Code Execution MCP

by marc-shade

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Python

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Token-Efficient MCP Server
Code Execution Developer Tools Autonomous Agents
ingpoc
F
license
A
quality
D
maintenance
Provides sandboxed code execution and data processing for CSVs and logs to achieve over 95% token savings. It enables secure multi-language execution and progressive tool disclosure to optimize LLM context usage.
Updated 9 months ago (2025-12-30 18:20 UTC)
7
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MCP Code Execution Server
Code Execution Developer Tools
ozgureyilmaz
F
license
Not graded
quality
Not graded
maintenance
Enables efficient code execution in a secure sandbox with 98.7% token reduction by allowing agents to write JavaScript/TypeScript code to interact with tools, process data, and maintain state instead of loading all tool definitions into context.
Updated a year ago (2025-11-07 22:59 UTC)
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MCPWorksofficial
Code Execution Autonomous Agents Agent Orchestration
MCPWorks-Technologies-Inc
A
license
Not graded
quality
C
maintenance
Cuts AI token costs by running user code in a secure sandbox, so data never enters the context window, enabling efficient data processing with MCP protocol.
Updated 4 months ago (2026-06-03 19:29 UTC)
10
Business Source 1.1
MCP Code Mode
Code Execution Autonomous Agents Developer Tools
hoangnb24
F
license
Not graded
quality
D
maintenance
Enables AI agents to write and execute Python code in an isolated sandbox that can orchestrate multiple MCP tool calls, reducing context window bloat and improving efficiency for complex workflows.
Updated 10 months ago (2025-11-24 07:13 UTC)
23
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mcp-graphify-autotrigger
Knowledge & Memory Shell Access Autonomous Agents
ChharithOeun
A
license
Not graded
quality
D
maintenance
Provides efficient knowledge-graph queries and unrestricted shell delegation for AI agents, reducing token usage by 80-150x and bypassing app tier restrictions.
Updated 5 months ago (2026-04-27 19:16 UTC)
1
MIT
context-architect
Agent Orchestration Autonomous Agents Observability
hanifalkauni
A
license
Not graded
quality
B
maintenance
Enables autonomous context window management, prompt caching, token optimization, and real-time observability for AI coding agents across multiple IDEs, reducing token costs and improving performance.
Updated 18 days ago (2026-09-09 12:30 UTC)
MIT