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Azure SQL MCP Server

by carlosgutierrezch
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Python
Remote

Azure SQL MCP Server

A Model Context Protocol (MCP) server for connecting to Azure SQL databases. This server provides tools to query, explore, and interact with Azure SQL databases through Claude Desktop or any MCP-compatible client.

Features

  • Execute arbitrary SQL queries

  • Browse database tables and schemas

  • Get sample data from tables

  • Configuration validation

  • Azure Active Directory authentication via DefaultAzureCredential

Related MCP server: mcp-mssql-server

Prerequisites

Before starting, ensure you have these installed:

  • Python 3.11+ - Managed via pyenv (recommended) or system Python

  • Node.js 18+ - For MCP Inspector (Download)

  • uv - Fast Python package manager (Install guide)

  • ODBC Driver 17 for SQL Server - (Download)

  • Azure credentials - Azure CLI, VS Code, or Managed Identity

  • npx - Comes with Node.js (used to run MCP Inspector)

Installing Prerequisites

Install Node.js and npx

# Windows: Download from https://nodejs.org/
# Or use winget
winget install OpenJS.NodeJS

# macOS
brew install node

# Linux
curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash -
sudo apt-get install -y nodejs

# Verify installation
node --version
npx --version

Install uv

# Windows (PowerShell)
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Verify installation
uv --version

Install pyenv (Optional but Recommended)

# Windows
# Use pyenv-win: https://github.com/pyenv-win/pyenv-win

# macOS
brew install pyenv

# Linux
curl https://pyenv.run | bash

Installation & Setup

1. Clone the Repository

git clone <your-repo-url>
cd mcp_component

2. Set Python Version (if using pyenv)

# Set global Python version
pyenv global 3.11.4

# Or set local version for this project only
pyenv local 3.11.4

# Verify
python --version

3. Create and Activate Virtual Environment

# Create virtual environment using uv
uv venv

# Activate the virtual environment
# Windows (PowerShell)
.venv\Scripts\activate

# Windows (CMD)
.venv\Scripts\activate.bat

# macOS/Linux
source .venv/bin/activate

4. Install Dependencies

# Option 1: Sync from pyproject.toml (recommended)
uv sync

# Option 2: Install from requirements.txt
uv pip install -r requirements.txt
uv add -r requirements.txt

# Verify installation
uv pip list

Pro Tip: If you need to add new packages later:

# Add a single package
uv add package-name

# Add multiple packages from requirements.txt
uv add -r requirements.txt

5. Configure Environment Variables

Create a .env file in the project root:

# Copy the example
cp .env.example .env

# Or create manually

Edit .env with your Azure SQL credentials:

SERVER_NAME=your-server.database.windows.net
DATABASE=your-database-name

Example:

SERVER_NAME=mycompany-sql.database.windows.net
DATABASE=Northwind

Note: You can test the server without a real database! Leave the default values to test the MCP server functionality. The tools will indicate configuration is needed, but the server will run fine.

6. Authenticate with Azure

Make sure you're authenticated with Azure using one of these methods:

# Option 1: Azure CLI (recommended for local testing)
az login

# Option 2: Use environment variables
# Add to .env file:
# AZURE_TENANT_ID=your-tenant-id
# AZURE_CLIENT_ID=your-client-id
# AZURE_CLIENT_SECRET=your-client-secret

# Option 3: Use VS Code Azure Account extension
# (automatically works if signed in)

Verify Azure authentication:

az account show

Testing with MCP Inspector

The MCP Inspector is an interactive tool for testing your MCP server before connecting it to Claude Desktop.

Install and Run Inspector

# Run the inspector (npx will auto-install it if needed)
npx @modelcontextprotocol/inspector uv --directory . run server.py

What happens:

  1. npx downloads and runs the MCP Inspector (first time only)

  2. Opens automatically in your default browser at http://localhost:5173

  3. Shows all available tools in the left sidebar

  4. Displays request/response JSON for debugging

Inspector Interface Overview

  • Left Panel: List of available tools

  • Center Panel: Tool parameters and execution

  • Right Panel: JSON request/response output

  • Bottom: Server logs and errors (stderr)

Testing Workflow

  1. Start the Inspector (command above)

  2. Check Configuration:

    • Click on check_database_config tool

    • Click "Run Tool"

    • Verify your database settings

  3. Test Tools:

    • Try get_tables to see available tables

    • Use get_table_schema with a table name

    • Execute get_sample_data to preview data

  4. Debug Issues:

    • Check the stderr output for error messages

    • Verify your .env file is correct

    • Ensure Azure authentication is working

Available Tools

1. check_database_config

Check if your database credentials are properly configured.

No parameters required

Example Response:

{
  "configured": true,
  "message": "Configuration OK",
  "server_name": "myserver.database.windows.net",
  "database": "Northwind"
}

2. execute_query

Execute any SQL query against your database.

Parameters:

  • query (string): SQL query to execute

Example:

{
  "query": "SELECT TOP 5 * FROM Customers ORDER BY CompanyName"
}

Response:

{
  "rows": 5,
  "columns": ["CustomerID", "CompanyName", "ContactName"],
  "data": [...]
}

3. get_tables

Get a list of all tables in the database.

No parameters required

Example Response:

{
  "tables": [
    {
      "schema": "dbo",
      "name": "Customers",
      "full_name": "dbo.Customers"
    },
    {
      "schema": "dbo",
      "name": "Orders",
      "full_name": "dbo.Orders"
    }
  ]
}

4. get_table_schema

Get detailed schema information for a specific table.

Parameters:

  • table_name (string): Name of the table (e.g., "Customers" or "dbo.Customers")

Example:

{
  "table_name": "Customers"
}

Response:

{
  "table": "Customers",
  "columns": [
    {
      "COLUMN_NAME": "CustomerID",
      "DATA_TYPE": "nchar",
      "IS_NULLABLE": "NO",
      "COLUMN_DEFAULT": null,
      "CHARACTER_MAXIMUM_LENGTH": 5
    }
  ]
}

5. get_sample_data

Get sample rows from a table.

Parameters:

  • table_name (string): Name of the table

  • limit (integer, optional): Number of rows (default: 5, max: 100)

Example:

{
  "table_name": "Customers",
  "limit": 10
}

Response:

{
  "table": "Customers",
  "sample_rows": 10,
  "total_columns": 11,
  "columns": ["CustomerID", "CompanyName", ...],
  "data": [...]
}

Using with Claude Desktop

Once you've tested with the Inspector, connect to Claude Desktop:

1. Locate Claude Desktop Config

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

2. Add Server Configuration

Edit or create the config file:

Windows:

{
  "mcpServers": {
    "azure-sql": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\Users\\YourUsername\\path\\to\\mcp_component",
        "run",
        "server.py"
      ]
    }
  }
}

macOS/Linux:

{
  "mcpServers": {
    "azure-sql": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/yourname/projects/mcp_component",
        "run",
        "server.py"
      ]
    }
  }
}

Important: Replace the path with your actual absolute project path!

3. Restart Claude Desktop

After saving the config:

  1. Completely quit Claude Desktop (not just close the window)

  2. Restart the application

  3. Look for the 🔌 plug icon in the interface

  4. Click it to verify "azure-sql" server is connected (green indicator)

4. Test with Claude

Try asking Claude:

  • "Can you check if my database is configured?"

  • "What tables are available in my database?"

  • "Show me the schema for the Customers table"

  • "Get me 5 sample rows from the Orders table"

  • "Execute this query: SELECT COUNT(*) FROM Products"

Project Structure

mcp_component/
├── .env                      # Environment variables (create this - not in git)
├── .env.example             # Example environment file
├── .gitignore               # Git ignore rules
├── .python-version          # Python version for pyenv
├── pyproject.toml           # Project metadata and dependencies
├── requirements.txt         # Python dependencies
├── uv.lock                  # Dependency lock file
├── server.py                # Main entry point wrapper
├── mcp_server/
│   ├── __init__.py
│   └── server.py           # MCP server implementation
├── README.md               # This file
└── .venv/                  # Virtual environment (created by uv venv)

Troubleshooting

"npx: command not found"

Cause: Node.js/npm is not installed or not in PATH

Solution:

# Verify Node.js installation
node --version
npm --version

# If not installed, install Node.js from https://nodejs.org/
# Or use a package manager (see Prerequisites section)

"uv: command not found"

Cause: uv is not installed or not in PATH

Solution:

# Windows (PowerShell as Administrator)
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Then restart your terminal
# Verify
uv --version

Inspector shows "error" notification

Cause: Server crashed during startup or has import errors

Solution:

  1. Check the stderr output in the Inspector console for detailed error messages

  2. Common issues:

    • Python dependencies not installed: Run uv sync

    • Wrong Python version: Run python --version (need 3.11+)

    • Import errors: Make sure you're running from the project root

    • Azure auth issues: Run az login

"Database not configured" error

Expected behavior if you haven't set up your .env file with real credentials. The server will still run—you just won't be able to query data until you configure it.

Solution: Update .env with your actual Azure SQL Server details:

SERVER_NAME=your-actual-server.database.windows.net
DATABASE=your-actual-database-name

Connection timeout or authentication errors

Solutions:

  1. Firewall: Verify your Azure SQL firewall allows your IP address

    # Check current IP
curl https://api.ipify.org
# Add it to Azure SQL firewall rules in Azure Portal

  2. Authentication: Run az login to authenticate

    az login
az account show

  3. Permissions: Check that your Azure account has access to the database

    az sql db show --resource-group <rg-name> --server <server-name> --name <db-name>

  4. ODBC Driver: Verify ODBC Driver 17 is installed

    # Windows - Check installed ODBC drivers
Get-OdbcDriver

# Should show "ODBC Driver 17 for SQL Server"

Tools not appearing in Claude Desktop

Solutions:

  1. Config path: Double-check the config file is in the correct location:

    # Windows - Open config directory
explorer %APPDATA%\Claude

# Verify claude_desktop_config.json exists

  2. Absolute path: Ensure the project path in args is absolute and correct

    // ❌ Wrong - relative path
"C:\\mcp_component"

// ✅ Correct - absolute path
"C:\\Users\\YourName\\Projects\\mcp_component"

  3. Restart properly: Completely quit Claude Desktop (not just minimize)

    • Windows: Right-click system tray icon → Exit

    • macOS: Cmd+Q or Claude → Quit Claude

  4. Check logs: Look for errors in Claude Desktop logs:

    • Windows: %APPDATA%\Claude\logs

    • macOS: ~/Library/Logs/Claude

  5. Test with Inspector first: Verify the server works:

    npx @modelcontextprotocol/inspector uv --directory . run server.py

"ODBC Driver not found"

Solution: Install ODBC Driver 17 for SQL Server:

Windows:

# Download and run installer from:
# https://go.microsoft.com/fwlink/?linkid=2249004

# Verify installation
Get-OdbcDriver | Where-Object {$_.Name -like "*SQL Server*"}

macOS:

brew tap microsoft/mssql-release https://github.com/Microsoft/homebrew-mssql-release
brew update
brew install msodbcsql17

# Verify
odbcinst -q -d

Linux (Ubuntu/Debian):

curl https://packages.microsoft.com/keys/microsoft.asc | sudo apt-key add -
curl https://packages.microsoft.com/config/ubuntu/$(lsb_release -rs)/prod.list | sudo tee /etc/apt/sources.list.d/mssql-release.list
sudo apt-get update
sudo ACCEPT_EULA=Y apt-get install -y msodbcsql17

# Verify
odbcinst -q -d

"Module not found" errors

Solution:

# Ensure virtual environment is activated
# Windows
.venv\Scripts\activate

# macOS/Linux
source .venv/bin/activate

# Reinstall dependencies
uv sync

# Or
uv pip install -r requirements.txt

Development

Adding Custom Tools

You can add your own tools to mcp_server/server.py:

@mcp.tool()
async def my_custom_tool(param: str) -> str:
    """Description of what this tool does
    
    Args:
        param: Description of the parameter
    """
    try:
        engine = await get_azure_engine()
        
        # Your custom logic here
        query = text("SELECT * FROM MyTable WHERE column = :param")
        
        with engine.connect() as connection:
            result = connection.execute(query, {"param": param})
            data = [dict(row) for row in result]
        
        return json.dumps({"result": data}, indent=2)
        
    except Exception as e:
        return json.dumps({"error": str(e)}, indent=2)

Best Practices:

  • Always wrap tools in try/except

  • Return JSON strings (use json.dumps())

  • Use parameterized queries to prevent SQL injection

  • Add descriptive docstrings for Claude to understand the tool

  • Test new tools in the Inspector before using in Claude Desktop

Updating Dependencies

# Add a new package
uv add package-name

# Update all packages
uv sync --upgrade

# Remove a package
uv remove package-name

Authentication Details

This server uses DefaultAzureCredential from Azure Identity SDK, which tries multiple authentication methods in order:

  1. Environment variables - AZURE_TENANT_ID, AZURE_CLIENT_ID, AZURE_CLIENT_SECRET

  2. Managed Identity - Works automatically in Azure services (App Service, Functions, VMs)

  3. Azure CLI - Uses az login credentials (✅ recommended for local development)

  4. Visual Studio Code - Uses VS Code Azure Account extension

  5. Interactive browser - Falls back to browser login

For local testing (recommended):

az login

For production (recommended):

  • Use Managed Identity in Azure services

  • Or use Service Principal with environment variables

Contributing

Contributions are welcome! Please:

  1. Fork the repository

  2. Create a feature branch (git checkout -b feature/XXX-XXX)

  3. Make your changes

  4. Test with the MCP Inspector

  5. Commit your changes (git commit -m 'Add XXXX feature')

  6. Push to the branch (git push origin feature/XXX-XXX)

  7. Open a Pull Request

Resources

Available Tools

5 tools
check_database_configA

Check if the database is properly configured.

Returns configuration status and any error messages.
ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully cover behavioral traits. It only states that it returns configuration status and errors, but does not mention whether it is read-only, modifies state, or requires any permissions. This is insufficient for a tool with no annotations.

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 two sentences, front-loaded, and contains no unnecessary information. Every sentence serves a purpose.

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?

Given the tool's simplicity (no parameters, output schema exists), the description is mostly complete. It mentions the return type (status and errors). However, it could explicitly state that it does not modify any state, which would improve completeness for an agent.

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?

There are no parameters, so schema coverage is 100%. The description adds no parameter info, but with zero parameters, baseline 4 is appropriate as there is nothing to add.

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 clearly states the tool checks database configuration, which is a specific verb+resource combination. It is distinctly different from sibling tools like execute_query and get_tables, which focus on data operations rather than configuration.

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?

No explicit guidance on when to use versus siblings. The context implies it might be used prior to queries to ensure configuration, but no clear when-not or alternative recommendations are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

execute_queryC

Execute a SQL query against the Azure SQL database.

Args:
    query: SQL query to execute (e.g., "SELECT * FROM users LIMIT 10")
ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description must disclose behavioral traits. It only mentions execution against Azure SQL but does not address potential side effects (data modification, schema changes), performance impact, required permissions, or error states. This is insufficient for a tool that can alter data.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise (two sentences) with front-loaded purpose. Every word earns its place given the single parameter. However, the brevity sacrifices important behavioral context, making it feel under-specified rather than efficiently concise.

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?

Given the tool's complexity (arbitrary SQL execution) and lack of annotations, the description is incomplete. It does not cover allowed operations, query safety, or connections to sibling tools. The existence of an output schema justifies no return-value explanations, but other gaps remain critical.

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?

With 0% schema description coverage, the description must add meaning. It provides an example query and states the parameter is a SQL query. This clarifies the parameter's intent beyond the raw schema, but lacks details on syntax rules, limits, or escape characters. Barely meets minimal expectations.

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 description clearly states the tool's action ('Execute a SQL query') and target resource ('Azure SQL database'). It distinguishes from sibling tools (check_database_config, get_tables, etc.) by its verb and scope. However, it doesn't specify allowed query types (e.g., SELECT only, DDL, DML), which could cause agent confusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like get_sample_data or get_table_schema. Missing exclusions such as 'do not use for schema inspection' or warnings about destructive queries. The example suggests a SELECT, but the description does not restrict usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_sample_dataA

Get sample data from a table.

Args:
    table_name: Name of the table (e.g., "users" or "schema.table_name")
    limit: Number of rows to return (default: 5, max: 100)
ParametersJSON Schema
NameRequiredDescriptionDefault
limitNo
table_nameYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.5/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, and the description does not disclose read-only nature, error handling for missing tables, or any side effects. It only describes parameters.

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?

Minimal and well-structured: purpose in first sentence, parameter docs follow. Every sentence adds value.

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?

Adequate for a simple tool with output schema, but lacks usage guidelines and behavioral details that would make it fully self-contained.

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?

The description adds concrete examples and constraints for both parameters (e.g., table_name format, limit defaults and max), compensating for the schema's 0% description coverage.

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 clearly states the verb 'Get' and resource 'sample data from a table', which distinguishes it from sibling tools like execute_query or get_table_schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit guidance on when to use this tool versus alternatives, nor any context about when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_tablesA

Get list of all tables in the Azure SQL database.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.1/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description bears the full burden. It implies a read-only listing but does not explicitly state safety, performance, or side effects. Adequate for a simple list operation.

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?

Single sentence with no extraneous words. Front-loaded and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameter-free list tool with an output schema (not shown), the description sufficiently covers functionality. No gaps given simplicity.

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?

No parameters, so schema coverage is 100%. The description adds no parameter info, which is acceptable. Baseline score of 4 for zero-parameter tools.

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 clearly states the verb 'Get list of all tables' and the resource 'Azure SQL database', which is distinct from sibling tools that focus on specific tables, schemas, data, or queries.

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?

No explicit guidance on when to use this tool vs alternatives, but the scope is obvious (listing all tables). No exclusions or context provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_table_schemaB

Get schema information for a specific table.

Args:
    table_name: Name of the table (e.g., "users" or "schema.table_name")
ParametersJSON Schema
NameRequiredDescriptionDefault
table_nameYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It only mentions 'schema information' without specifying what that includes (e.g., columns, types, constraints) or whether it is read-only. This is a significant gap for a tool that retrieves database metadata.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded with the main purpose. The structure with 'Args' is clear, but it could be more concise by removing the 'Args' header if not needed. Overall, every sentence earns its place.

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?

Given the tool's simplicity (one parameter) and presence of an output schema, the description is adequate but not complete. It does not clarify what the output schema contains, which is important for an AI agent to interpret results. More detail would improve completeness.

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?

The input schema has no description (0% coverage), so the description adds value by explaining the 'table_name' parameter with an example. However, it could provide more detail on valid formats or restrictions beyond the example.

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 clearly states the action ('Get schema information') and the resource ('a specific table'), making the purpose unambiguous. It naturally distinguishes from sibling tools like 'get_tables' which likely lists table names, while this tool retrieves schema details.

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 does not provide explicit guidance on when to use this tool versus alternatives. While the purpose is clear, there is no mention of when to prefer this over 'execute_query' or 'get_tables', or under what conditions it should be used.

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. Dates show when Glama detected each change.

  1. 5 tool updatesv0.1.0
    • First observedcheck_database_config
    • First observedexecute_query
    • First observedget_sample_data
    • First observedget_table_schema
    • First observedget_tables
Install Server
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TDQS

A3.7/5.0
Disambiguation5/5

Each tool serves a distinct function: config check, arbitrary query execution, listing tables, getting schema, and retrieving sample data. No two tools overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (check_database_config, execute_query, get_sample_data, get_tables, get_table_schema), making the set predictable.

Tool Count5/5

Five tools cover essential database introspection and querying capabilities without unnecessary clutter, appropriate for a focused Azure SQL server.

Completeness4/5

Core operations are covered, but dedicated tools for data manipulation (insert/update/delete) or schema modification are missing, though these can be performed via execute_query. Minor gap.

Maintenance

ActivityInactive
ResponsivenessNo issues

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