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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.

A2A Bridge Dashboard-Desktop-Showcase

So führen Sie dieselbe Mock-UI lokal aus:

cd /absolute/path/to/mcp-a2a-bridge/dashboard
npm ci
npm run dev:mock -- --host 127.0.0.1

Der 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 --locked

MCP + 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 build

Für 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:

  1. A2A_BRIDGE_CONFIG

  2. .a2a-agents.json im aktuellen Verzeichnis des Bridge-Prozesses

  3. ~/.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-bridge

Dies 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-bridge

Codex – 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-bridge

Starten 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

a2a_list_agents

Agenten mit Skills und Erreichbarkeit auflisten

a2a_send_message

Nachricht senden; Task mit task_id fortsetzen

a2a_get_task

Einen früher gestarteten Task abfragen

a2a_cancel_task

Einen laufenden Task abbrechen

a2a_add_agent

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-dashboard

Es 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 0.0.0.0

Öffnen 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-bridge

Bridge-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

A2A_BRIDGE_CONFIG

—

Absoluter Pfad zur JSON-Registry der Agenten

A2A_BRIDGE_DASHBOARD

nicht gesetzt (aus)

Auf 1 setzen, um die Aktivität dieses Bridge-Prozesses zu speichern

A2A_BRIDGE_ACTIVITY_DB

~/.config/a2a-bridge/activity.sqlite3

Gemeinsame SQLite-Aktivitätsdatenbank

A2A_BRIDGE_ACTIVITY_SOURCE

remote

Quellbezeichnung für von kompatiblen Prozessen geschriebene Aktivität

A2A_BRIDGE_DASHBOARD_HOST

127.0.0.1

Bind-Adresse des Dashboards

A2A_BRIDGE_DASHBOARD_PORT

9100

Dashboard-Port

A2A_BRIDGE_DASHBOARD_TOKEN

nicht gesetzt (aus)

Optionales Bearer-Token, das Dashboard und API schützt

A2A_BRIDGE_HERMES_AUDIT

nicht gesetzt

Schreibgeschützter Überschreibwert für Hermes' a2a_audit.jsonl

HERMES_HOME

nicht gesetzt

Wird verwendet, um $HERMES_HOME/a2a_audit.jsonl zu finden, wenn kein Überschreibwert gesetzt ist

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-dashboard

Wenn 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 dev

Vite 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/repository

Er 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 -v

Führen Sie Dashboard-Tests aus oder erstellen Sie einen Produktions-Dashboard- Build aus dashboard/:

npm test
npm run build

CI 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 tools
a2a_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.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYes
nameYes
headersNo
persistNo

TDQS

A3.5/5.0
Behavior4/5

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.

Conciseness5/5

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.

Completeness3/5

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.

Parameters2/5

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.

Purpose4/5

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.

Usage Guidelines3/5

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
agentYes
task_idYes

TDQS

B3.3/5.0
Behavior3/5

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.

Conciseness5/5

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.

Completeness2/5

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.

Parameters1/5

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.

Purpose5/5

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.

Usage Guidelines3/5

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
agentYes
task_idYes

TDQS

A3.5/5.0
Behavior3/5

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.

Conciseness5/5

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.

Completeness2/5

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.

Parameters2/5

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.

Purpose5/5

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.

Usage Guidelines3/5

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
refreshNo

TDQS

A4/5.0
Behavior3/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines3/5

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
agentYes
messageYes
task_idNo
providerNo
timeout_sNo
context_idNo

TDQS

A4.1/5.0
Behavior4/5

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.

Conciseness5/5

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.

Completeness3/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

  1. 5 tool updatesv0.1.0
    • First observeda2a_add_agent
    • First observeda2a_cancel_task
    • First observeda2a_get_task
    • First observeda2a_list_agents
    • First observeda2a_send_message

TDQS

A3.9/5.0

Scored across 5 tools

Disambiguation5/5

Each tool has a distinct purpose: listing agents, sending messages, getting task state, cancelling tasks, and adding agents. No overlap or ambiguity between them.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness4/5

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

ActivitySlowing
ResponsivenessNo issues

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    license
    Not graded
    quality
    D
    maintenance
    MCP 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 npm
    MIT
  • A
    license
    A
    quality
    F
    maintenance
    Enables agent-to-agent discovery and delegation via MCP, with tools for registering agents, discovering them by keyword matching, and delegating tasks over HTTP.
    4
    9 npm
    1
    MIT
  • F
    license
    Not graded
    quality
    D
    maintenance
    Enables AI agents to discover other agents, publish and match tasks, exchange messages and artifacts, and build transaction-backed reputation over MCP and A2A.
    -