:

#!/usr/bin/env bash
set -euo pipefail
if [[ -f "$HOME/.cursor/mcp-secrets.env" ]]; then
  set -a
  # shellcheck source=/dev/null
  source "$HOME/.cursor/mcp-secrets.env"
  set +a
fi
export PYTHONPATH="/absolute/path/to/SportSciMCP${PYTHONPATH:+:$PYTHONPATH}"
exec /absolute/path/to/SportSciMCP/.venv/bin/python -m sportsci_mcp.server

chmod +x ~/.cursor/mcp-wrappers/sportscience.sh

4. Verify

"Use SportSciMCP list_sources — which sources are active?"

Sources at a glance

Literature (papers & preprints)

Source	Site	Key?
pubmed	PubMed	No
openalex	OpenAlex	No
ssrn	SSRN	No (HTML search)
arxiv	arXiv	No
semantic_scholar	Semantic Scholar	Optional
core	CORE	Optional
dimensions	Dimensions.ai	Required
scorenetwork	SCORE Network	No (HTML)
osf	OSF	No
sportdiscus	SPORTDiscus	EBSCO (institutional)

Datasets

Source	Site	Key?
zenodo	Zenodo	No
physionet	PhysioNet	No
figshare	Figshare	No
simtk	SimTK	No (HTML)
motrpac	MoTrPAC	No (HTML)
mendeley_data	Mendeley Data	Optional
kaggle	Kaggle	Required

Other

Tool	Purpose
scrape_url	Any public web page → title + text

Disable any row in config/sources.yaml with enabled: false.

API keys

None needed (13+ sources work immediately)

PubMed, OpenAlex, SSRN, arXiv, CORE (no key), OSF, Figshare, PhysioNet, SimTK, MoTrPAC, SCORE Network, Zenodo, and generic scrape_url.

Optional politeness env vars: PUBMED_EMAIL, OPENALEX_EMAIL

Optional (better limits)

Variable	Where to get it
`CORE_API_KEY`	core.ac.uk/api-keys/register
`SEMANTIC_SCHOLAR_API_KEY`	semanticscholar.org/product/api
`MENDELEY_ACCESS_TOKEN`	dev.mendeley.com

Required only if you use that source

Variables	Source
`DIMENSIONS_API_KEY`	Dimensions.ai
`KAGGLE_USERNAME` + `KAGGLE_KEY`	Kaggle → Settings → API
`EBSCO_USER_ID` + `EBSCO_PASSWORD`	Your university library (SPORTDiscus)
`EBSCO_PROFILE`	Optional; default `eds`

Missing keys → source is skipped with a clear message in list_sources. No crash, no blocking other sources.

TranscriptMCP (YouTube pipeline only)

Variable	Purpose
`TRANSCRIPT_MCP_PATH`	Path to TranscriptMCP if not cloned as `../TranscriptMCP`

Template: config/secrets.example.env

Tools

Discovery

Tool	What it does
`list_sources`	Show active sources, phases, credential status
`search_literature`	Papers across enabled literature sources
`search_datasets`	Datasets across enabled dataset sources
`search_all`	Literature + datasets in one call
`get_record`	Fetch one item (`pubmed:123`, `osf:abc`, `figshare:99`, …)
`scrape_url`	Public URL → extracted text

Research workflow

Tool	What it does
`save_research_brief`	Markdown brief on disk
`papers_to_bibtex`	Refs → BibTeX
`format_for_notebooklm`	Notebook-ready markdown
`compare_papers`	Comparison table (2–5 papers)
`build_literature_review_outline`	IMRaD outline from topic + refs

Applied sports science

Tool	What it does
`parse_session_csv`	Parse GPS / sRPE / load CSV
`calc_training_load`	Load, acute/chronic, ACWR, spike flags
`lookup_norms`	CMJ, sprint, Y-balance (`data/norms.yaml`)
`rtp_checklist`	ACL, hamstring, ankle (`data/rtp/`)

NotebookLM & YouTube

Tool	What it does
`notebooklm_list_notebooks`	Aliases from config + live `nlm` list
`notebooklm_add_source`	Add URL or text via `nlm` CLI
`ingest_youtube_research`	YouTube → TranscriptMCP → NotebookLM

Requires nlm login for NotebookLM tools. See YouTube pipeline below.

Sample prompts

Copy into Cursor, Claude, Gemini, Copilot, or Codex.

Unified search

search_all for "ACL return to sport" — literature and datasets,
max 5 per source. Use pubmed, openalex, osf, zenodo, figshare.

search_all on hamstring injury prevention since 2020.

Literature

Search literature on concussion using pubmed, core, and scorenetwork.
Save briefs for the top 5 results.

Compare pubmed:38123456 and openalex:W123 — table and evidence gaps.

Build a literature review outline on Nordic hamstring programs
using these refs: [paste refs].

Datasets

Search datasets for GPS football tracking on zenodo, figshare, and physionet.

Find exercise omics data on motrpac and kaggle for endurance training.

Clinical & performance

RTP checklist for ACL, phase return_to_play.

Lookup norms for cmj_height_cm — female, soccer, collegiate.

Parse ~/data/team_srpe.csv and calculate ACWR (7-day acute, 28-day chronic).

NotebookLM & web

Format pubmed:36234567 for NotebookLM and add to acl_rehab.

Scrape https://example.com/guideline and save a brief tagged rtp.

Admin

list_sources — show active sources and which need API keys.

YouTube → TranscriptMCP → NotebookLM

The ingest_youtube_research tool runs a full pipeline:

YouTube URL
    → TranscriptMCP (yt-dlp + Whisper)
    → formatted markdown
    → NotebookLM (nlm source add)
    → optional research brief on disk

Prerequisites

TranscriptMCP cloned beside SportSciMCP:

MCP Servers/
├── SportSciMCP/
└── TranscriptMCP/

TranscriptMCP dependencies installed (yt-dlp, faster-whisper, ffmpeg)
nlm login for NotebookLM
Notebook alias in config/notebooks.yaml (e.g. acl_rehab)

Example prompt

Ingest this YouTube lecture into acl_rehab:
https://www.youtube.com/watch?v=VIDEO_ID
Save a brief tagged youtube.

What you get back

Transcript preview + length
NotebookLM add status
Optional brief file path

Configuration

File	Purpose
`config/sources.yaml`	Enable/disable each source
`config/notebooks.yaml`	NotebookLM notebook aliases → UUIDs
`config/secrets.example.env`	API key template
`sportsci_mcp/data/norms.yaml`	Performance norms (edit for your lab)
`sportsci_mcp/data/rtp/*.yaml`	RTP checklists (edit with your protocol)

Example — turn off Kaggle:

datasets:
  kaggle:
    enabled: false

Example — custom briefs folder:

export SPORTSCI_BRIEFS_DIR=~/research/briefs

Architecture

┌─────────────────────────────────────────────────────────┐
│  AI client (Cursor · Claude · Gemini · Copilot · Codex) │
└─────────────────────────┬───────────────────────────────┘
                          │ MCP stdio
┌─────────────────────────▼───────────────────────────────┐
│  SportSciMCP tools (search, load, RTP, YouTube, …)      │
└─────────────────────────┬───────────────────────────────┘
                          │
        ┌─────────────────┼─────────────────┐
        ▼                 ▼                 ▼
   Literature         Datasets      Services
   adapters           adapters      (load, rtp, nlm)
   pubmed·osf·…       zenodo·…      youtube_pipeline
        │                 │
        ▼                 ▼
   Public APIs      HTML scrape (ethical, no paywalls)

Pluggable adapters — add a file under sportsci_mcp/adapters/
Unified records — same JSON shape for papers and datasets
Fail soft — missing credentials skip that source only

Roadmap: docs/ROADMAP.md · Coming: athleteOS API (Phase 5)

Project layout

SportSciMCP/
├── config/
│   ├── sources.yaml
│   ├── notebooks.yaml
│   └── secrets.example.env
├── sportsci_mcp/
│   ├── adapters/          # pubmed, osf, figshare, kaggle, …
│   ├── services/          # load, norms, rtp, notebooklm, youtube_pipeline
│   ├── data/              # norms + RTP YAML
│   └── server.py
├── docs/ROADMAP.md
└── pyproject.toml

Run locally: python -m sportsci_mcp.server

Contributing

PRs welcome for:

New source adapters (see sportsci_mcp/adapters/base.py)
Expanded norms and RTP YAML
Bug fixes on HTML-scrape sources (site layout changes)

Keep new sources opt-in via config/sources.yaml.

License

Built for researchers, clinicians, and performance staff who live in the data.
Enable what you need · Ignore the rest · Ship the science

Name		Name	Last commit message	Last commit date
config		config
docs		docs
sportsci_mcp		sportsci_mcp
.gitignore		.gitignore
README.md		README.md
pyproject.toml		pyproject.toml

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SportSciMCP

Table of contents

Why SportSciMCP?

Compatible AI clients

Quick start

1. Clone & install

2. Optional — API keys

3. Wrapper script (Cursor example)

4. Verify

Sources at a glance

Literature (papers & preprints)

Datasets

Other

API keys

None needed (13+ sources work immediately)

Optional (better limits)

Required only if you use that source

TranscriptMCP (YouTube pipeline only)

Tools

Discovery

Research workflow

Applied sports science

NotebookLM & YouTube

Sample prompts

Unified search

Literature

Datasets

Clinical & performance

NotebookLM & web

Admin

YouTube → TranscriptMCP → NotebookLM

Prerequisites

Example prompt

What you get back

Configuration

Architecture

Project layout

Contributing

License

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages