Skip to main content
Glama
codelad1304

agentic-compute-mcp

by codelad1304
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
EVM_PRIVATE_KEYYesYour 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

CapabilityDetails
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

NameDescription
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:

  • Code must be standard Python 3.

  • Script execution is subject to a 30-second timeout limit; avoid infinite loops or blocking operations.

  • Ensure all required imports are included within the snippet.

  • x402 micropayments (USDC on Base) are automatically verified per execution call.

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:

  • 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.

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:

  • 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'.

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

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4.4/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: generic code execution, CSV sanitization, genetic algorithm optimization, and plotting. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow the consistent pattern of verb_object_securely (e.g., execute_code_securely, generate_plot_securely). This makes the set predictable and easy to navigate.

Tool Count5/5

With 4 tools, the set is well-scoped and each tool earns its place. The count is within the ideal 3-15 range and matches the server's focus on secure remote compute tasks.

Completeness4/5

The code execution tool provides a general catch-all capability, and the specialized tools cover data cleaning, optimization, and visualization. Minor gaps exist (e.g., no generic data transformation tool), but the core workflows are covered without dead ends.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources