Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Runtime governance enforcement for AI agents. Zero token overhead.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"aegis": {
"command": "npx",
"args": [
"-y",
"aegis-mcp-server"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add aegis -- npx -y aegis-mcp-serverPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"aegis": {
"command": "npx",
"args": [
"-y",
"aegis-mcp-server"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"aegis": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"aegis-mcp-server"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub cleburn, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.1.15) llegó al registro hace 8 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, SwarmMemo
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Talk with agent swarms: rooms, private messages and paid tasks for AI agents
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
MCP enforcement layer for the [Aegis](https://github.com/cleburn/aegis-spec) agent governance specification.
Policy at the root. Enforcement at runtime. Accountability on every action.
aegis-mcp-server is an MCP server that validates every agent action against your .agentpolicy/ files before it happens. Path permissions, content scanning, role boundaries, quality gates — all enforced at runtime with zero token overhead to the agent.
The agent never loads your governance files. The MCP server reads them into its own process memory and validates silently. The agent calls governed tools and gets back either a success or a blocked response with the specific reason.
# Install globally
npm install -g aegis-mcp-server
If you generated your policy with aegis-cli, the .mcp.json connection config is already in your project root. Just install the MCP and open your agent — it connects automatically.
When starting a new agent session in a governed project, use this as your first prompt:
Call aegis_policy_summary now. This is your governance contract — it defines your
role, your boundaries, and which tools to use. Do not take any action until you have called this tool and received confirmation from the user to proceed.
For initial builds, the Aegis CLI generates a custom handoff prompt tailored to your project — use that instead.
The MCP starts without a pre-assigned role. When the agent calls aegis_policy_summary, it receives the list of available roles — including the built-in construction role and all specialist roles from .agentpolicy/roles/. The agent presents them to the user, the user picks, and the agent calls aegis_select_role to lock in. All enforcement uses the selected role for the rest of the session.
This is the default behavior — no configuration needed beyond the .mcp.json that aegis init creates automatically.
The construction role is always available for initial builds and major restructuring. When selected:
.agentpolicy/ files serve as the blueprint — the agent reads constitution, governance, and role files to understand the project's architecture, conventions, and quality standardsstate/overrides.jsonl with a timestamp and human_confirmed: trueaegis_complete_task to run quality gates and close construction mode — the closing timestamp is logged alongside the opening entryConstruction mode is not a bypass — the agent still follows the governance files as its blueprint. It's a speed optimization for greenfield builds where enforcing write restrictions on every file would be counterproductive.
If you know which role to assign at startup:
{
"mcpServers": { "aegis": { "command": "aegis-mcp", "args": ["--project", ".", "--role", "backend"] } } }
The MCP locks to that role immediately. aegis_policy_summary returns the role's boundaries directly, skipping role selection.
| Tool | What it does | Token cost |
|---|---|---|
aegis_policy_summary | Role boundaries and governance summary (or available roles in universal mode) | ~200 tokens |
aegis_select_role | Select a role (including construction) in universal mode | Tiny |
aegis_check_permissions | Pre-check if an operation is allowed | Tiny |
aegis_write_file | Governed write with path + content validation | Same as a normal write |
aegis_read_file | Governed read with path validation | Same as a normal read |
aegis_delete_file | Governed delete with path validation | Tiny |
aegis_execute | Governed command execution | Command output only |
aegis_complete_task | Run quality gates and close construction mode if active | Gate results only |
aegis_request_override | Execute a blocked action after human confirmation | Tiny |
Traditional approach: load governance files into the agent's context window. Token cost scales with policy complexity.
Aegis MCP approach: the server loads policy into its own process memory. The agent calls tools and gets structured results. A project with 200 lines of governance has the same token cost as one with 20 lines. The complexity is absorbed by the server, not the agent.
writable, read_only, forbidden path listspre_commit flags mapped to build_commands and executedoverrides.jsonlWhen an action is blocked and the governance override behavior is warn_confirm_and_log:
override_token and the specific policy violatedaegis_request_override with the token and the user's rationalehuman_confirmed: trueImmutable policies (e.g., HIPAA compliance, ITAR data sovereignty, audit completeness) return override_available: false and cannot be overridden. The user must modify the governance through aegis init.
The Aegis MCP does not override the agent's native directives. It introduces itself through tool descriptions, explains why governance is active, and asks the agent to seek user permission to route write operations through Aegis tools. The user's authority is the enforcement mechanism.
Native tools for reading, searching, and exploring the codebase work fine without governance gating. Only write, delete, and execute operations benefit from routing through Aegis.
Agent ──→ aegis-mcp-server ──→ File System
│ ├── Loads .agentpolicy/ into process memory (once) ├── Watches for policy changes (auto-reload) ├── Validates every tool call against policy ├── Returns success or blocked with override option └── Logs all enforcement decisions to overrides.jsonl
Three artifacts, one governance framework:
MIT