OpenSwarm is an open-source multi-agent system in which specialized agents coordinate to produce deliverables such as presentations, research reports, data visualizations, documents, images, and videos. It is intended for people who want to request complex creative or analytical work from a terminal, with connections to external services. Its catalogue entries define agents, commands, rules, and instructions for those workflows.
Borrowing it
Nothing to install: this file belongs to VRSEN/OpenSwarm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/VRSEN/OpenSwarm/main/.cursor/commands/mcp-code-exec.mdgit clone --depth 1 https://github.com/VRSEN/OpenSwarmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/vrsen/openswarm/mcp-code-exec)<a href="https://agentmods.dev/commands/vrsen/openswarm/mcp-code-exec"><img src="https://agentmods.dev/badge/commands/vrsen/openswarm/mcp-code-exec/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/vrsen/openswarm/mcp-code-exec"><img src="https://agentmods.dev/badge/commands/vrsen/openswarm/mcp-code-exec.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.02872 |
| Opus 5.5 | $0.00000 | $0.01149 |
| Sonnet 5.5 | $0.00000 | $0.00574 |
| Haiku 4.5 | $0.00000 | $0.00287 |
Grade A, and why
mcp-code-exec scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MCP Code Execution Pattern - Implementation Guide
Your task is to convert an MCP server into the Code Execution Pattern described in Anthropic's blog post. This pattern enables progressive disclosure of tools, reducing token usage by up to 98% compared to loading all tools upfront.
Why This Pattern?
Problem with Traditional MCP:
- Loading all tools upfront consumes 150,000+ tokens
- Intermediate tool results flow through model context repeatedly
- Agents slow down with many connected tools
Solution - Code Execution Pattern:
- Tools organized as importable Python modules (filesystem-based)
- Agents load only what they need (progressive disclosure)
- Data processing happens in code before returning to model
- Result: 98.7% token reduction (150K → 2K tokens)
Step-by-Step Implementation
Step 0: Create a TO-DO List.
Before starting, check if the user has provided the necessary auth credentials for the MCP servers. If not, ask the user to provide them before starting this task.
After credentials are provided, create a to-do list for yourself with all the steps you need to complete below.
Step 1: Create MCP Server Files
Create a dedicated folder for the MCP server in ./agent_name/mcp_servers/ directory.
Example:
./agent_name/mcp_servers/[server_name]/
├── notion.py # Server singleton & connection management (with discovery in __main__)
├── salesforce.py
├── github.py
├── youtube.py
└── __init__.py # Package exports
Step 2: Create the Server Module
The server.py module manages the MCP server connection as a singleton. It is converted into tools using ToolFactory.from_mcp method, available in agency_swarm specifically for this pattern. This method converts the MCP server into a list of tools.
Include tool discovery in the __main__ block to list available tools.
Note: This pattern works with any MCP server type, except HostedMCPTool. The template below shows MCPServerStdio (most common), but you can use:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 334 lines · 0 tokens per session scan A 7f824ab799bd
mcp-code-exec is a command published in the GitHub repository VRSEN/OpenSwarm (2,873 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,872 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.
speckit.bug.test
Validate that a previously fixed bug is resolved and record the verification report.