OpenText Analytics Database 26.2.x – MCP server

Admin: Claude Desktop example

Mon, 01 Jan 0001 00:00:00 +0000

This page provides examples for configuring the OpenText™ Analytics Database MCP server with Claude Desktop.

Configuring MCP server with Claude Desktop

Claude Desktop can connect to your MCP server to enable database query capabilities within Claude conversations.

Prerequisites

Claude Desktop installed (latest version).
MCP server running and accessible.
Valid JWT token for authentication.
Certificates to access VCluster server from the database administrator. For database adminstrators, the default certificates are located at /opt/vertica/config/vcluster_server/.
NPX tool mcp-remote (npm install -g mcp-remote).

Configuration

The Claude Desktop configuration file location varies by OS:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

Edit the configuration file and add your MCP server. Example configuration:

{
  "mcpServers": {
    "vertica-mcp-server": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://ip-address/mcp",
        "--insecure",
        "--tls-skip-verify",
        "--cert",
        "<path_to_certificate>\\certfile.pem",
        "--key",
        "<path_to_key_file>\\keyfile.key",
        "--ca-cert".
        "<path_to_ca_cert>\\ca.pem",
        "--header",
        "X-API-Key:${API_KEY_VALUE}"
        ],
      "env": {
        "NODE_TLS_REJECT_UNAUTHORIZED": "0",
        "API_KEY_VALUE": "replace with your API key"
      }
    }
  }
}

Verifying the configuration

Restart Claude Desktop after saving the configuration (make sure kill all background processes)
Check Claude's MCP status - look for your server in the MCP panel
Test with a simple query:
```
SELECT version()
```

Admin: Curl examples

Mon, 01 Jan 0001 00:00:00 +0000

This page provides curl examples for testing and automating interactions with the OpenText™ Analytics Database MCP server. This is provided as a developer guide for interacting with the APIs.

Using the MCP server with curl

For testing and automation, you can interact with the MCP server directly using curl.

Basic request format

All MCP requests use JSON-RPC 2.0 format:

curl -k -X POST https://localhost:8667/mcp \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: YOUR_JWT_TOKEN" \
  -d '{
    "jsonrpc": "2.0",
    "method": "METHOD_NAME",
    "params": {
      "key": "value"
    },
    "id": 1
  }'

Initialize connection

First, initialize the MCP session:

curl -k -X POST https://localhost:8667/mcp \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." \
  -d '{
    "jsonrpc": "2.0",
    "method": "initialize",
    "params": {
      "protocolVersion": "2024-11-05",
      "capabilities": {
        "roots": {
          "listChanged": true
        }
      },
      "clientInfo": {
        "name": "curl-client",
        "version": "1.0.0"
      }
    },
    "id": 1
  }'

Response:

{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "protocolVersion": "2024-11-05",
    "serverInfo": {
      "name": "vertica-mcp-server",
      "version": "1.0.0"
    },
    "capabilities": {
      "tools": {},
      "resources": {},
      "prompts": {}
    }
  }
}

List available tools

Discover what tools are available:

curl -k -X POST https://localhost:8667/mcp \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/list",
    "params": {},
    "id": 2
  }'

Response:

{
  "jsonrpc": "2.0",
  "id": 2,
  "result": {
    "tools": [
      {
        "name": "execute_query",
        "description": "Execute SQL queries against Vertica database",
        "inputSchema": {
          "type": "object",
          "properties": {
            "query": {
              "type": "string",
              "description": "SQL query to execute"
            },
            "subcluster": {
              "type": "string",
              "description": "Optional subcluster name"
            }
          },
          "required": ["query"]
        }
      },
      {
        "name": "submit_queue_query",
        "description": "Submit long-running query to job queue"
      },
      {
        "name": "list_jobs",
        "description": "List all jobs in the queue"
      }
    ]
  }
}

Execute a query

Run a simple SQL query:

curl -k -X POST https://localhost:8667/mcp \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "execute_query",
      "arguments": {
        "query": "SELECT version()"
      }
    },
    "id": 3
  }'

Response:

{
  "jsonrpc": "2.0",
  "id": 3,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "{\"columns\":[\"version\"],\"rows\":[{\"version\":\"Vertica Analytic Database v24.3.0-0\"}],\"count\":1}"
      }
    ]
  }
}

Execute query with subcluster routing

Route query to a specific subcluster:

curl -k -X POST https://localhost:8667/mcp \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "execute_query",
      "arguments": {
        "query": "SELECT node_name, subcluster_name FROM nodes",
        "subcluster": "analytics_cluster"
      }
    },
    "id": 4
  }'

Submit long-running query to queue

For complex analytics queries that take longer to complete:

curl -k -X POST https://localhost:8667/mcp \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "submit_queue_query",
      "arguments": {
        "query": "SELECT customer_id, SUM(amount) FROM sales GROUP BY customer_id",
        "description": "Monthly sales by customer"
      }
    },
    "id": 5
  }'

Response:

{
  "jsonrpc": "2.0",
  "id": 5,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "Job submitted successfully. Job ID: 550e8400-e29b-41d4-a716-446655440000"
      }
    ]
  }
}

Check job status

curl -k -X POST https://localhost:8667/mcp \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "get_job",
      "arguments": {
        "job_id": "550e8400-e29b-41d4-a716-446655440000"
      }
    },
    "id": 6
  }'

List all jobs

curl -k -X POST https://localhost:8667/mcp \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "list_jobs",
      "arguments": {}
    },
    "id": 7
  }'

Cancel a job

curl -k -X POST https://localhost:8667/mcp \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "cancel_job",
      "arguments": {
        "job_id": "550e8400-e29b-41d4-a716-446655440000"
      }
    },
    "id": 8
  }'

Health check

Check server health without authentication:

curl -k https://localhost:8667/health

Response:

{
  "status": "healthy",
  "total_pools": 3,
  "max_pools": 100
}

Using jq for pretty output

Format JSON responses with jq:

curl -k -X POST https://localhost:8667/mcp \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "execute_query",
      "arguments": {
        "query": "SELECT table_name FROM tables LIMIT 5"
      }
    },
    "id": 1
  }' | jq '.'

Bash script example

Create a reusable script for queries:

#!/bin/bash
# mcp_query.sh

API_KEY="eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."
SERVER="https://localhost:8667/mcp"
QUERY="$1"

if [ -z "$QUERY" ]; then
    echo "Usage: $0 'SQL QUERY'"
    exit 1
fi

curl -k -X POST "$SERVER" \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: $API_KEY" \
  -d "{
    \"jsonrpc\": \"2.0\",
    \"method\": \"tools/call\",
    \"params\": {
      \"name\": \"execute_query\",
      \"arguments\": {
        \"query\": \"$QUERY\"
      }
    },
    \"id\": 1
  }" | jq -r '.result.content[0].text'

Usage:

chmod +x mcp_query.sh
./mcp_query.sh "SELECT COUNT(*) FROM customers"

Admin: Query profiling

Mon, 01 Jan 0001 00:00:00 +0000

The Query Profiling feature in MCP Server enables deep analysis of SQL queries, visualization of execution plans, and export of detailed query profile data. These tools help you understand query performance, resource usage, and execution flow across OpenText™ Analytics Database nodes and subclusters.

Key features

Profile storage in tables: Save query profiles in persistent tables within a specified schema, or use temporary tables for ad-hoc analysis.
Large Language Model (LLM)-driven analysis: Analyze queries using the MCP server's LLM, which processes operator statistics and performance aggregates (such as execution time and resource usage) computed by profiling tools.
Interactive plan tree visualization: Visualize query plan trees, including data flow volumes and execution times per node, with interactive HTML output.
Export/import profile data: Export all profile tables as a compressed tarball (.tar.gz) for sharing or offline analysis, and import them into other clusters or tools.
Subcluster and sandbox support: Profile query execution on specific subclusters or sandboxes by routing requests to the desired subcluster or sandbox.
Prompt support: Configure and execute analysis prompts with flexible input parameters to tailor profiling to your specific needs.

Profiling workflows

Query profiling supports two distinct workflows:

Profile and analyze using prompts – A guided, step-by-step approach where the LLM orchestrates the entire workflow, including job monitoring and analysis generation.
Direct tool access – Ask the LLM to use specific profiling tools directly without the built-in prompts, giving you fine-grained control over each step.

Both approaches produce the same profiling results that can be visualized, exported, and imported. Choose the approach that best fits your workflow.

Profile and analyze a query using prompts

Configure prompt inputs

In the LLM, click Add Connectors, select Add from vertica-mcp-server, and then click Analyze Query.
To begin profiling a SQL query, enter the required prompt details and click Add prompt:
- Mode: Set to new to execute and profile a new query, or existing to analyze a previously executed query by providing its transaction ID and statement ID.
- Query: The query you want to profile (for example, select avg(price) from sales;).
- Target_schema: The schema where results will be stored (for example, qprof1). A new schema is automatically created if it does not already exist.
- Key_id: A unique identifier for this profiling run (for example, k1). Profile table names have this ID appended (for example, qprof_query_profiles_k1).
- Transaction_id: The transaction id for an existing query.
- Statement_id: The statement id for an existing query.
- User_instructions: Additional instructions to customize the analysis.
- Analysis_scope: Specify the depth of analysis (basic or detailed). Defaults is basic.
- Subcluster: The subcluster where the query should be executed.
- Sandbox: The sandbox where the query should be executed. If both subcluster and sandbox are specified, sandbox is ignored.
Note
- Transaction ID and Statement ID: Provide Transaction_id and Statement_id to profile an already-executed query, or provide Query with Mode=new to execute and profile a new query. These fields are required when using Mode=existing, and should be left empty when using Mode=new.
- Key ID: If you leave the Key ID blank for mode existing, the system auto-generates a timestamp-based identifier. Using an explicit Key ID makes it easier to reference and retrieve your profiling results later.
Click analyze_query_text in the chat to view the analysis workflow and tools the LLM will use.

Review workflow steps

The analysis workflow displays the following steps:

Execute query: The qprof_execute tool runs the query with profiling enabled.
Generate job ID: A job_id is created to track the asynchronous execution.
Monitor completion: The LLM checks the job status once. If still running, it pauses and waits for your confirmation before checking again to prevent infinite loops.

Execute, monitor, and review analysis

When processing your analysis, the LLM performs these steps:

Submit and monitor: The job is submitted and checked for completion. If still running, the LLM pauses and requests confirmation before checking again.
Generate summary: Once the job completes, the LLM produces a comprehensive profile analysis, including performance assessment, key metrics (duration, success, queue wait time, nodes involved), bottleneck analysis, and optimization recommendations.

Profile storage

Profile data persists in the specified database schema and tables.

Analyze query profiles directly

Alternatively, you can ask the LLM to use specific profiling tools without the built-in prompts. This approach gives you direct control and works independently of the prompt-based workflow.

To use these tools, ask the LLM directly in the chat. For example, "Analyze this query profile using qprof_get_operator_stats" or "Get a summary of my query execution".

You can also analyze existing query profiles saved from previous runs. Provide the schema and key ID where the profile data is stored. For example: "Give me a summary of the query profile that is saved in the schema xyz with key slow_query". This is useful for analyzing historical query executions without re-executing them.

Available tools:

qprof_get_profile_summary: Get a high-level summary of the query execution, including duration, status, and resource usage.
qprof_get_events: Retrieve warnings, optimization hints, and execution issues.
qprof_get_resources: Examine resource pool acquisition details and queue wait times.
qprof_get_steps: See a detailed breakdown of execution phases and timing.
qprof_get_operator_stats: Analyze operator-level performance metrics (for example, slowest operators, memory usage).
qprof_get_path_stats: View aggregated statistics at the query plan path level.
qprof_get_plan: Get the raw EXPLAIN plan for the query.
qprof_get_plan_tree: Retrieve the complete query plan tree with structure, metrics, and operator details. This is best suited for LLM or programmatic analysis.

Each tool provides a focused view or set of metrics to help you understand and troubleshoot query performance.

Visualize, export, and import results

The following capabilities work with both profiling approaches above. Use them to interact with and share your profiling data.

Visualize query profile tree

You can visualize the query execution tree by asking the LLM to generate an interactive visualization:

Ask the LLM to visualize the query profile tree. Optionally, provide your MCP server IP and port.
The LLM generates an HTML file saved on the MCP server and provides a link to view it. To access the link, ensure you have network access to the MCP server's IP address and port (accounting for any port mappings in your environment).
Open the provided link in a browser to view the visualization.
The visualization tree displays all the details of the query profile. You can zoom in and out and interact with the query tree to analyze query execution.

Note

The query profile tree is generated as an HTML file on the MCP server rather than sent directly to your agent. This approach significantly reduces token usage. Alternatively, you can request an embedded object format to load the tree directly in agents such as Claude Desktop, though this option requires more tokens.

Export query profile data

You can export all profile tables as a compressed tarball (.tar.gz) for sharing or further analysis.

Ask the LLM to export the profiling data.
The system generates the profile export details and a download link for the profile tarball (.tar.gz).
Open the link in a browser to download the tarball (.tar.gz) to share or analyze offline.

Import and analyze query profiles

You can import profile data from other clusters or tools for analysis in your current environment. You can also import a tarball (.tar.gz) into VCluster using Query Profile and use the target schema and key ID to analyze using the MCP Server Query Profiling tools. For more information about Query Profile in the VCluster UI, see Query Profile.