MCP server for Veritas Acta — contribute, challenge, verify, and query contestable public records from AI coding tools.
Free
Nothing to run from here
Jupyter Editor is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
Open Installation—
p95 latency
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call success
local
runs on your machine
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calls last 7d
What it does
Model Context Protocol server providing 29 specialized tools for programmatic Jupyter notebook editing with format preservation, batch operations, and no Jupyter server required
Quickstart
# 1 — install (mcprush login holds a key from your dashboard)
npx mcprush@latest add jupyter-editor-mcp
# 2 — ask your agent something
> Model Context Protocol server providing 29 specialized tools for programmatic Jupyter notebook editing with format preservation, batch
Jupyter Editor is free: there is no plan to choose, no cap to set and nothing that can bill you.
Publishernot claimed
JSAll 1 server
jsamuel11 server
1 category
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. This one runs on your machine: your client starts Jupyter Editor as a process under your own user, with your files and your network, so the tool surface below is what it can do to your computer rather than to a server somewhere else. It is scanned, signed and pinned to the version you choose — read the surface before you approve it.
Claude Codedeclared by this listing
This is a public server: you run it yourself and this marketplace is not in the path. It is published at https://github.com/jsamuel1/jupyter-editor-mcp.
# see the README at https://github.com/jsamuel1/jupyter-editor-mcp — this one is built and started in its own way, and inventing a one-liner here would be a command that does not runReconnect, or start a new session, and the tools appear in the model’s tool list.
Free to install, and it runs from its publisher’s own package: no account, no key, no card.
What it writes
One config entry your client uses to start the process locally. A local server runs with your file system and your network, which is why it is priced without metering.
What you approve
This listing has not published its tool surface here yet, so what a client will ask you to approve is whatever the manifest declares at install time.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
No tool surface on file
The catalogue records 0 tools on this listing, and the manifest behind them has not been imported into the marketplace yet. Nothing is shown here rather than another listing’s tools, which is what this page used to do.
Freeyour plan
$0 forever
- Every tool, no call limit
- No card, no account needed
- Source published under a licence you can read
- Runs on your machine — nothing of it reaches our gateway
- Nothing to cap, because nothing bills
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable. Jupyter Editor costs nothing to install and nothing to call, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
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Alert at 80% · then the tool returns a normal MCP error
Used this month: $8.42
$16.58 left
ATrust score 99 / 100
Release 0.2.2 · scanned 26 Aug 2026
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
Threat score100
Client score99
Findings0 threat · 2 capability
Capabilityminimal
Coveragesource
Verificationrepo
Surface digest4275cdace66b8461f11824361030c6d48a670ec96e41cc5df3a06a343e232408
Score vectorthreat 100 → client 99
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | minimal | capability blast radius (minimal) — client exposure if the model is manipulated | −0 |
| verification-discount | repo | publisher verification (public source) — no provenance, but the source is public and inspectable | −1 |
| coverage-honesty | source | inspection depth (source) — how much of the target the scan could see | −0 |
score = clamp(round(threatScore − E_cap − E_ver − E_cov), 0, 100). No model, no opinion, no network call at scoring time: the same methodology version against the same bytes always produces this number, and every line above is a field of the report rather than a summary of it.
What the scan could actually read
A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.
Tool surfaceread
Implementation sourceread
Package metadataread
Live transportnot available
Tools were statically extracted from the published source (29 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Capability — what it could do if the model were manipulated
Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. minimal is the level these add up to.
| Tool | Capability tags | Why the tag was assigned |
|---|---|---|
| ipynb_read_notebook | no tags | |
| ipynb_list_cells | no tags | |
| ipynb_get_cell | no tags | |
| ipynb_search_cells | no tags | |
| ipynb_replace_cell | no tags | |
| ipynb_insert_cell | no tags | |
| ipynb_append_cell | no tags | |
| ipynb_delete_cell | no tags | |
| ipynb_str_replace_in_cell | no tags | |
| ipynb_get_metadata | no tags | |
| ipynb_update_metadata | no tags | |
| ipynb_set_kernel | no tags | |
| ipynb_list_available_kernels | no tags | |
| ipynb_replace_cells_batch | no tags | |
| ipynb_delete_cells_batch | no tags | |
| ipynb_insert_cells_batch | no tags | |
| ipynb_search_replace_all | no tags | |
| ipynb_reorder_cells | no tags | |
| ipynb_filter_cells | no tags | |
| ipynb_merge_notebooks | no tags | |
| ipynb_split_notebook | no tags | |
| ipynb_apply_to_notebooks | no tags | |
| ipynb_search_notebooks | no tags | |
| ipynb_sync_metadata | no tags | |
| ipynb_extract_cells | no tags | |
| ipynb_clear_outputs | no tags | |
| ipynb_validate_notebook | no tags | |
| ipynb_get_notebook_info | no tags | |
| ipynb_validate_notebooks_batch | no tags |
Toxic-flow graph
The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.
The public result for this release does not print the flow graph, so there is nothing to show here. That is not the same as "no paths were found": what the scan did read is above, under coverage.
Supply chain and provenance
Integrityfirst-seen
Surface digest4275cdace66b8461
Scanned2026-08-26
This is the first scan of this surface here, so there is nothing yet to compare it against.
Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.
Release history
Pinned to 0.2.2 — the install command below asks for that release. A pin is part of an install, so it is kept for this visit and written down when you install.
0.2.2
undated
installedNo release note was published with this version.
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no reviews yet
Reviews are earned
Only accounts with at least 50 real tool calls against this server in the last 90 days can post. Ratings are weighted by how much the reviewer actually uses it, and publishers can reply once per review.
Reviews are earned
Writing one takes an account with at least 50 real tool calls against Jupyter Editor in the last 90 days. That is the whole gate — there is no other way to post, which is why the counts beside each review are worth reading.
No reviews yet
Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes an account with 50 real tool calls against it.