agentic-compute-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| EVM_PRIVATE_KEY | Yes | Your EVM private key for processing agentic payments |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| execute_code_securelyA | Executes Python code in a remote, isolated Azure sandbox environment with automatic x402 payment handling. Use this tool to safely evaluate Python algorithms, process data structures, perform math calculations, or run custom scripts. Usage Guidelines:
|
| sanitize_csv_securelyA | 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:
|
| optimize_ga_securelyA | 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:
|
| generate_plot_securelyA | 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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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