REPOGEO REPORT · LITE

datalayer/jupyter-mcp-server

Default branch main · commit af5441f7 · scanned 5/17/2026, 4:17:06 AM

GitHub: 1,104 stars · 161 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface datalayer/jupyter-mcp-server, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README H1 tagline to emphasize multi-cloud Jupyter management for AI

Why:

CURRENT

An MCP server developed for AI to connect and manage Jupyter Notebooks in real-timeDeveloped by Datalayer*

COPY-PASTE FIX

A Model Context Protocol (MCP) server for AI, unifying real-time management and orchestration of Jupyter Notebooks and kernels across diverse compute platforms and multi-cloud environments.

mediumtopics#2
Add more specific topics to reflect management, orchestration, and multi-cloud capabilities
Why:
CURRENT
```
ai, jupyter, mcp, mcp-server, tools
```
COPY-PASTE FIX
```
ai, jupyter, mcp, mcp-server, tools, mlops, orchestration, multi-cloud, jupyter-management
```

lowreadme#3

Add a 'Comparison to Alternatives' section in the README

Why:

COPY-PASTE FIX

## 🆚 Comparison to Alternatives

While tools like `nbclient` and `Jupyter Client` provide programmatic interaction with individual Jupyter instances, Jupyter MCP Server offers a higher-level management and orchestration layer. It unifies the control of Jupyter Notebooks and kernels across diverse compute platforms (local, SSH, Kubernetes, Slurm) and various cloud environments, acting as a central hub for AI/MLOps workflows, rather than a single-instance client.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface datalayer/jupyter-mcp-server

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

nbclient

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

nbclient · recommended 1×
Jupyter Client · recommended 1×
jupyter/jupyter_server · recommended 1×
papermill · recommended 1×
nbconvert · recommended 1×

CATEGORY QUERY
How can AI systems programmatically interact with Jupyter notebooks for real-time model context?
you: not recommended
AI recommended (in order):
1. nbclient
2. Jupyter Client
3. JupyterLab's REST API (jupyter/jupyter_server)
4. papermill
5. nbconvert
6. IPython.display
7. Comm.js
AI recommended 7 alternatives but never named datalayer/jupyter-mcp-server. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What solutions enable remote management and execution of code within Jupyter environments for AI applications?
you: not recommended
AI recommended (in order):
1. JupyterHub
2. Kubeflow Notebooks
3. Google Cloud Vertex AI Workbench
4. AWS SageMaker Studio
5. Azure Machine Learning Compute Instances
6. VS Code Remote Development
7. BinderHub
AI recommended 7 alternatives but never named datalayer/jupyter-mcp-server. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of datalayer/jupyter-mcp-server?
pass
AI named datalayer/jupyter-mcp-server explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts datalayer/jupyter-mcp-server in production, what risks or prerequisites should they evaluate first?
pass
AI named datalayer/jupyter-mcp-server explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo datalayer/jupyter-mcp-server solve, and who is the primary audience?
pass
AI named datalayer/jupyter-mcp-server explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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datalayer/jupyter-mcp-server — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite