mcp-a2a-bridge
mcp-a2a-bridge
Un servidor MCP que permite a los agentes de codificación — GitHub Copilot CLI, Claude Code, Codex y Hermes — llamar a agentes A2A remotos. También incluye un panel de actividad de solo lectura opcional.
Repositorio: https://github.com/TonPC64/mcp-a2a-bridge
Licenciado bajo la Licencia MIT.
Para instrucciones de agentes de codificación, comienza con AGENTS.md y luego usa skills/setup/SKILL.md para la instalación o skills/contribute/SKILL.md para cambios y PRs.
Presentación de la interfaz del panel
El panel opcional usa una interfaz futurista Liquid Glass con tarjetas de agentes compactas, actividad de tareas en vivo, resúmenes de tareas a dos columnas, indicadores de estado y diseños responsivos. Esta captura de pantalla usa el modo de datos simulados del frontend, por lo que es seguro reproducirla sin conectarse a agentes reales ni exponer registros.

Para ejecutar la misma interfaz simulada localmente:
cd /absolute/path/to/mcp-a2a-bridge/dashboard
npm ci
npm run dev:mock -- --host 127.0.0.1El modo simulado es solo para desarrollo de UI y capturas de pantalla; las compilaciones normales usan la API en vivo del panel y SSE.
Related MCP server: agentkit-mesh
Primeros pasos
Elige tu configuración
Ambos perfiles necesitan Python 3.11 o posterior y uv. El frontend opcional del panel requiere Node.js 24.15 o posterior; CI usa la última versión de Node.js 24.
Solo MCP
Instala y ejecuta solo el puente MCP. Esto no requiere Node.js/npm ni una compilación del frontend del panel.
git clone https://github.com/TonPC64/mcp-a2a-bridge.git mcp-a2a-bridge
cd mcp-a2a-bridge
uv sync --lockedMCP + panel
Instala el runtime web opcional del panel y luego compila sus assets de frontend incluidos. Node.js y npm solo se necesitan para esta compilación (o para el desarrollo del frontend), no para ejecutar un panel ya compilado.
git clone https://github.com/TonPC64/mcp-a2a-bridge.git mcp-a2a-bridge
cd mcp-a2a-bridge
uv sync --extra dashboard --locked
npm --prefix dashboard ci
npm --prefix dashboard run buildPara desarrollo y pruebas, usa uv sync --all-extras --locked; incluye los paquetes de Python necesarios para la suite de pruebas del panel. Los comandos siguientes usan /absolute/path/to/mcp-a2a-bridge. Reemplázalo con la ruta absoluta de tu clon; no copies la ruta de otro usuario.
Configura los agentes A2A
Crea .a2a-agents.json en la raíz del repositorio:
{
"agents": {
"example-agent": {
"url": "https://agent.example.invalid",
"headers": {
"Authorization": "Bearer REPLACE_WITH_YOUR_TOKEN"
}
}
}
}Usa la URL base de un agente A2A; el puente solicita su /.well-known/agent-card.json automáticamente. Elimina Authorization (o usa un objeto headers vacío) cuando el agente no requiera autenticación. El registro puede contener secretos, así que no lo confirmes y restringe sus permisos de archivo cuando corresponda.
La ubicación del registro se resuelve en este orden:
A2A_BRIDGE_CONFIG.a2a-agents.jsonen el directorio actual del proceso del puente~/.config/a2a-bridge/agents.json
Para un host que inicie el comando MCP fuera del repositorio, establece A2A_BRIDGE_CONFIG en una ruta absoluta dentro del entorno MCP de ese host. También puedes añadir un agente verificado en tiempo de ejecución con a2a_add_agent; por defecto escribe en el archivo de registro resuelto.
Registra el servidor MCP
Cada host debe iniciar el mismo comando de la copia local:
uv run --directory /absolute/path/to/mcp-a2a-bridge mcp-a2a-bridgeEsto usa la instalación editable creada anteriormente. Configura el host con el siguiente marcador de posición de ruta portátil.
GitHub Copilot CLI — añade este servidor a ~/.copilot/mcp-config.json:
{
"mcpServers": {
"a2a": {
"type": "stdio",
"command": "uv",
"args": [
"run",
"--directory",
"/absolute/path/to/mcp-a2a-bridge",
"mcp-a2a-bridge"
]
}
}
}Claude Code:
claude mcp add a2a -- uv run --directory /absolute/path/to/mcp-a2a-bridge mcp-a2a-bridgeCodex — añade esto a ~/.codex/config.toml:
[mcp_servers.a2a]
command = "uv"
args = ["run", "--directory", "/absolute/path/to/mcp-a2a-bridge", "mcp-a2a-bridge"]Hermes: usa su UI o comando de registro de servidores MCP, elige un servidor stdio y proporciona el comando uv con estos argumentos:
run --directory /absolute/path/to/mcp-a2a-bridge mcp-a2a-bridgeReinicia el host después del registro y luego llama a a2a_list_agents para confirmar que el registro está disponible y que los agentes son accesibles.
Herramientas
Herramienta | Propósito |
| Muestra los agentes con sus habilidades y accesibilidad |
| Envía un mensaje; continúa una tarea con |
| Consulta el estado de una tarea iniciada anteriormente |
| Cancela una tarea en ejecución |
| Registra un agente por URL |
a2a_send_message espera hasta timeout_s (60 segundos por defecto). Si una tarea sigue en ejecución, devuelve done: false con un task_id; consúltala con a2a_get_task. Si un agente devuelve state: "input_required", llama a a2a_send_message de nuevo con el mismo task_id.
Panel
El panel independiente opcional muestra los agentes configurados, su estado/habilidades y la actividad de tareas en vivo de los procesos del puente en la misma máquina. Es de solo lectura: nunca envía mensajes a los agentes.
Instala el perfil dashboard y compila los assets antes de iniciarlo:
cd /absolute/path/to/mcp-a2a-bridge
uv sync --extra dashboard --locked
npm --prefix dashboard ci
npm --prefix dashboard run build
uv run mcp-a2a-bridge-dashboardPor defecto escucha solo en 127.0.0.1:9100. Abre http://127.0.0.1:9100 en la misma máquina.
Datos simulados del frontend
Para ejecutar la UI del panel sin el servicio Python ni SSE, usa el modo simulado opcional. Incluye agentes representativos y actividad de tareas para capturas de pantalla:
cd /absolute/path/to/mcp-a2a-bridge/dashboard
npm ci
npm run dev:mock -- --host 0.0.0.0Abre la URL de Vite en la misma red. VITE_USE_MOCK_DATA=true solo lo usa la compilación/servidor de desarrollo del frontend; npm run dev normal y las compilaciones de producción continúan usando la API en vivo del panel y SSE.
Habilita el reporte para cada proceso del puente cuya actividad deba aparecer en el panel. Añade esta variable de entorno a la configuración del servidor MCP (o al lanzarlo manualmente):
A2A_BRIDGE_DASHBOARD=1 uv run --directory /absolute/path/to/mcp-a2a-bridge mcp-a2a-bridgeLos procesos del puente sin esa marca mantienen la actividad en memoria y no aparecen en el panel compartido. El panel puede iniciarse antes o después de ellos.
Variable de entorno | Valor predeterminado | Propósito |
| — | Ruta absoluta al registro JSON de agentes |
| sin establecer (desactivado) | Establécelo en |
|
| Base de datos SQLite de actividad compartida |
|
| Etiqueta de origen para la actividad escrita por procesos compatibles |
|
| Dirección de enlace del panel |
|
| Puerto del panel |
| sin establecer (desactivado) | Token portador opcional que protege el panel y la API |
| sin establecer | Anulación de solo lectura para |
| sin establecer | Se usa para encontrar |
Despliegue en LAN
El panel puede exponerse deliberadamente en una LAN de confianza sin token estableciendo un enlace explícito que no sea de bucle local. Este modo no tiene autenticación, así que úsalo solo en una red privada y de confianza; nunca lo expongas a internet público.
Para acceso LAN protegido, establece un token fuerte y coloca el servicio detrás de un proxy inverso que termine TLS con sus propios controles de acceso a la red:
export A2A_BRIDGE_DASHBOARD_HOST=0.0.0.0
export A2A_BRIDGE_DASHBOARD_TOKEN="$(openssl rand -hex 32)"
uv run mcp-a2a-bridge-dashboardCuando se establece un token, el HTML del panel, los assets estáticos, las rutas de API y las rutas SSE lo requieren. Los usuarios de navegador lo introducen una vez en /login; la cookie de sesión HttpOnly y same-site resultante permite que EventSource se reconecte normalmente. Los clientes de API pueden enviar en su lugar Authorization: Bearer <token>. El servicio nunca registra el token. Un token autentica el acceso pero no cifra el tráfico, así que no uses el endpoint HTTP directo de LAN a través de redes no confiables; usa HTTPS en el proxy inverso.
Para macOS, la plantilla launchd/com.example.a2a-bridge-dashboard.plist puede ejecutar el panel al iniciar sesión. Reemplaza sus marcadores de posición __REPO__ y __HOME__ antes de instalarla como LaunchAgent.
Desarrollo del frontend del panel
Node.js 24.15+ y npm solo se requieren para trabajar en el frontend o recompilar sus assets incluidos:
cd /absolute/path/to/mcp-a2a-bridge/dashboard
npm ci
npm run devVite imprime la URL de desarrollo (normalmente http://localhost:5173). Compila los assets que consume el panel de Python con npm run build.
Servidor A2A de Copilot
El servidor incluido requiere una CLI copilot autenticada:
copilot-a2a-agent --port 9002 --cwd /path/to/default/repositorySe enlaza a 127.0.0.1 porque ejecuta Copilot con --allow-all-tools. Su actividad entrante puede usar el mismo almacén SQLite compartido opcional y se muestra con campos de origen y destino en el panel.
El panel también puede leer el archivo de auditoría nativo de Hermes sin modificar Hermes. Comprueba A2A_BRIDGE_HERMES_AUDIT, luego $HERMES_HOME/a2a_audit.jsonl y luego ~/.hermes/a2a_audit.jsonl.
Desarrollo y pruebas
Ejecuta la suite de pruebas de Python desde la raíz del repositorio:
uv sync --all-extras --locked
uv run pytest -vEjecuta las pruebas del panel o haz una compilación de producción del panel desde dashboard/:
npm test
npm run buildCI ejecuta la suite de Python en Python 3.11, valida uv.lock y ejecuta la instalación, las pruebas y la compilación de producción del panel en la última versión de Node.js 24.
tests/test_integration.py ejecuta un agente A2A en el mismo proceso y ejercita el puente de extremo a extremo.
Available Tools
5 toolsa2a_add_agentA
Register a new A2A agent by its base URL.
The agent card is fetched first, so an unreachable URL is rejected instead of being saved. With persist=true the agent is written to the shared registry file and stays available in future sessions.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| name | Yes | ||
| headers | No | ||
| persist | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and it does meaningful work: it discloses that the agent card is fetched first, unreachable URLs are rejected instead of saved, and persist=true writes to a shared registry file. It does not cover every edge case, but it provides substantial insight into the tool's behavior.
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 three short, front-loaded sentences with no filler. The core action comes first, and the following sentences provide useful behavioral details without repeating the schema or wasting words.
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?
The description adequately covers the main registration flow and persistence behavior, which is enough for a basic call. But it omits important context such as the purpose of headers, what the tool returns after successful registration, and the exact behavior when persist is false, leaving it short of fully complete.
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?
Schema description coverage is 0%, so the description must compensate for missing parameter explanations. It adds real meaning for 'url' (base URL) and 'persist' (shared registry file), but it leaves 'name' and especially 'headers' entirely unexplained, and does not clarify what persist=false means for session lifetime.
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 first sentence names a concrete action ('Register') and a specific resource ('a new A2A agent'), and adds the mechanism ('by its base URL'). This makes the tool's purpose immediately clear and it is obviously distinct from sibling tools like list, send, get, and cancel, though it does not explicitly name those alternatives.
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 implies when to use the tool through 'Register a new A2A agent,' and it gives some context about persistence across sessions. However, it never explicitly contrasts this tool with alternatives or states when not to use it, so the agent has to infer the usage boundary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
a2a_cancel_taskB
Cancel a running A2A task and return its final state.
| Name | Required | Description | Default |
|---|---|---|---|
| agent | Yes | ||
| task_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It does disclose that cancellation returns a final state, which is useful, but it omits side effects, idempotency, error behavior, or whether cancellation is irreversible.
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?
A single, front-loaded sentence that states the core operation and the return value without any filler. Every word earns its place.
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?
For a state-changing tool with no annotations and no output schema, this description is too thin. Missing details include parameter semantics, handling of non-running tasks, failure modes, and any irreversible consequences, leaving the agent to guess important call behavior.
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?
Schema description coverage is 0%, and the description adds no meaning to the 'agent' or 'task_id' parameters beyond their names. Neither the schema nor the description explains what values these accept or how they relate to the cancellation operation.
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 uses a specific verb ('cancel') and a specific resource ('A2A task'), and clarifies that it returns the task's final state. This clearly distinguishes it from sibling tools like a2a_get_task or a2a_send_message, even without naming them.
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 implies this tool is for active/running tasks, giving reasonable contextual usage. However, it does not explicitly state when not to use it, mention alternatives, or explain what to do if the task is already completed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
a2a_get_taskA
Get the current state and output of a previously started A2A task.
| Name | Required | Description | Default |
|---|---|---|---|
| agent | Yes | ||
| task_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns state and output, and 'get' implies a read-only operation. However, it does not mention error behavior for unknown task IDs, whether the call is idempotent, or whether it affects task execution in any way.
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, front-loaded sentence with no fluff. It efficiently communicates the tool's purpose and the key precondition that the task must already exist.
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?
With no annotations, no output schema, and zero parameter descriptions, the tool description is too sparse. An agent cannot reliably know what value 'agent' expects, what the returned state/output structure looks like, or what happens when a task does not exist.
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?
Schema description coverage is 0%, so the description must compensate. The task_id parameter is reasonably inferable from the tool purpose, but 'agent' is ambiguous: it is not explained as an identifier, name, or what agent list it refers to. The description adds no parameter-level meaning.
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 states a specific verb ('Get') and a specific resource ('current state and output of a previously started A2A task'). It is clearly distinguishable from siblings like a2a_send_message, a2a_cancel_task, a2a_list_agents, and a2a_add_agent, none of which retrieve task state.
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 phrase 'previously started' implies this should be used to poll or inspect tasks that were already kicked off, which gives a minimal context. However, it provides no explicit guidance about when to choose this over sibling tools, when not to use it, or whether it is intended for polling workflows.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
a2a_list_agentsA
List configured A2A agents with their skills and reachability.
Set refresh=true to re-fetch agent cards that were previously cached.
| Name | Required | Description | Default |
|---|---|---|---|
| refresh | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the burden. It usefully discloses caching behavior and the effect of refresh, but it does not state that the operation is read-only, describe the return structure, or mention any potential side effects or prerequisites. Some relevant behavior remains implicit.
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?
Two concise sentences: the first states the tool's purpose and result scope; the second explains the only parameter. No filler or duplication of the schema.
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?
For a simple, single optional-parameter list operation, this description is largely complete. It identifies what the result includes, notes the caching behavior, and explains the parameter. A fully explicit return-format statement would be nice, but it is not essential for correct invocation.
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?
Schema description coverage is 0%, so the description must compensate. It does by explaining that refresh=true re-fetches cached agent cards. It stops short of explaining the exact behavior when refresh is false (e.g., serving from cache), but the meaning is strongly implied.
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 states a specific action ('List'), a clear resource ('configured A2A agents'), and the relevant output facets ('skills and reachability'). It is immediately distinguishable from its siblings, which send messages or manage tasks.
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 refresh guidance is clear operational context ('Set refresh=true to re-fetch agent cards that were previously cached'). However, there is no explicit statement about when to prefer this tool over alternatives such as a2a_send_message or a2a_get_task; the distinction is only implied by the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
a2a_send_messageA
Send a message to an A2A agent and return its reply.
Set provider to "litellm-auto" or "github" when the target agent supports provider selection. The selected provider is carried in the message for the local Copilot A2A agent; other agents may ignore it. Pass task_id to continue an existing task, for example to answer an agent that returned state="input_required". If the agent is still working when timeout_s elapses, this returns done=false with a task_id to poll rather than blocking.
| Name | Required | Description | Default |
|---|---|---|---|
| agent | Yes | ||
| message | Yes | ||
| task_id | No | ||
| provider | No | ||
| timeout_s | No | ||
| context_id | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the full behavioral burden. It discloses the asynchronous/non-blocking timeout behavior, the done=false + task_id polling contract, and the nuance that the provider field may be ignored by non-local agents. It does not mention auth, error cases, or side effects, but it covers the most important behavioral traits for correct invocation.
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 front-loaded with the core purpose and then supplies only high-value operational details: provider selection, task continuation, and polling behavior. Every sentence earns its place, and the length is appropriate for the 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?
The description is useful for a complex async tool, but there is no output schema and no annotations, so the description must explain the return contract. It partially does by mentioning 'reply,' 'done=false,' and 'task_id,' yet it never describes the full response shape, error behavior, or the purpose of context_id. An agent could still be uncertain about the exact payload format.
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?
Schema description coverage is 0%, so the description must compensate. It adds meaning for provider, task_id, and timeout_s, and the required agent and message parameters are inferable from 'send a message to an A2A agent.' However, context_id is never explained, leaving one of six parameters semantically undocumented.
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 opens with a specific verb and resource: 'Send a message to an A2A agent and return its reply.' This clearly distinguishes the tool from its siblings (a2a_list_agents, a2a_get_task, a2a_cancel_task, a2a_add_agent), all of which serve different operations.
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 gives clear context for when to use it: sending messages, continuing existing tasks via task_id, responding to state="input_required", and handling non-blocking timeouts. It does not explicitly name a2a_get_task as the polling alternative, but strongly implies the polling workflow by mentioning 'to poll rather than blocking.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
5 tool updates
v0.1.0- First observed
a2a_add_agent - First observed
a2a_cancel_task - First observed
a2a_get_task - First observed
a2a_list_agents - First observed
a2a_send_message
TDQS
Scored across 5 tools
Each tool has a distinct purpose: listing agents, sending messages, getting task state, cancelling tasks, and adding agents. No overlap or ambiguity between them.
All tools follow a consistent 'a2a_' prefix followed by a verb_noun pattern (list_agents, send_message, get_task, cancel_task, add_agent). Naming is predictable and uniform.
Five tools is well-scoped for an A2A bridge: listing, messaging, task management, and agent registration cover the core functionality without being excessive or sparse.
The tool surface covers the essential operations for an A2A bridge. A remove_agent or update_agent tool is missing, but it's not critical; the set supports the primary workflows adequately.
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