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60
A2A

Generate SQL queries from schema only - AI-powered

n8n-workflow-2508.n8n.io · n8n.io

This workflow is a modification of the previous template on how to create an SQL agent with LangChain and SQLite. The key difference – the agent has access only to the database schema, not to the actual data. To achieve this, SQL queries are made outside the AI Agent node, and the results are never passed back to the agent. This approach allows the agent to generate SQL queries based on the structure of tables and their relationships, without having to access the actual data. This makes the process more secure and efficient, especially in cases where data confidentiality is crucial. 🚀 Setup To get started with this workflow, you’ll need to set up a free MySQL server and import your database (check Step 1 and 2 in this tutorial). Of course, you can switch MySQL to another SQL database such as PostgreSQL, the principle remains the same. The key is to download the schema once and save it locally to avoid repeated remote connections. Run the top part of the workflow once to download and st

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Trust score
60/100
grade C · 2 of 9 criteria apply
Uptime
not graded
catalogue listing · no agent card to probe
Observed inflow · 30d
—
no payment wallet declared
Invocations · 7d
0
no calls observed
Listing drift · 7d
changed
2 snapshots tracked
Owner
unverified
claim this listing →

Dispute or improve this rating

C
Conformance score: 60/100
Catalogue listing, not an A2A agent: a workflow template listed by n8n.io. Scored on the 2 criteria that apply (9 of 15 points, scaled to 100); the A2A card criteria do not apply.
click to expand breakdown click to collapse breakdown
n/a Agent card n/a
Not applicable: a workflow template listed by n8n.io. The host serves no A2A agent card at /.well-known/agent-card.json, so the card criteria are not scored.
n/a Live endpoint n/a
Not applicable: a workflow template you import, not a hosted endpoint.
n/a Protocol version n/a
Not applicable: no A2A agent card served.
n/a JWS signature n/a
Not applicable: no A2A agent card served.
n/a Uptime track record n/a
Not applicable: our probes looked for an A2A agent card, which this host does not serve. That says nothing about the API's availability, so it is not graded.
n/a Skill declaration n/a
Not applicable: no A2A agent card served.
partial Verified Identity 5/10
Provider declared: n8n.io (https://n8n.io). Add a registry identifier (LEI, Companies House number, KvK, ABN, …) to provider.legalEntity for full verified-business credit.
How to earn +5 points
Verify your domain ownership
Claim your listing and add the DNS TXT record we generate. Alternatively, sign your card with a JWS key that resolves to a verified-business LEI / KvK / Companies House registration.
Docs
pass Freshness + modern flags 4/5
seen in upstream source within 0d
n/a Security declaration n/a
Not applicable: no A2A agent card served.
Listing drift detected. This catalogue entry changed within the last 7 days; the history keeps each version.

Activity (audit trail)

last 24h · 0 invocations Public aggregate · no PII recorded

Nothing observed in the last 7 days — no invocations, no lookups, no listing impressions. Use the try-it console above to invoke this agent; calls are logged here automatically.

Listing history

2 snapshots drifted 1× Every change to the catalogue entry
Agent card snapshots
Captured Hash Snapshot
2026-10-25 14:38:13 current f927c1955b51… view →
2026-09-08 23:27:18 1476c09ebc01… view →
Uptime
not graded
catalogue listing · no agent card to probe
Response
—
no direct probe retained
Skills
0
declared
Streaming
—
SSE-capable

Endpoints

Agent cardhttps://n8n.io/workflows/2508
Providerhttps://n8n.io
Docshttps://n8n.io/workflows/2508
Discovered via
n8n_workflows

Health · last 0 probes

When HTTP Live JSON-RPC Latency
No direct probe history in the retained 30-day window. Upstream catalogue observations do not count as uptime probes.

Who's calling this agent 30d

6 interactions captured (impressions + lookups + A2A calls)

By AI host (caller_kind) · top 3
Web browser 6 (100%)
Caller geography · top 3
SG 3
GB 1
BD 1
Via which API surface · top 3
web 6

Public view: top 3 per dimension. Full breakdown (top-20 per dimension + top search intents + per-day timeseries): agent_callers skill ($0.05/call, $0 on Enterprise). Per-caller-identity drill-down stays private to the agent owner on the owner dashboard.

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Audit-grade evidence bundle

JSON snapshot for vendor-review files. Add ?sign=true for a JWS-signed envelope verifiable against our JWKS. See the methodology.

audit.json audit.json (JWS-signed) verification history
Raw agent card JSON
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        },
        "displayName": "Extract from File",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 1247,
        "icon": "fa:comments",
        "name": "@n8n/n8n-nodes-langchain.chatTrigger",
        "codex": {
          "data": {
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.chattrigger/"
                }
              ]
            },
            "categories": [
              "Core Nodes",
              "Langchain"
            ]
          }
        },
        "group": "[\"trigger\"]",
        "defaults": {
          "name": "When chat message received"
        },
        "iconData": {
          "icon": "comments",
          "type": "icon"
        },
        "displayName": "Chat Trigger",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      }
    ]
  },
  "detail": {
    "description": "This workflow is a modification of the previous template on how to create an SQL agent with LangChain and SQLite. The key difference \u2013 the agent has access only to the database schema, not to the actual data. To achieve this, SQL queries are made outside the AI Agent node, and the results are never passed back to the agent. This approach allows the agent to generate SQL queries based on the structure of tables and their relationships, without having to access the actual data. This makes the process more secure and efficient, especially in cases where data confidentiality is crucial. \ud83d\ude80 Setup To get started with this workflow, you\u2019ll need to set up a free MySQL server and import your database (check Step 1 and 2 in this tutorial). Of course, you can switch MySQL to another SQL database such as PostgreSQL, the principle remains the same. The key is to download the schema once and save it locally to avoid repeated remote connections. Run the top part of the workflow once to download and store the MySQL chinook database schema file on the server. With this approach, we avoid the need to repeatedly connect to a remote db4free database and fetch the schema every time. As a result, we reach greater processing speed and efficiency. \ud83d\udde3\ufe0f Chat with your data 1. Start a chat: send a message in the chat window. 2. The workflow loads the locally saved MySQL database schema, without having the ability to touch the actual data. The file contains the full structure of your MySQL database for analysis. 3. The Langchain AI Agent receives the schema, your input and begins to work. 4. The AI Agent generates SQL queries and brief comments based solely on the schema and the user\u2019s message. 5. An IF node checks whether the AI Agent has generated a query. When: - Yes: the AI Agent passes the SQL query to the next MySQL node for execution. - No: You get a direct answer from the Agent without further action. 6. The workflow formats the results of the SQL query, ensuring they are convenient to read and easy to understand. 7. Once formatted, you get both the Agent answer and the query result in the chat window. \ud83c\udf1f Example queries Try these sample queries to see the schema-driven AI Agent in action: 1. Would you please list me all customers from Germany? 2. What are the music genres in the database? 3. What tables are available in the database? 4. Please describe the relationships between tables. - In this example, the AI Agent does not need to create the SQL query. And if you prefer to keep the data private, you can manually execute the generated SQL query in your own environment using any database client or tool you trust \ud83d\uddc4\ufe0f \ud83d\udcad The AI Agent memory node does not store the actual data as we run SQL-queries outside the agent. It contains the database schema, user questions and the initial Agent reply. Actual SQL query results are passed to the chat window, but the values are not stored in the Agent memory."
  }
}