mcp-a2a-bridge
mcp-a2a-bridge
Ein MCP-Server, der Coding-Agenten – GitHub Copilot CLI, Claude Code, Codex und Hermes – den Aufruf entfernter A2A-Agenten ermöglicht. Er enthält außerdem ein optionales, schreibgeschütztes Aktivitäts-Dashboard.
Repository: https://github.com/TonPC64/mcp-a2a-bridge
Lizenziert unter der MIT-Lizenz.
Anweisungen für Coding-Agenten finden Sie in AGENTS.md; für die
Installation nutzen Sie skills/setup/SKILL.md, für
Änderungen und Pull Requests
skills/contribute/SKILL.md.
Dashboard-UI-Showcase
Das optionale Dashboard verwendet eine futuristische Liquid-Glass-Oberfläche mit kompakten Agent-Karten, Live-Task-Aktivität, zweispaltigen Task-Zusammenfassungen, Statusanzeigen und responsiven Layouts. Dieser Screenshot verwendet den opt-in Frontend-Mock-Datenmodus, sodass er ohne Verbindung zu echten Agenten oder Offenlegung von Logs reproduziert werden kann.

So führen Sie dieselbe Mock-UI lokal aus:
cd /absolute/path/to/mcp-a2a-bridge/dashboard
npm ci
npm run dev:mock -- --host 127.0.0.1Der Mock-Modus ist nur für die UI-Entwicklung und Screenshots gedacht; normale Builds verwenden die Live-Dashboard-API und SSE.
Related MCP server: agentkit-mesh
Erste Schritte
Setup-Variante wählen
Beide Profile benötigen Python 3.11 oder neuer und uv.
Das optionale Dashboard-Frontend erfordert Node.js 24.15 oder neuer; CI
verwendet die neueste Node.js-24-Version.
Nur MCP
Nur die MCP-Bridge installieren und ausführen. Das erfordert kein Node.js/npm und keinen Dashboard-Frontend-Build.
git clone https://github.com/TonPC64/mcp-a2a-bridge.git mcp-a2a-bridge
cd mcp-a2a-bridge
uv sync --lockedMCP + Dashboard
Die optionale Dashboard-Web-Runtime installieren und anschließend die gebündelten Frontend-Assets erstellen. Node.js und npm werden nur für diesen Build (oder die Frontend-Entwicklung) benötigt, nicht zum Ausführen eines bereits erstellten Dashboards.
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 buildFür Entwicklung und Tests verwenden Sie uv sync --all-extras --locked; es
enthält die Python-Pakete, die die Dashboard-Testsuite benötigt. Die Befehle
unten verwenden /absolute/path/to/mcp-a2a-bridge. Ersetzen Sie dies durch
den absoluten Pfad zu Ihrem Klon; übernehmen Sie nicht den Pfad eines anderen
Benutzers.
A2A-Agenten konfigurieren
Erstellen Sie .a2a-agents.json im Repository-Stammverzeichnis:
{
"agents": {
"example-agent": {
"url": "https://agent.example.invalid",
"headers": {
"Authorization": "Bearer REPLACE_WITH_YOUR_TOKEN"
}
}
}
}Verwenden Sie die Basis-URL eines A2A-Agenten; die Bridge ruft dessen
/.well-known/agent-card.json automatisch ab. Entfernen Sie Authorization
(oder verwenden Sie ein leeres headers-Objekt), wenn der Agent keine
Authentifizierung erfordert. Die Registry kann Geheimnisse enthalten – committen
Sie sie daher nicht und schränken Sie ihre Dateiberechtigungen gegebenenfalls
ein.
Der Registry-Speicherort wird in dieser Reihenfolge aufgelöst:
A2A_BRIDGE_CONFIG.a2a-agents.jsonim aktuellen Verzeichnis des Bridge-Prozesses~/.config/a2a-bridge/agents.json
Für einen Host, der den MCP-Befehl außerhalb des Repositorys startet, setzen
Sie A2A_BRIDGE_CONFIG auf einen absoluten Pfad in der MCP-Umgebung dieses
Hosts. Sie können zur Laufzeit auch einen verifizierten Agenten mit
a2a_add_agent hinzufügen; standardmäßig schreibt dieser in die aufgelöste
Registry-Datei.
MCP-Server registrieren
Jeder Host sollte denselben lokalen Checkout-Befehl starten:
uv run --directory /absolute/path/to/mcp-a2a-bridge mcp-a2a-bridgeDies verwendet die oben erstellte editierbare Installation. Konfigurieren Sie den Host mit dem folgenden portablen Pfad-Platzhalter.
GitHub Copilot CLI – fügen Sie diesen Server zu
~/.copilot/mcp-config.json hinzu:
{
"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 – fügen Sie dies zu ~/.codex/config.toml hinzu:
[mcp_servers.a2a]
command = "uv"
args = ["run", "--directory", "/absolute/path/to/mcp-a2a-bridge", "mcp-a2a-bridge"]Hermes: Verwenden Sie die MCP-Server-Registrierungs-UI oder den
entsprechenden Befehl, wählen Sie einen stdio-Server und geben Sie den
Befehl uv mit diesen Argumenten an:
run --directory /absolute/path/to/mcp-a2a-bridge mcp-a2a-bridgeStarten Sie den Host nach der Registrierung neu und rufen Sie
a2a_list_agents auf, um zu bestätigen, dass die Registry verfügbar und die
Agenten erreichbar sind.
Tools
Tool | Zweck |
| Agenten mit Skills und Erreichbarkeit auflisten |
| Nachricht senden; Task mit |
| Einen früher gestarteten Task abfragen |
| Einen laufenden Task abbrechen |
| Agenten per URL registrieren |
a2a_send_message wartet bis zu timeout_s (standardmäßig 60 Sekunden). Wenn
ein Task noch läuft, gibt es done: false mit einer task_id zurück; fragen
Sie ihn mit a2a_get_task ab. Wenn ein Agent state: "input_required"
zurückgibt, rufen Sie a2a_send_message erneut mit derselben task_id auf.
Dashboard
Das optionale eigenständige Dashboard zeigt konfigurierte Agenten, deren Status/Skills und Live-Task-Aktivität von Bridge-Prozessen auf demselben Rechner. Es ist schreibgeschützt: Es sendet niemals Nachrichten an Agenten.
Installieren Sie das Profil dashboard und erstellen Sie die Assets, bevor
Sie es starten:
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-dashboardEs lauscht standardmäßig nur auf 127.0.0.1:9100. Öffnen Sie
http://127.0.0.1:9100 auf demselben Rechner.
Frontend-Mock-Daten
Um die Dashboard-UI ohne den Python-Dienst oder SSE auszuführen, verwenden Sie den opt-in Mock-Modus. Er enthält repräsentative Agenten und Task-Aktivität für Screenshots:
cd /absolute/path/to/mcp-a2a-bridge/dashboard
npm ci
npm run dev:mock -- --host 0.0.0.0Öffnen Sie die Vite-URL im selben Netzwerk. VITE_USE_MOCK_DATA=true wird
nur vom Frontend-Build/Dev-Server verwendet; normale npm run dev- und
Produktions-Builds verwenden weiterhin die Live-Dashboard-API und SSE.
Aktivieren Sie das Reporting für jeden Bridge-Prozess, dessen Aktivität im Dashboard erscheinen soll. Fügen Sie diese Umgebungsvariable zur MCP-Server-Konfiguration hinzu (oder setzen Sie sie beim manuellen Start):
A2A_BRIDGE_DASHBOARD=1 uv run --directory /absolute/path/to/mcp-a2a-bridge mcp-a2a-bridgeBridge-Prozesse ohne dieses Flag behalten die Aktivität im Arbeitsspeicher und erscheinen nicht im gemeinsamen Dashboard. Das Dashboard kann vor oder nach ihnen gestartet werden.
Umgebungsvariable | Standard | Zweck |
| — | Absoluter Pfad zur JSON-Registry der Agenten |
| nicht gesetzt (aus) | Auf |
|
| Gemeinsame SQLite-Aktivitätsdatenbank |
|
| Quellbezeichnung für von kompatiblen Prozessen geschriebene Aktivität |
|
| Bind-Adresse des Dashboards |
|
| Dashboard-Port |
| nicht gesetzt (aus) | Optionales Bearer-Token, das Dashboard und API schützt |
| nicht gesetzt | Schreibgeschützter Überschreibwert für Hermes' |
| nicht gesetzt | Wird verwendet, um |
LAN-Bereitstellung
Das Dashboard kann absichtlich in einem vertrauenswürdigen LAN ohne Token bereitgestellt werden, indem ein expliziter Nicht-Loopback-Bind gesetzt wird. Dieser Modus hat keine Authentifizierung – verwenden Sie ihn nur in einem privaten, vertrauenswürdigen Netzwerk; setzen Sie ihn niemals dem öffentlichen Internet aus.
Für geschützten LAN-Zugriff setzen Sie ein starkes Token und platzieren Sie den Dienst hinter einem TLS-terminierenden Reverse-Proxy mit eigenen Netzwerkzugriffskontrollen:
export A2A_BRIDGE_DASHBOARD_HOST=0.0.0.0
export A2A_BRIDGE_DASHBOARD_TOKEN="$(openssl rand -hex 32)"
uv run mcp-a2a-bridge-dashboardWenn ein Token gesetzt ist, erfordern Dashboard-HTML, statische Assets,
API-Routen und SSE-Routen es alle. Browser-Benutzer geben es einmal unter
/login ein; das resultierende HttpOnly-Same-Site-Sitzungscookie ermöglicht
EventSource, sich normal zu verbinden. API-Clients können stattdessen
Authorization: Bearer <token> senden. Der Dienst protokolliert das Token
niemals. Ein Token authentifiziert den Zugriff, verschlüsselt aber keinen
Datenverkehr – verwenden Sie den direkten HTTP-LAN-Endpunkt daher nicht über
nicht vertrauenswürdige Netzwerke; verwenden Sie HTTPS am Reverse-Proxy.
Für macOS kann die Vorlage launchd/com.example.a2a-bridge-dashboard.plist
das Dashboard bei der Anmeldung ausführen. Ersetzen Sie die Platzhalter
__REPO__ und __HOME__, bevor Sie sie als LaunchAgent installieren.
Dashboard-Frontend-Entwicklung
Node.js 24.15+ und npm werden nur benötigt, um am Frontend zu arbeiten oder die gebündelten Assets neu zu erstellen:
cd /absolute/path/to/mcp-a2a-bridge/dashboard
npm ci
npm run devVite gibt die Entwicklungs-URL aus (normalerweise http://localhost:5173).
Erstellen Sie die vom Python-Dashboard verwendeten Assets mit
npm run build.
Copilot-A2A-Server
Der gebündelte Server erfordert eine authentifizierte copilot-CLI:
copilot-a2a-agent --port 9002 --cwd /path/to/default/repositoryEr bindet an 127.0.0.1, weil er Copilot mit --allow-all-tools ausführt.
Seine eingehende Aktivität kann denselben opt-in gemeinsam genutzten
SQLite-Speicher verwenden und wird im Dashboard mit Quell- und
Ziel-Feldern angezeigt.
Das Dashboard kann auch Hermes' native Audit-Datei lesen, ohne Hermes zu
verändern. Es prüft A2A_BRIDGE_HERMES_AUDIT, dann
$HERMES_HOME/a2a_audit.jsonl, dann ~/.hermes/a2a_audit.jsonl.
Entwicklung und Tests
Führen Sie die Python-Testsuite aus dem Repository-Stammverzeichnis aus:
uv sync --all-extras --locked
uv run pytest -vFühren Sie Dashboard-Tests aus oder erstellen Sie einen Produktions-Dashboard-
Build aus dashboard/:
npm test
npm run buildCI führt die Python-Suite unter Python 3.11 aus, validiert uv.lock und führt
die Dashboard-Installation, Tests und den Produktions-Build unter der
neuesten Node.js-24-Version aus.
tests/test_integration.py führt einen A2A-Agenten prozessintern aus und testet
die Bridge Ende-zu-Ende.
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.
Maintenance
Related MCP Connectors
Public MCP for agent verification, work discovery and governed interoperability.
Agent communication platform for agent to agent messaging via MCP. Messages, channels, skills.
Discover and call AI agents via MCP. Supports A2A agents and platform agents with async tasks.
Agent-native collaboration network: orchestrate a team of long-running agents from any MCP client.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceMCP server that gives AI agents the ability to discover, match with, and build relationships with other autonomous agents. Supports agent registration, matchmaking, messaging, shared goals, relationship lifecycle management, and real-time event subscriptions.22 npmMIT

agentkit-meshofficial
AlicenseAqualityFmaintenanceEnables agent-to-agent discovery and delegation via MCP, with tools for registering agents, discovering them by keyword matching, and delegating tasks over HTTP.49 npm1MIT- AlicenseAqualityCmaintenanceMCP server for multi-agent AI systems providing mailbox messaging, A2A task delegation, resource coordination, and a web dashboard.2113 npm1MIT
- FlicenseNot gradedqualityDmaintenanceEnables AI agents to discover other agents, publish and match tasks, exchange messages and artifacts, and build transaction-backed reputation over MCP and A2A.-