exasol-mcp-server-t2s-option 1.0.0
Exasol MCP Server with a experimental text-to-SQL option.
Requires Python: <=3.13,>=3.10
Keywords: exasol, MCP server, Text-to-SQL

# Exasol MCP Server with Text-to-SQL option

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Provides an LLM access to the Exasol database via MCP tools. Includes tools  
for reading the database metadata and executing data reading queries, and an  
optional tool for translating natural language into SQL statements for the Exasol  
database.  
  

## Disclaimer

The accuracy of Large Language Models (LLM) depend on the training set used, and on further  
methods like Retrieval Augmented Generation (RAG). Large Language Models can make mistakes and  
produce faulty or wrong results!  

Check each result produced by the Text-to-SQL option of this experimental MCP-Server!

To mitigate further security related risks this MCP-Server allows Read-Only queries, only.

Nor Exasol as a company, nor the author(s) of the Text-to-SQL option can be held liable for  
damage to any possible kind by using this software. Moreover, the Safe Harbour Statement at the  
end of this README shall remain valid.

__Do NOT use the MCP-Server with the Text-to-SQL option if you do not agree to these conditions!__
  

## Introduction

Text-to-SQL is nothing unknown or rarely technology anymore. However, most of the solutions rely on  
publicly served Large Langauge Models (LLM) by companies like OpenAI, or Antrophic. This contradicts  
the principle of *Data  Sovereignty* where your keep full control of your data or metadata. Moving  
the transformation of natural language into SQL is not enough, as widely used AI Desktops, e.g.  
Antrophic's Claude utilize a public served LLM to render the results. To fully protect your data you  
have to use a AI Desktop application which allows to use a self-hosted Large Language model.

The MCP sever with the text-to-sql option can be used with commercially AI Desktops like Claude or  
open source AI Desktops like Jan.ai.


## Features

- Checks the relevance of a natural language question for a requested database schema
- Checks the VectorDB for similar questions and give hints if feasible
- Transforms the question into an SQL statement
- Checks if the SQL statement is allowed; currently, we only allow read-only statements.
- Executes the SQL statement
- Checks if SQL statement is valid
  - If a result set is returned, the question, the SQL statement, and some metadata is stored in a VectorDB
- If required, rewrites the question
- Generates a result
  

### Workflow of Text-to-SQL Agent

This is the workflow of the Text-to-SQL agent, coded with the langgraph library.

<img src="./exasol/ai/mcp/server/text_to_sql_option/images/langgraph_workflow.png" width="640"  alt=""/>
  

## Prerequisites

- [Python](https://www.python.org/) >= 3.10.
- MCP Client application, e.g. [Claude Desktop](https://claude.ai/download), or [Open-WebUI](https://github.com/open-webui/open-webui)

* A dedicated Large Language Model (LLM) server with a LLM of your choice loaded, e.g.:  
  
  * LM-Studio (https://lmstudio.ai) or  
  * Ollama (https://ollama.com).  
  
Preferable with GPU support. For accessing the servers the OpenAI API is used. You can use  
any other LLM server application which supports the OpenAI API. 
  

## Installation

### Remark

Do not configure the Exasol supported MCP server and this MCP server at the same time within the  
AI tool of your choice. This MCP server will use the same version numbers as the officially  
supported Exasol MCP-Server. Equal version numbers indicate the same functionality, plus the  
Text-to-SQL option.

Ensure the `uv` package is installed. If uncertain call
```bash
uv --version
```
To install `uv`, please follow [the instructions](https://docs.astral.sh/uv/getting-started/installation/)
in the `uv` official documentation.
  
Depending on the AI Desktop application or Frontend there are two different installation methods,
one with a configuration that calls the MCP-Server directly, the second option requires a 
so-called proxy server.


### Using the server with the Claude Desktop (and probably others).

To enable the Claude Desktop using the Exasol MCP server, the latter must be listed
in the configuration file `claude_desktop_config.json`.

To find the configuration file, click on the Settings and navigate to the
“Developer” tab. This section contains options for configuring MCP servers and other  
developer features. Click the “Edit Config” button to open the configuration file in  
the editor of your choice.

Add the Exasol MCP server to the list of MCP servers as shown in this configuration
example.
```json
{
  "mcpServers": {
    "exasol_db": {
      "command": "uvx",
      "args": ["--from exasol-mcp-server-t2s exasol-mcp-server"],
      "env": {
        "EXA_DSN": "exasol-server-hostname:8563",
        "EXA_USER": "my-user-name",
        "EXA_PASSWORD": "my-password",
        "EXA_MCP_SETTINGS": "path-to-your-settings-file (see below)"
      }
    },
    "other_server": {}
  }
}
```

With these settings, uv will install and run the "exasol-mcp-package" in an
ephemeral environment, using the default `uv` parameters and default server settings.  
  
Other AI Desktop applications may use the same syntax to configure MCP servers.  
Consult  the documentation of the respective AI Desktop application for detailed   
information about configuring the MCP server.

### Using the server via OPenAPI interface

First, install the prox server with

```
pip install mcpo
```

and start the proxy server 

```
mcpo --port 8000 --env EXA_DSN=<database-dsn> --env EXA_USER=<database-username> --env EXA_PASSWORD=<user-password> --env EXA_MCP_SETTINGS=<path-to-settings-file>
-- uvx --from exasol-mcp-server-t2s exasol-mcp-server
```

Configure you AI application, e.g. Open-WebUI, of choice for the tool server and point him to the proxy server.

## Configuration settings

The configuration is split in two parts, one for the Exasol supported MCP-Server, and one  
for the text-to-sql option.  
  

### Original Settings for the Exasol MCP Server

Most importantly, the server configuration specifies if reading the data using SQL
queries is enabled. Note that reading is disabled by default. To enable the data
reading, the `enable_read_query` property must be set to true (see the
configuration settings json below).

The server configuration settings can also be used to enable/disable or filter the
listing of a particular type of database objects. Similar settings are defined for
the following object types:
```
schemas,
tables,
views,
functions,
scripts
```
The settings include the following properties:
- `enable`: a boolean flag that enables or disables the listing.
- `like_pattern`: filters the output by applying the specified SQL LIKE condition to
the object name.
- `regexp_pattern`: filters the output by matching the object name with the specified
regular expression.

The settings can be specified using another environment variable - `EXA_MCP_SETTINGS`.
They should be written in the json format. The json text can be set directly as a
value of the environment variable, for example
```json
{"EXA_MCP_SETTINGS": "{\"schemas\": {\"like_pattern\": \"my_schemas\"}"}
```
Note that double quotes in the json text must be escaped, otherwise the environment
variable value will be interpreted, not as a text, but as a part of the outer json.

Alternatively, the settings can be written in a json file. In this case, the
`EXA_MCP_SETTINGS` should contain the path to this file, e.g.
```json
{"EXA_MCP_SETTINGS": "path_to_settings.json"}
```

The following json shows the default configuration settings.
```json
{
  "schemas": {
    "enable": true,
    "like_pattern": "",
    "regexp_pattern": ""
  },
  "tables": {
    "enable": true,
    "like_pattern": "",
    "regexp_pattern": ""
  },
  "views": {
    "enable": false,
    "like_pattern": "",
    "regexp_pattern": ""
  },
  "functions": {
    "enable": true,
    "like_pattern": "",
    "regexp_pattern": ""
  },
  "scripts": {
    "enable": true,
    "like_pattern": "",
    "regexp_pattern": ""
  },
  "enable_read_query": false,
  "enable_text_to_sql": true
}
```

### Settings for the Text-to-SQL option

The specific settings for the Text-to-SQL option are to be set in a ".env" file in your  
home directory file as follows:
```
MCP_SERVER_EXASOL_SECRET_KEY=<secret-key>>
MCP_EXASOL_DATABASE_HOST=<your Exasol database URL>
MCP_EXASOL_DATABASE_USER=<your username>
MCP_EXASOL_DATABASE_PASSWORD=<your encrypted uer passpword>
MCP_OPENAI_SERVER_URL=<your LLM server URL>
MCP_OPENAI_SERVER_API_KEY=<API-Key of your LLM Server>
MCP_OPENAI_SERVER_MODEL_NAME_TRANSFORMATION=<your model name>
MCP_OPENAI_SERVER_MODEL_NAME_RENDERING=<your model name>
MCP_VECTORDB_FILE=<path-to-database-file-including-filename>  
MCP_LLM_TEMPERATURE_RELEVANCE_CHECK=0.0  
MCP_LLM_TEMPERATURE_TRANSLATION=0.0  
MCP_LLM_TEMPERATURE=QUERY_REWRITE=0.4  
MCP_LLM_TEMPERATURE_RENDERING=0.0  
MCP_LLM_TEMPERATURE_INFO=0.7  
MCP_VECTORDB_SIMILARITY_SEARCH_DISTANCE=0.3  
MCP_EXASOL_LOGGER=True
MCP_EXASOL_LOGGER_MODE=EXTENSIVE     # (INFO|DEBUG)
MCP_EXASOL_LOGGER_FILE=<absolute path to your log file>
```
The meaning of these settings should be self-explanatory. Changing the so-called temperatures for the the  
relevance check, translation and rendering should be changed if you know what the consequences are.  
The temperatures for the info messages and the query rewrite can be lowered or increased to your likings,  
however, be cautious with the setting for the query rewrite.  

In general, the temperature defines, are strict the LLM will generate answers. The higher the temperature,   
the more variation you will see.


They secret key and the encrypted password shall be created with the 

```
mcp_exasol_passwords.py
```

tool. For security reasons, keep this tool in a safe place and restrict the access to yourself only!  
  
  
### Large Language Models to consider

For the transformation process, you can select any LLM which is known to code (specifically for SQL)  
with good quality. We have made some good experience with the

- qwen/qwn-coder-30b, quantized down to 4-bit, approx. 18GB of size.

Large Language Model for both transforming into SQL statements and rendering result sets.  
In case you decide to use an AI Desktop where you can configure the LLM to be utilized,  
you need to check, if the LLM is trained for tool usage. The more parameters the LLM features  
the higher is the performance requirement for a timely answer. LLM's with too many parameters might  
consume quite some time. Having a dedicated LLM server (on premise) is definitely a plus.

Also, the desired LLM needs to be trained for tool usage.
  


  
### Please consider!

Large Language Models do not act like a human brain, basically they predict the next possible  
word based on a set of parameters (do not mix it with connections between neurons of the neural   
network, a.k.a. parameters), e.g. temperature. You have to instruct them precisely about  
the task they have to solve. For many AI Desktop applications this is even valid for displaying  
the result set the Text-to-SQL option has created. 

For example, with __Claude Desktop__ or __Open-WebUI__ the following text helped to receive pure  
results without any commentary, or other additional information. Consider it purely optional:  

```
Use only answer from tool to display result. Do not comment!
```

## Examples

Here are a few examples how Text-to-SQL transformed a natural language question into an SQL Statement.  
You need to instruct the LLM precisely with your question and the desired outcome. You have to include  
the name of the database schema you want to use. As of now, Text-to-SQL does not search for an adequate  
database schema when it is missing in the question, or misspelled.

### Simple count

- Show me the number of rows in the SALES_POSITIONS table of the RETAIL database schema, do not comment.


```
SELECT 
   COUNT(*) AS RECORD_COUNT 
FROM 
   RETAIL.SALES_POSITIONS
```
### Date Extractions
- Top three products with regard to revenue for the area of Hessen for the 22nd week of day 2023 in the
  RETAIL database schema, include units sold?

```
SELECT 
   a.DESCRIPTION AS PRODUCT_DESCRIPTION, 
   SUM(sp.AMOUNT) AS UNITS_SOLD, 
   SUM(sp.PRICE) AS REVENUE 
FROM 
   RETAIL.SALES s JOIN RETAIL.SALES_POSITIONS sp ON s.SALES_ID = sp.SALES_ID 
                  JOIN RETAIL.ARTICLE a ON sp.ARTICLE_ID = a.ARTICLE_ID 
                  JOIN RETAIL.MARKETS m ON s.MARKET_ID = m.MARKET_ID 
WHERE 
   m.AREA = 'Hessen' AND 
   YEAR(s.SALES_DATE) = 2023 AND 
   to_char(s.SALES_DATE, 'uW') = '22' 
GROUP BY 
   a.DESCRIPTION 
ORDER BY 
   REVENUE DESC 
LIMIT 3
```

### Higher degree of GROUP BY attributes

- Show the top 5 routes with the most delayed flights between departure city and destination city,  
  include accrued minutes of delay in the list; use the FLIGHTS database schema

```
SELECT 
   F.ORIGIN_CITY_NAME AS ORIGIN_CITY_NAME, 
   F.DEST_CITY_NAME AS DEST_CITY_NAME, 
   COUNT(F.DEP_DELAY) AS DELAY_COUNT, 
   SUM(F.DEP_DELAY) AS TOTAL_DELAY_MINUTES 
FROM 
   FLIGHTS.FLIGHTS F 
WHERE 
   F.DEP_DELAY > 0 
GROUP BY 
   F.ORIGIN_CITY_NAME, F.DEST_CITY_NAME 
ORDER BY 
   DELAY_COUNT DESC LIMIT 5
```

The first query just returns the number of rows. The second and third example return  
a table in Markdown syntax, which is rendered in the client accordingly. As the visual  
result differs between different AI Desktops they are not shown here. In order to retrieve  
consistent visual results you need to set the so-called temperature to '0.0'. For all  
process steps within Text-to-SQL where an LLM is involved the temperature is '0.0'. However,  
when using AI Desktops with publicly used LLMs the final rendering may look differently  
with every single run - this is an indication of a temperature greater than '0.0'.


## Auditing

Auditing can happen in two ways. Search in the log file specified in the ".env" file or by querying  
the internal Vector database from the AI Desktop application with the "SQL History" tool of the MCP-Server.

As an example, the following request:  

```Show me SQL statements about busy routes in Texas include the question and date```

shows the following SQL statement

![img.png](./exasol/ai/mcp/server/text_to_sql_option/images/sql_history.png)

Please beware, the quality of the search results depend on the selected LLM. If you are using a different 
LLM you might have a different experience.


## License

This project is licensed under the MIT License - see the LICENSE file for details.

### Safe Harbor Statement: Exasol MCP Server & AI Solutions

Exasol’s AI solutions (including MCP Server) are designed to enable intelligent,
autonomous, and highly performant access to data through AI and LLM-powered agents.
While these technologies unlock powerful new capabilities, they also introduce
potentially significant risks.

By granting AI agents access to your database, you acknowledge that the behavior of
large language models (LLMs) and autonomous agents cannot be fully predicted or
controlled. These systems may exhibit unintended or unsafe behavior—including but not
limited to hallucinations, susceptibility to adversarial prompts, and the execution of
unforeseen actions. Such behavior may result in data leakage, unauthorized data
generation, or even data modification or deletion.

Exasol provides the tools to build AI-native workflows; however, you, as the implementer
and system owner, assume full responsibility for managing these solutions within your
environment. This includes establishing appropriate governance, authorization controls,
sandboxing mechanisms, and operational guardrails to mitigate risks to your organization,
your customers, and their data.