Servidor de protocolo de contexto de modelo para descargar el trabajo de codificación de IA a Aider, mejorando la eficiencia y la flexibilidad del desarrollo.
Este servidor permite a Claude Code delegar las tareas de codificación de IA a Aider, el mejor asistente de codificación de IA de código abierto. Al delegar ciertas tareas de codificación a Aider, podemos reducir costos, controlar nuestro modelo de codificación y operar Claude Code de forma más orquestada para revisar y corregir el código.
Related MCP server: AiderMCP
Clonar el repositorio:
git clone https://github.com/disler/aider-mcp-server.gitInstalar dependencias:
uv syncCrea tu archivo de entorno:
cp .env.sample .envConfigure sus claves API en el archivo .env (o use la sección "env" de mcpServers) para tener la clave API necesaria para el modelo que desea usar en aider:
GEMINI_API_KEY=your_gemini_api_key_here
OPENAI_API_KEY=your_openai_api_key_here
ANTHROPIC_API_KEY=your_anthropic_api_key_here
...see .env.sample for moreCopie y complete el .mcp.json en la raíz de su proyecto y actualice --directory para apuntar al directorio raíz de este proyecto y --current-working-dir para apuntar a la raíz de su proyecto.
{
"mcpServers": {
"aider-mcp-server": {
"type": "stdio",
"command": "uv",
"args": [
"--directory",
"<path to this project>",
"run",
"aider-mcp-server",
"--editor-model",
"gpt-4o",
"--current-working-dir",
"<path to your project>"
],
"env": {
"GEMINI_API_KEY": "<your gemini api key>",
"OPENAI_API_KEY": "<your openai api key>",
"ANTHROPIC_API_KEY": "<your anthropic api key>",
...see .env.sample for more
}
}
}
}Pruebas realizadas con gemini-2.5-pro-exp-03-25
Para ejecutar todas las pruebas:
uv run pytestPara ejecutar pruebas específicas:
# Test listing models
uv run pytest src/aider_mcp_server/tests/atoms/tools/test_aider_list_models.py
# Test AI coding
uv run pytest src/aider_mcp_server/tests/atoms/tools/test_aider_ai_code.pyNota: Las pruebas de codificación de IA requieren una clave API válida para el modelo Gemini. Asegúrese de configurarla en su archivo .env antes de ejecutar las pruebas.
gemini-2.5-pro-exp-03-25claude mcp add aider-mcp-server -s local \
-- \
uv --directory "<path to the aider mcp server project>" \
run aider-mcp-server \
--editor-model "gemini/gemini-2.5-pro-exp-03-25" \
--current-working-dir "<path to your project>"gemini-2.5-pro-preview-03-25claude mcp add aider-mcp-server -s local \
-- \
uv --directory "<path to the aider mcp server project>" \
run aider-mcp-server \
--editor-model "gemini/gemini-2.5-pro-preview-03-25" \
--current-working-dir "<path to your project>"quasar-alphaclaude mcp add aider-mcp-server -s local \
-- \
uv --directory "<path to the aider mcp server project>" \
run aider-mcp-server \
--editor-model "openrouter/openrouter/quasar-alpha" \
--current-working-dir "<path to your project>"llama4-maverick-instruct-basicclaude mcp add aider-mcp-server -s local \
-- \
uv --directory "<path to the aider mcp server project>" \
run aider-mcp-server \
--editor-model "fireworks_ai/accounts/fireworks/models/llama4-maverick-instruct-basic" \
--current-working-dir "<path to your project>"Este servidor MCP proporciona las siguientes funcionalidades:
Delegar tareas de codificación de IA a Aider :
Toma un aviso y rutas de archivos
Utiliza Aider para implementar los cambios solicitados
Devuelve el éxito o el fracaso
Lista de modelos disponibles :
Proporciona una lista de modelos que coinciden con una subcadena
Útil para descubrir modelos compatibles
Este servidor MCP expone las siguientes herramientas:
aider_ai_codeEsta herramienta le permite ejecutar Aider para realizar tareas de codificación de IA según una solicitud proporcionada y archivos específicos.
Parámetros:
ai_coding_prompt (cadena, obligatoria): la instrucción en lenguaje natural para la tarea de codificación de IA.
relative_editable_files (lista de cadenas, obligatoria): Una lista de rutas de archivo (relativas al current_working_dir ) que Aider puede modificar. Si un archivo no existe, se creará.
relative_readonly_files (lista de cadenas, opcional): Una lista de rutas de archivo (relativas al current_working_dir ) que Aider puede leer para contexto, pero no modificar. El valor predeterminado es una lista vacía [] .
model (cadena, opcional): El modelo de IA principal que Aider debe usar para generar código. El valor predeterminado es "gemini/gemini-2.5-pro-exp-03-25" . Puede usar la herramienta list_models para encontrar otros modelos disponibles.
editor_model (cadena, opcional): El modelo de IA que Aider debe usar para editar/refinar el código, especialmente al usar el modo arquitecto. Si no se proporciona, se podría usar el model principal según la lógica interna de Aider. El valor predeterminado es None .
Ejemplo de uso (dentro de una solicitud MCP):
Indicación de Claude Code:
Use the Aider AI Code tool to: Refactor the calculate_sum function in calculator.py to handle potential TypeError exceptions.Resultado:
{
"name": "aider_ai_code",
"parameters": {
"ai_coding_prompt": "Refactor the calculate_sum function in calculator.py to handle potential TypeError exceptions.",
"relative_editable_files": ["src/calculator.py"],
"relative_readonly_files": ["docs/requirements.txt"],
"model": "openai/gpt-4o"
}
}Devoluciones:
Un diccionario simple: {éxito, diferencia}
success : booleano - Si la operación fue exitosa.
diff : string - La diferencia de los cambios realizados en el archivo.
list_modelsEsta herramienta enumera los modelos de IA disponibles compatibles con Aider que coinciden con una subcadena determinada.
Parámetros:
substring (cadena, obligatoria): la subcadena que se buscará dentro de los nombres de los modelos disponibles.
Ejemplo de uso (dentro de una solicitud MCP):
Indicación de Claude Code:
Use the Aider List Models tool to: List models that contain the substring "gemini".Resultado:
{
"name": "list_models",
"parameters": {
"substring": "gemini"
}
}Devoluciones:
Una lista de cadenas de nombres de modelos que coinciden con la subcadena proporcionada. Ejemplo: ["gemini/gemini-1.5-flash", "gemini/gemini-1.5-pro", "gemini/gemini-pro"]
El servidor está estructurado de la siguiente manera:
Capa de servidor : maneja la comunicación del protocolo MCP
Capa de átomos : Componentes funcionales individuales y puros
Herramientas : Capacidades específicas (codificación de IA, modelos de listado)
Utilidades : Constantes y funciones auxiliares
Tipos de datos : definiciones de tipos utilizando Pydantic
Todos los componentes se prueban exhaustivamente para garantizar su confiabilidad.
El proyecto está organizado en los siguientes directorios y archivos principales:
.
├── ai_docs # Documentation related to AI models and examples
│ ├── just-prompt-example-mcp-server.xml
│ └── programmable-aider-documentation.md
├── pyproject.toml # Project metadata and dependencies
├── README.md # This file
├── specs # Specification documents
│ └── init-aider-mcp-exp.md
├── src # Source code directory
│ └── aider_mcp_server # Main package for the server
│ ├── __init__.py # Package initializer
│ ├── __main__.py # Main entry point for the server executable
│ ├── atoms # Core, reusable components (pure functions)
│ │ ├── __init__.py
│ │ ├── data_types.py # Pydantic models for data structures
│ │ ├── logging.py # Custom logging setup
│ │ ├── tools # Individual tool implementations
│ │ │ ├── __init__.py
│ │ │ ├── aider_ai_code.py # Logic for the aider_ai_code tool
│ │ │ └── aider_list_models.py # Logic for the list_models tool
│ │ └── utils.py # Utility functions and constants (like default models)
│ ├── server.py # MCP server logic, tool registration, request handling
│ └── tests # Unit and integration tests
│ ├── __init__.py
│ └── atoms # Tests for the atoms layer
│ ├── __init__.py
│ ├── test_logging.py # Tests for logging
│ └── tools # Tests for the tools
│ ├── __init__.py
│ ├── test_aider_ai_code.py # Tests for AI coding tool
│ └── test_aider_list_models.py # Tests for model listing toolsrc/aider_mcp_server : contiene el código de la aplicación principal.
atoms : Contienen los componentes fundamentales. Están diseñados para ser funciones puras o clases simples con mínimas dependencias.
tools : cada archivo aquí implementa la lógica central para una herramienta MCP específica ( aider_ai_code , list_models ).
utils.py : contiene constantes compartidas como nombres de modelos predeterminados.
data_types.py : define modelos de Pydantic para estructuras de solicitud/respuesta, lo que garantiza la validación de datos.
logging.py : configura un formato de registro consistente para la consola y la salida de archivo.
server.py : Orquesta el servidor MCP. Inicializa el servidor, registra las herramientas definidas en el directorio atoms/tools , gestiona las solicitudes entrantes, las enruta a la lógica de herramientas adecuada y envía respuestas según el protocolo MCP.
__main__.py : proporciona el punto de entrada de la interfaz de línea de comandos ( aider-mcp-server ), analiza argumentos como --editor-model e inicia el servidor definido en server.py .
tests : contiene pruebas que reflejan la estructura del directorio src , lo que garantiza que cada componente (especialmente los átomos) funcione como se espera.
Run Aider to perform AI coding tasks based on the provided prompt and files
| Name | Required | Description | Default |
|---|---|---|---|
| ai_coding_prompt | Yes | The prompt for the AI to execute | |
| relative_editable_files | Yes | LIST of relative paths to files that can be edited | |
| relative_readonly_files | No | LIST of relative paths to files that can be read but not edited, add files that are not editable but useful for context | |
| model | No | The primary AI model Aider should use for generating code, leave blank unless model is specified in the request |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Run Aider' implies execution and potential code modification, the description doesn't disclose critical behavioral traits: whether this tool makes permanent changes to files, what permissions are required, error handling, rate limits, or what happens when execution completes. For a tool that appears to modify code files, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place with no redundancy or unnecessary elaboration. It's appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this appears to be a code execution/modification tool with no annotations, no output schema, and 4 parameters, the description is insufficiently complete. It doesn't explain what happens after execution, what the return values might be, error conditions, or safety considerations for a tool that presumably edits files. The single sentence description leaves too many important questions unanswered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already documents all 4 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters, provide examples, or clarify edge cases. The baseline of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Run Aider to perform AI coding tasks based on the provided prompt and files'. It specifies the verb ('Run Aider') and resource ('AI coding tasks'), but doesn't differentiate from its only sibling 'list_models', which is a different type of tool. The purpose is clear but lacks sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, appropriate contexts, or exclusions. With a sibling tool 'list_models' available, there's no indication of when to choose one over the other or if they're complementary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List available models that match the provided substring
| Name | Required | Description | Default |
|---|---|---|---|
| substring | No | Substring to match against available models |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions substring matching but fails to describe key behaviors like whether the list is paginated, if it includes metadata, what happens when no substring is provided, or any rate limits. This leaves significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without any unnecessary words. It is appropriately sized and front-loaded, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of model names, full details), behavioral traits like error handling, or usage context relative to the sibling tool. For a tool with no structured support, this leaves too many unknowns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'substring' fully documented in the schema. The description adds minimal value by implying substring matching but doesn't provide additional semantics beyond what the schema already states, such as case sensitivity or matching patterns.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('List') and resource ('available models'), and includes the filtering mechanism ('match the provided substring'). It distinguishes itself from a generic list operation by specifying substring matching, though it doesn't explicitly differentiate from the sibling tool 'aider_ai_code'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as the sibling 'aider_ai_code' or other potential model-related tools. It lacks context about prerequisites, exclusions, or specific scenarios where substring matching is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0aider_ai_codelist_modelsThe two tools have completely distinct purposes with no overlap: aider_ai_code performs AI coding tasks, while list_models provides information about available models. An agent can easily differentiate between them based on their clear, separate functions.
The naming shows mixed conventions: aider_ai_code uses a descriptive compound name with underscores, while list_models follows a more standard verb_noun pattern. They are both readable but lack a unified naming style, indicating some inconsistency in the tool set.
With only 2 tools, the server feels thin for an AI coding assistant domain. While aider_ai_code is a core tool, the lack of additional tools for tasks like file management, code review, or configuration limits the server's scope and utility, making the count too low for effective coverage.
The tool surface is severely incomplete for an AI coding assistant. It includes a primary coding tool and a model listing, but lacks essential operations such as file manipulation, code analysis, or session management. This creates significant gaps that will hinder agent workflows and lead to dead ends.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
No-data MCP handoff for local Claude Code to Codex harness moves. $49 lifetime.
Use AI models for chat, image, and video generation from Claude Code and other MCP hosts.
One identity across Claude Code, Codex, Cursor, Gemini, Windsurf: shared inbox and handoffs.
The OpenRouter MCP server plugs OpenRouter into the AI tools you already use. Once connected, your assistant can pull live OpenRouter data (models, prices, your credits, rankings, and docs) and send quick test messages, all without leaving your editor.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/disler/aider-mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
