Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Computer or cloud
Academic Research Kernel
Overview
Description from the maker (original English)
Deterministic research state, evidence validation and statistical recomputation.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiVery new↓ 138/wk · ★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Search & AI memory
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"academic-research-kernel": {
"command": "uvx",
"args": [
"academic-research-kernel",
"mcp"
]
}
}
}Run this in your terminal
claude mcp add academic-research-kernel -- uvx academic-research-kernel mcpOr copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"academic-research-kernel": {
"command": "uvx",
"args": [
"academic-research-kernel",
"mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"academic-research-kernel": {
"type": "stdio",
"command": "uvx",
"args": [
"academic-research-kernel",
"mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account xngg1021, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Search & AI memoryi
- More stars than 0% of the other 5688 servers in this category
- Published to the registry more recently than 57% of the other 5688 servers in this category
- Basic checks equal to or better than 100% of the other 5688 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Academic Research Kernel safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (2.1.0) reached the registry 30 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: Context7, Codebase Memory, Exa
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Codebase Memory
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Runs on your computer
Exa
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Runs in the cloud✓ exa.ai
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
academic-research-kernel
English · 简体中文 · 繁體中文 · 日本語 · 한국어 · Deutsch · Français · Español · Português · Русский · Bahasa Indonesia · Italiano · हिन्दी · العربية · বাংলা · اردو · Tiếng Việt · Türkçe · فارسی · Kiswahili · Polski
This repository is a practical toolkit for verifying scholarly sources, checking statistical numbers, and tracking research evidence with AI agents. When researchers use language models to read papers or draft literature reviews, models often invent citations, misread tables, or lose track of which page supports a specific claim. To prevent this, academic-research-kernel packages 14 scholarly workflows and 12 deterministic verification tools behind a standard Model Context Protocol (MCP) server and command-line interface. You can use it to check paper identities and retractions, recompute reported statistics, read papers into source-linked reports, manage systematic review screening logs, and record every claim alongside its supporting evidence and negative results. It works directly with Claude Code, Cursor, Codex, Gemini CLI, and Hermes Agent. Validation scope and external service limits are documented in the project audit.
Author: Junfu Shi (SJF, xngg1021), Hermes Agent. License: Source Lineage License 1.0.
Quick start
If you want your AI agent to read a paper, check its numbers, and generate a report, install the package with PDF support and export the paper-research workflow into your agent's skill directory; if you want to connect the 12 verification tools as a background MCP server for an MCP client, install uv and start the stdio server:
# 1. Read PDF papers and generate reports with your local AI agentuv tool install "academic-research-kernel[research]==2.2.0" academic-research-kernel research skill --output ./paper-research
2. Or start the background stdio MCP server for an MCP client
uvx academic-research-kernel@2.2.0 mcp
Note that running mcp in a terminal waits silently for JSON-RPC requests from an MCP client rather than opening an interactive menu. The 2.2.0 release page lists package hashes and publication status. If you download the release wheel, you can also run it locally with uvx --from ./academic_research_kernel-2.2.0-py3-none-any.whl academic-research-kernel mcp. The package supports Python 3.10–3.14, and uvx --python 3.12 selects a specific Python version. The base installation includes all 12 verification tools and loads statistical libraries on demand when statistical recomputation is called.
Verify installation
uvx academic-research-kernel@2.2.0 doctoruvx academic-research-kernel@2.2.0 --version
The doctor command runs offline and checks runtime dependencies, JSON schemas, and all 12 tool definitions. Leaving optional external APIs unconfigured will not fail the check. Pass doctor --json if you need machine-readable output.
Package install
uv tool install academic-research-kernel==2.2.0academic-research-kernel doctor academic-research-kernel mcp
You can also install the package with pipx install academic-research-kernel==2.2.0 or python -m pip install academic-research-kernel==2.2.0 inside a virtual environment. Installing with [full] adds libraries used in extended scientific computing examples. Installing with [research] adds pypdf==6.19.0 and fonttools==4.65.0 so PDF font decoding behaves identically across clean environments on Python 3.10 and newer. Installing with [qa] adds repository test tools on top of those parsing libraries. HTML, XML, DOCX, and XLSX reading use the Python standard library, while GPU backends and paid scholarly databases remain optional.
Hermes
Make sure uv is on the Hermes process PATH, then install and enable the plugin:
hermes plugins install xngg1021/academic-research-kernel --no-enablehermes plugins enable academic-skills
The plugin keeps the academic-skills name for compatibility and includes all 14 skills, including paper-research. Its mcp.json configuration runs uv run --frozen --no-dev --no-editable with Python 3.12 and the repository's uv.lock file, storing its isolated environment under ${PLUGIN_DATA}/runtime. On first run, it installs its own dependencies without modifying Hermes or system Python packages. If you prefer installing individual skills without the plugin runtime, see Integration & Portable Usage.
Run from source
git clone https://github.com/xngg1021/academic-research-kernel.gitcd academic-research-kernel uv sync --frozen --no-dev --no-editable uv run --frozen --no-dev --no-editable academic-research-kernel doctor uv run --frozen --no-dev --no-editable python scripts/mcp_server.py
See distribution and release for dependency groups, reproducible build steps, and release verification checkpoints.
License
Snapshots containing this notice apply Source Lineage License 1.0 to the covered material and rights described in LICENSE-APPLICATION.md. The initial SLL commit and the subsequent boundary-recording commit are recorded in LICENSE-HISTORY.md and SOURCE-LINEAGE.md. Later snapshots that keep this notice carry the same license scope.
Historical snapshots through commit 439ce4a29d6d2d9137308d7f9f045863e9f0c5b3 were distributed under the MIT License. Users of those earlier snapshots keep their MIT permissions and do not need to migrate to SLL. The previous MIT license text and tests/upstream/LICENSE are preserved unchanged.
SLL permits use, study, modification, commercial use, redistribution, and proprietary extensions, provided you keep the required license and source-lineage notices. It is not copyleft and does not require disclosing your source code. Running a network service without distributing software copies does not trigger the source-lineage notice requirement. The license includes no express patent grant, and third-party files remain under their original licenses. The English LICENSE text is authoritative; LicenseRef-Source-Lineage-1.0 is a local identifier rather than an SPDX or OSI-approved code. Contribution rules are separate from downstream license rights.
Skills
| Skill | Version | What it does |
|---|---|---|
skills/academic-source-verification | 1.2.0 | Check paper metadata and citation counts across databases, inspect corrections or retractions, find open-access full text, and verify that a local PDF matches the cited work |
skills/literature-analysis | 1.3.0 | Twelve everyday workflows: find similar papers, check text overlap, search for counter-evidence, summarize author histories, run mock peer review, check reasoning flaws, build comparison tables, suggest target journals, export BibTeX, read bilingually, spot research gaps, and plan reproduction |
skills/academic-writing | 1.1.1 | Revise academic prose, format citations (ISO 690, APA, MLA, Chicago, IEEE, AMA, and regional standards), check journal author guidelines, prepare submission files, and verify anonymization for blind review |
skills/math-computation | 1.2.1 | Route symbolic, numerical, and statistical calculation tasks to verified Python tools, accompanied by four domain reference guides |
skills/quantitative-paper-audit | 1.1.0 | Recompute reported effect sizes, p-values, confidence intervals, odds or risk ratios, and statistical power from summary numbers to catch reporting errors |
skills/research-reproducibility | 1.0.1 | Audit whether a paper's code, data, dependencies, and reported metrics can be reproduced across a 14-step checklist |
skills/systematic-review-meta-analysis | 1.0.1 | Record PRISMA search and screening logs, convert effect sizes, test heterogeneity, pool fixed or random effects, and run sensitivity and publication-bias checks |
skills/literature-watch | 1.1.0 | Monitor OpenAlex and Crossref weekly for new papers by topic, author, or cited DOI, deduplicating results so only new works are reported |
skills/retraction-watch | 1.1.0 | Recheck a DOI watchlist weekly against OpenAlex retraction flags and Crossref update records, reporting only papers whose status changed |
skills/research-object-identity | 1.1.0 | Normalize DOIs, arXiv IDs, and file hashes into canonical records, verify resource status, and trace how derived files were produced from raw sources |
skills/claim-evidence-graph | 1.0.0 | Link each scientific claim to the exact source passage, table, or calculation record that supports or contradicts it |
skills/decision-ledger | 1.0.0 | Keep an append-only Research Decision Log of methodological choices, failed attempts, reasons for dropping a direction, and later outcome revisions |
skills/paper-research | 1.0.0 | Read a paper and its supplements from a DOI, URL, or local folder, verify extracted numbers, save structured research state, and generate source-linked Chinese reports or two-paper comparisons |
skills/cross-review-five | 2.0.0 | Coordinate independent blind reviews across multiple language models or subagents, merge overlapping points, assign cross-challenges, and grade issues from P0 to P3 while preserving unresolved dissent |
Across the 14 skills, 22 Markdown reference guides are loaded on demand when a task requires them.
Scholarly Standards & Multi-Profile Baseline
Citation formats, reporting checklists, and metadata rules depend on your target journal, funder, discipline, and region. This repository uses ISO 690:2021 (bibliographic references), ISO 5127:2017 (documentation vocabulary), and W3C PROV (provenance records) as its international baseline, alongside regional standards such as GB/T 7714-2025 in Mainland China, UNE-ISO 690:2024 in Spain, and DIN ISO 690:2021 in Germany, plus disciplinary guides including APA 7th, IEEE, ACM, Vancouver, Chicago, PRISMA 2020, and ICMJE. A target journal's own instructions always override default settings. See the Scholarly Standards Architecture and Natural Terminology Guide.
When saving research state, the kernel checks that DOIs and external identifiers match, prevents contradictory statistical verdicts on the same check, and tracks unresolved questions in a shared review list. Cache verification rebuilds state directly from saved source packages and hash bindings rather than trusting summary lists, so deleting both a record and its cache entry will still be caught during replay. Details are recorded in the four-blocker closeout.
Integration & Portable Usage
1. Universal Agent Plugins v1 & Model Context Protocol (MCP)
This repository follows the vendor-neutral Agent Plugins v1 format (plugin.json) and exposes its verification and statistical tools through a standard stdio MCP server (mcp.json / scripts/mcp_server.py). You can load it directly in Claude Code, Cursor, Codex, Gemini CLI, or any other MCP-compatible client. Although the repository is named academic-research-kernel, the plugin name academic-skills and existing MCP aliases remain unchanged so existing configurations keep working:
# Only the beginning is shown; read the rest in the repo