MCP Numpy vs Jacobian
A side-by-side look at the MCP Numpy and Jacobian MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose MCP Numpy for data science tools work as a local process; choose Jacobian for data science tools as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose MCP Numpy when
- You need dedicated capabilities in the Data Science Tools domain.
- You prefer a local stdio subprocess transport.
- You want tools like
np_array, np_zeros, np_ones.
Choose Jacobian when
- You need dedicated capabilities in the Data Science Tools domain.
- You prefer a local stdio subprocess transport.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Data Science Tools | Data Science Tools |
| Quality signal | 51/100 · Good | 49/100 · Fair |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 318 |
| Directory activity | 2 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 72 | Not listed |
| Full listing | View MCP Numpy | View Jacobian |
Tools & capabilities
MCP Numpy tools 72
np_arrayCreate a NumPy array from a list.np_zerosCreate an array of zeros.np_onesCreate an array of ones.np_fullCreate an array filled with a constant value.np_arangeCreate an array with evenly spaced values within a given interval.np_linspaceCreate an array with evenly spaced numbers over a specified interval.np_eyeReturn a 2D identity array.np_diagCreate a diagonal array or extract the diagonal of an array.np_reshapeGive a new shape to an array without changing its data.np_transposeReverse or permute the axes of an array.np_concatenateJoin a sequence of arrays along an existing axis.np_splitSplit an array into multiple sub-arrays.- +60 more on the listing page
Jacobian tools
No tool names declared yet. Check the project README from the listing page.
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
MCP Numpy
{
"mcpServers": {
"mcp-numpy": {
"command": "uvx",
"args": [
"mcp-numpy"
]
}
}
}
Jacobian
{
"mcpServers": {
"jacobian": {
"command": "npx",
"args": [
"-y",
"jacobian@latest"
]
}
}
}
Frequently asked questions
MCP Numpy is categorized under Data Science Tools and uses a local stdio subprocess. Jacobian belongs to Data Science Tools and uses a local stdio subprocess. Pick MCP Numpy for data science tools work and Jacobian for data science tools.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
MCP Numpy has no declared pricing — check the project's own site. Jacobian has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add mcp-numpy and jacobian as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, MCP Numpy has a quality signal of 51/100 (Good) with 2 views, 0 installs and 0 GitHub stars. Jacobian has 49/100 (Fair) with 1 views, 0 installs and 318 GitHub stars.