Title: Resources
URL Source: https://plaid.com/docs/resources/mcp/
Markdown Content:
Plaid offers two Model Context Protocol (MCP) servers:
- The Dashboard MCP server is a remote MCP server hosted by Plaid. It provides Production diagnostics and analytics tools, including tools for debugging Items, reviewing Link conversion data, and checking usage metrics.
- The Plaid AI coding toolkit includes a local MCP server for development. It runs as a subprocess of an MCP client, such as Claude, Cursor, Codex, VS Code, or Zed, and provides tools for mock data generation, documentation search, Sandbox tokens, and webhook simulation.
This article covers the Dashboard MCP server. You can connect to it from an MCP client, or you can use it in a model provider's API as a remote MCP server.
The Dashboard MCP server is under active development. Breaking changes may occur, and Plaid currently provides limited support.
The Dashboard MCP server works with Production data only. Your team must be approved for Production access with at least one Plaid product to use the MCP server.
Use this option when your application calls a model provider API and lets the model call Dashboard MCP tools during that request. This is an API integration with a remote MCP server. OpenAI exposes remote MCP servers as mcp tools in the Responses API. Anthropic calls this feature the MCP connector in the Messages API.
For this flow, your backend creates or refreshes a Plaid OAuth token and passes it to the model provider SDK with the Dashboard MCP server URL. The field name depends on the provider. OpenAI uses authorization; Anthropic uses authorization_token.
Your model provider account must have access to remote MCP servers or hosted tools.
To connect to the Dashboard MCP server, first create an OAuth token with the mcp:dashboard scope and the client_credentials grant type by calling /oauth/token.
curl -X POST https://production.plaid.com/oauth/token \
-H 'Content-Type: application/json' \
-d '{
"client_id": "${PLAID_CLIENT_ID}",
"client_secret": "YOUR_PRODUCTION_SECRET",
"grant_type": "client_credentials",
"scope": "mcp:dashboard"
}'
The response includes an access_token for Dashboard MCP requests and a refresh_token for requesting a new access token.
The Dashboard MCP server is available at https://api.dashboard.plaid.com/mcp/ and uses the Streamable HTTP protocol. When you communicate with the MCP server directly, pass the access_token from the authorization step in an Authorization: Bearer <access_token> header.
The connection method depends on the LLM client library. Most provider SDKs expose remote MCP servers as tools that the model can call. See your model provider's documentation, such as OpenAI or Anthropic. Use a current version of your model provider's client library.
The following examples show how to access Link analytics through the Dashboard MCP server using the OpenAI or Anthropic Python client libraries.
These examples use the following environment variables: PLAID_CLIENT_ID, PLAID_PRODUCTION_SECRET, and either OPENAI_API_KEY or ANTHROPIC_API_KEY.
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import json
import os
import plaid
from openai import OpenAI
from plaid.api import plaid_api
from plaid.api_client import ApiClient
from plaid.model.o_auth_grant_type import OAuthGrantType
from plaid.model.o_auth_token_request import OAuthTokenRequest
DASHBOARD_MCP_SERVER_URL = "https://api.dashboard.plaid.com/mcp/"
def get_plaid_client():
configuration = plaid.Configuration(
host=plaid.Environment.Production,
api_key={
"clientId": os.environ["PLAID_CLIENT_ID"],
"secret": os.environ["PLAID_PRODUCTION_SECRET"],
},
)
api_client = ApiClient(configuration)
return plaid_api.PlaidApi(api_client)
def request_or_refresh_access_token():
# This example creates a new access token each run.
plaid_client = get_plaid_client()
oauth_token_request = OAuthTokenRequest(
grant_type=OAuthGrantType("client_credentials"),
scope="mcp:dashboard",
)
response = plaid_client.oauth_token(oauth_token_request)
return response.access_token
def main() -> None:
"""Retrieve Link analytics through the Dashboard MCP server."""
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
dashboard_token = request_or_refresh_access_token()
response = client.responses.create(
model="gpt-5.5",
tools=[
{
"type": "mcp",
"server_label": "plaid",
"server_url": DASHBOARD_MCP_SERVER_URL,
"require_approval": "never",
"authorization": dashboard_token,
}
],
input=(
"Use the Dashboard MCP server to get Link analytics from "
"2025-05-01 through 2025-05-08. Return Link opens, "
"institution selections, and handoffs."
),
)
for item in response.output:
if item.type == "mcp_list_tools":
print("\n=== MCP Tools ===")
for tool in item.tools:
print(f" - {tool['name']}")
elif item.type == "mcp_call":
print("\n=== MCP Call ===")
print(f"Tool: {item.name}")
try:
# The arguments field is a JSON string. Parse it for readability.
args = json.loads(item.arguments)
except (TypeError, json.JSONDecodeError):
args = item.arguments
print("Arguments:")
print(json.dumps(args, indent=2))
elif item.type == "message":
print("\n=== Assistant Message ===")
# The content field is a list of ResponseOutputText objects.
texts = []
for part in item.content:
# Depending on SDK version, `text` may be an attribute or dict key.
text = getattr(part, "text", None) or (
part.get("text") if isinstance(part, dict) else None
)
if text:
texts.append(text)
print("\n".join(texts))
else:
print(f"\n=== Unhandled output type: {item.type} ===")
print(item)
if __name__ == "__main__":
main()
=== MCP Tools ===
- plaid_debug_item
- plaid_get_link_analytics
- plaid_get_tools_introduction
- plaid_get_usages
- plaid_list_teams
=== MCP Call ===
Tool: plaid_list_teams
Arguments:
{}
=== MCP Call ===
Tool: plaid_get_link_analytics
Arguments:
{
"from_date": "2025-05-01",
"team_id": "<team_id>",
"to_date": "2025-05-08"
}
=== Assistant Message ===
From 2025-05-01 through 2025-05-08:
- Link opens: 58
- Institution selections: 29
- Handoffs: 18
Access tokens for the MCP server expire after 15 minutes. If the access token expires, MCP requests fail with a 401 status code or a provider-specific authentication error. Create a new token or call /oauth/token with the refresh_token.
curl -X POST https://production.plaid.com/oauth/token \
-H 'Content-Type: application/json' \
-d '{
"client_id": "${PLAID_CLIENT_ID}",
"client_secret": "YOUR_PRODUCTION_SECRET",
"refresh_token": "YOUR_REFRESH_TOKEN",
"grant_type": "refresh_token"
}'
The Dashboard MCP server currently supports the following tools:
| Tool | Purpose |
|---|
plaid_debug_item | Diagnose a Plaid Item using related metadata. |
plaid_get_link_analytics | Retrieve Link funnel, conversion, and error metrics. |
plaid_get_tools_introduction | Get usage guidance for Dashboard MCP tools. |
plaid_get_usages | Retrieve product usage metrics and API request volumes. |
plaid_list_teams | List teams available to the OAuth token. |
For more details about the tools provided by the Dashboard MCP server, connect to the server using a tool such as the MCP Inspector.