jigyasudham/veto
by Various
Veto MCP — gives every major AI CLI (Claude Code, Codex, Gemini, Cursor, Windsurf) a council of 49 specialist agents + 93 tools. Deterministic-first, self-learning, no API keys.
MCP
jigyasudham/veto
Added 4 July 2026
Overview
Veto MCP is a TypeScript tool that provides a council of 49 specialist agents and 93 tools to major AI CLIs including Claude Code, Codex, Gemini, Cursor, and Windsurf. It uses a deterministic-first, self-learning approach and requires no API keys.
Best for
Best for
Developers who want to add a large, deterministic agent council to their AI CLI without managing API keys
Use cases
- Augmenting AI CLI workflows with specialized agent councils for complex tasks
- Running deterministic, self-learning automation without external API dependencies
- Integrating multi-agent tooling into existing AI development environments
Notes
Veto MCP is a TypeScript tool that provides a council of 49 specialist agents and 93 tools to major AI CLIs including Claude Code, Codex, Gemini, Cursor, and Windsurf. It uses a deterministic-first, self-learning approach and requires no API keys.
0 stars on GitHub. Last updated 2026-07-04. Licensed MIT.
Use cases
- Augmenting AI CLI workflows with specialized agent councils for complex tasks
- Running deterministic, self-learning automation without external API dependencies
- Integrating multi-agent tooling into existing AI development environments
Pros
- No API keys required, reducing setup friction
- Large agent and tool library (49 agents, 93 tools) for diverse tasks
- Deterministic-first design improves reliability over probabilistic models
Cons
- Zero GitHub stars suggests limited community validation or adoption
- Vendor listed as ‘Various’ may indicate unclear ownership or support
- Self-learning approach may introduce unpredictable behavior in some scenarios
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- No API keys required, reducing setup friction
- Large agent and tool library (49 agents, 93 tools) for diverse tasks
- Deterministic-first design improves reliability over probabilistic models
Cons
- Zero GitHub stars suggests limited community validation or adoption
- Vendor listed as 'Various' may indicate unclear ownership or support
- Self-learning approach may introduce unpredictable behavior in some scenarios
Pairs with
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