MCP server

Occam Official

fit.occam/occam

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Finds the simplest equation consistent with your data. SINDy and PySR symbolic regression via MCP.

Working at last checkVerified publisher: occam.fitAbout 4,384 tokens per request once connected4 tools

https://occam.fit/mcp/

At a glance

Type: MCP server
Publisher: occam.fit (verified by the MCP Registry)
Status: Working at last check
Tools: 4
Token cost: About 4,384 tokens per request
Sets up in: Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
Last checked: 26 Oct 2026, 08:29 UTC

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http occam https://occam.fit/mcp/

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.occam]
url = "https://occam.fit/mcp/"

Gemini CLI

{"mcpServers":{"occam":{"httpUrl":"https://occam.fit/mcp/"}}}

Cursor

{"mcpServers":{"occam":{"url":"https://occam.fit/mcp/"}}}

VS Code

{"servers":{"occam":{"type":"http","url":"https://occam.fit/mcp/"}}}

Antigravity

agy mcp add occam https://occam.fit/mcp/

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"occam","server_url":"https://occam.fit/mcp/","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"occam","server_url":"https://occam.fit/mcp/"}

feature_request ~231 tokens
Request a feature that Occam doesn't support yet. Use this when you need a capability that Occam doesn't currently offer. Requests are logged and used to prioritize development. Rate limit: 5 requests/hour per IP, 50/hour global — stricter than the compute tools' 10/hour to prevent
pysr_run ~1656 tokens
Evolutionary Symbolic Regression (PySR). Discovers algebraic equations y = f(x1, x2, ...) from feature/target data. Returns a Pareto front ranked by the complexity/accuracy tradeoff. Slower than SINDy (10-60s); searches often terminate early on convergence. For differential equation
pysr_uncertainty ~1235 tokens
Bootstrap confidence intervals for the numeric constants of a frozen expression, plus optional prediction bands on an x-grid. Typical flow: call pysr_run, pick an expression from the response (best_expression or a pareto_front entry), pass it back here with the same dataset to get C
sindy_run ~1262 tokens
Sparse Identification of Nonlinear Dynamics (SINDy). Recovers governing differential equations (dx/dt = f(x)) from time series data. Returns human-readable sparse expressions. Fast (seconds). For algebraic y = f(x) relationships without time structure, use pysr_run instead. Pri

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