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Agentic Compute

Secure Python sandbox for data optimization and Matplotlib rendering via x402 microtransactions.

Connector by codelad1304 · GitHub account verified by the MCP Registry

IndexedMITLocal (stdio)
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Install

Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/codelad1304-agentic-compute

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Secure Python sandbox for data optimization and Matplotlib rendering via x402 microtransactions.

Reviews

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Permissions

What this server can do and what it asks for, from its tool list and install config. Checked Oct 25, 2026.

Tools
0 read · 1 write · 1 delete · 2 not yet classified
Secrets
None asked for
Install scripts
Not scanned yet
Runs as
Python package

Tools by what they do 4

Read from the source code; the running server may list different tools.

Write 1 · Create, change, send or run things.

  • optimize_ga_securely

Delete 1 · Delete, drop, revoke or wipe things.

  • execute_code_securely

Not yet classified 2 · We could not tell from the name and description.

  • generate_plot_securely
  • sanitize_csv_securely

Tools (4)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

  • execute_code_securely

    Executes Python code in remote sandbox, automatically resolving x402 payments.

  • generate_plot_securely

    Generate line or scatter charts from x and y data in an isolated Azure sandbox, enabling secure remote data visualization for AI agents. ARCHITECTURE NOTE: The remote sandbox generates the plot, but the local MCP client intercepts the base64 payload and securely writes it directly to the user's local home directory as a PNG. Use this tool to visually represent numerical trends. Keep data arrays under 10,000 points to prevent sandbox timeouts.

  • optimize_ga_securely

    Executes a Genetic Algorithm in a secure remote Azure sandbox to minimize Mean Absolute Percentage Error (MAPE) against ground-truth target values. Ideal for driving down error metrics in complex time-series predictions, such as electrical load forecasting. Usage Guidelines: - `actuals` array size must not exceed 5,000 data points to prevent sandbox execution timeouts. - `generations` should be kept under 1,000 iterations for optimal performance vs. compute cost. - `model_type` is strictly limited to 'polynomial', 'exponential', or 'logistic'.

  • sanitize_csv_securely

    Sanitize raw CSV data by cleaning and normalizing it within a secure remote Azure sandbox environment. Use this tool to prepare messy tabular data for downstream processing. It automatically strips leading/trailing whitespace, standardizes delimiters to commas, and resolves malformed rows. Usage Guidelines: - `csv_content` must be a plain-text string representation of a CSV. - Limit payload size to a maximum of 50,000 rows to prevent sandbox memory limits and payload timeouts. - Do not pass binary files or Excel (.xlsx) formats; strictly text-based CSV data.

Signals

Stars
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Forks
0
Open issues
0
Weekly downloads
77
Last commit
Sep 4, 2026 (1 month ago)
Last release
Aug 15, 2026 (2 months ago)
Last package publish
Aug 15, 2026 (2 months ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name
io.github.codelad1304/agentic-compute
Version
1.1.4
Auth
No auth
Packages
pypi agentic-compute-mcp-codelad1304
License
MIT
Repository
github.com/codelad1304/agentic-compute-mcp
First seen
Oct 24, 2026
Last checked
Oct 25, 2026
Source
Official MCP Registrygithub-graphql
Cite this page

Agentic Compute MCP server. Agent Mart. https://agentmart.dev/connectors/codelad1304-agentic-compute (last updated 2026-10-25)