Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Microsoft SQL Server MCP Server (MSSQL) show me the top 5 customers by total sales this month"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An easy-to-use bridge that lets AI assistants like Claude directly query and explore Microsoft SQL Server databases. No coding experience required!
This tool allows AI assistants to:
Discover tables in your SQL Server database
View table structures (columns, data types, etc.)
Execute read-only SQL queries safely
Generate SQL queries from natural language requests
Related MCP server: MS SQL MCP Server
No Coding Required: Give Claude and other AI assistants direct access to your SQL Server databases without writing complex integration code
Maintain Control: All queries are read-only by default, ensuring your data remains safe
Private & Secure: Your database credentials stay local and are never sent to external services
Save Hours of Manual Work: No more copy-pasting data or query results to share with AI
Deeper Analysis: AI can navigate your entire database schema and provide insights across multiple tables
Natural Language Interface: Ask questions about your data in plain English
End the Context Limit Problem: Access large datasets that would exceed normal AI context windows
Data Analysts who want AI help interpreting SQL data without sharing credentials
Developers looking for a quick way to explore database structure through natural conversation
Business Analysts who need insights without SQL expertise
Database Administrators who want to provide controlled access to AI tools
Install Node.js (version 14 or higher)
Have access to a Microsoft SQL Server database (on-premises or Azure)
# Clone this repository
git clone https://github.com/dperussina/mssql-mcp-server.git
# Navigate to the project directory
cd mssql-mcp-server
# Install dependencies
npm install
# Copy the example environment file
cp .env.example .envEdit the .env file with your database credentials:
DB_USER=your_username
DB_PASSWORD=your_password
DB_SERVER=your_server_name_or_ip
DB_DATABASE=your_database_name
PORT=3333
HOST=0.0.0.0 # Host for the server to listen on, e.g., 'localhost' or '0.0.0.0'
TRANSPORT=stdio
SERVER_URL=http://localhost:3333
DEBUG=false # Set to 'true' for detailed logging (helpful for troubleshooting)
QUERY_RESULTS_PATH=/path/to/query_results # Directory where query results will be saved as JSON files# Start with default stdio transport
npm start
# OR start with HTTP/SSE transport for network access
npm run start:sse# Run the interactive client
npm run clientExplore your database structure without writing SQL
mcp_SQL_mcp_discover_database()Get detailed information about a specific table
mcp_SQL_mcp_table_details({ tableName: "Customers" })Run a safe query
mcp_SQL_mcp_execute_query({ sql: "SELECT TOP 10 * FROM Customers", returnResults: true })Find tables by name pattern
mcp_SQL_mcp_discover_tables({ namePattern: "%user%" })Use pagination to navigate large result sets
// First page
mcp_SQL_mcp_execute_query({
sql: "SELECT * FROM Users ORDER BY Username OFFSET 0 ROWS FETCH NEXT 10 ROWS ONLY",
returnResults: true
})
// Next page
mcp_SQL_mcp_execute_query({
sql: "SELECT * FROM Users ORDER BY Username OFFSET 10 ROWS FETCH NEXT 10 ROWS ONLY",
returnResults: true
})Cursor-based pagination for optimal performance
// First page
mcp_SQL_mcp_execute_query({
sql: "SELECT TOP 10 * FROM Users ORDER BY Username",
returnResults: true
})
// Next page using the last value as cursor
mcp_SQL_mcp_execute_query({
sql: "SELECT TOP 10 * FROM Users WHERE Username > 'last_username' ORDER BY Username",
returnResults: true
})Ask natural language questions
"Show me the top 5 customers with the most orders in the last month"Sales Performance Analysis: "Show me monthly sales trends for the past year and identify our top-performing products by region."
Customer Segmentation: "Analyze our customer base by purchase frequency, average order value, and geographical location."
Financial Reporting: "Create a quarterly profit and loss report comparing this year to last year."
Schema Optimization: "Help me identify tables with missing indexes by examining query performance data."
Data Quality Auditing: "Find all customer records with incomplete information or invalid values."
Usage Analysis: "Show me which tables are most frequently accessed and what queries are most resource-intensive."
API Exploration: "I'm building an API - help me analyze the database schema to design appropriate endpoints."
Query Optimization: "Review this complex query and suggest performance improvements."
Database Documentation: "Create comprehensive documentation of our database structure with explanations of relationships."
The bundled client provides an easy menu-driven interface:
List available resources - See what information is available
List available tools - See what actions you can perform
Execute SQL query - Run a read-only SQL query
Get table details - View structure of any table
Read database schema - See all tables and their relationships
Generate SQL query - Convert natural language to SQL
When working with Claude or other AI assistants through this MCP server, the way you phrase your requests significantly impacts the results. Here's how to help the AI use the database tools effectively:
When prompting an AI to use this tool, follow this structure:
Can you use the SQL MCP tools to [your goal]?
For example:
- Check what tables exist in my database
- Query the Customers table and show me the first 10 records
- Find all orders from the past monthHere are the main tools and their correct syntax:
// Discover the database structure
mcp_SQL_mcp_discover_database()
// Get detailed information about a specific table
mcp_SQL_mcp_table_details({ tableName: "YourTableName" })
// Execute a query and return results
mcp_SQL_mcp_execute_query({
sql: "SELECT * FROM YourTable WHERE Condition",
returnResults: true
})
// Find tables by name pattern
mcp_SQL_mcp_discover_tables({ namePattern: "%pattern%" })
// Access saved query results (for large result sets)
mcp_SQL_mcp_get_query_results({ uuid: "provided-uuid-here" })When to use each tool:
Database Discovery: Start with this when the AI is unfamiliar with your database structure.
Table Details: Use when focusing on a specific table before writing queries.
Query Execution: When you need to retrieve or analyze actual data.
Table Discovery by Pattern: When looking for tables related to a specific domain.
For complex tasks, guide the AI through a series of steps:
I'd like to analyze our sales data. Please:
1. First use mcp_SQL_mcp_discover_tables to find tables related to sales
2. Use mcp_SQL_mcp_table_details to examine the structure of relevant tables
3. Create a query with mcp_SQL_mcp_execute_query that shows monthly sales by product categoryFirst, discover what tables exist in my database. Then, look at the structure
of the Customers table. Finally, show me the top 10 customers by total purchase amount.Query the top 5 underperforming products based on sales vs. forecasts,
and explain your approach to writing this query.Remind the AI about SQL Server's specific syntax:
Please use SQL Server syntax for pagination:
- For offset/fetch: "OFFSET 10 ROWS FETCH NEXT 10 ROWS ONLY"
- For cursor-based: "WHERE ID > last_id ORDER BY ID"If the AI uses incorrect syntax, you can help it with:
That's not quite right. Please use this format for the tool call:
mcp_SQL_mcp_execute_query({
sql: "SELECT * FROM Customers WHERE Region = 'West'",
returnResults: true
})If the AI is struggling with a database task, try these approaches:
Be more specific about tables: "Before writing that query, please check if the CustomerOrders table exists and what columns it has."
Break complex tasks into steps: "Let's approach this step by step. First, look at the Products table structure. Then, check the Orders table..."
Ask for intermediate results: "Run a simple query on that table first so we can verify the data format before trying more complex analysis."
Request query explanations: "After writing this query, explain what each part does so I can verify it's doing what I need."
The MCP Server provides powerful tools for exploring your database structure:
Pattern-based table discovery: Find tables matching specific patterns
mcp_SQL_mcp_discover_tables({ namePattern: "%order%" })Schema overview: Get a high-level view of tables by schema
mcp_SQL_mcp_execute_query({
sql: "SELECT TABLE_SCHEMA, COUNT(*) AS TableCount FROM INFORMATION_SCHEMA.TABLES GROUP BY TABLE_SCHEMA"
})Column exploration: Examine column metadata for any table
mcp_SQL_mcp_table_details({ tableName: "dbo.Users" })The server supports multiple pagination methods for handling large datasets:
Offset/Fetch Pagination: Standard SQL pagination using OFFSET and FETCH
mcp_SQL_mcp_execute_query({
sql: "SELECT * FROM Users ORDER BY Username OFFSET 0 ROWS FETCH NEXT 10 ROWS ONLY"
})Cursor-Based Pagination: More efficient for large datasets
// Get first page
mcp_SQL_mcp_execute_query({
sql: "SELECT TOP 10 * FROM Users ORDER BY Username"
})
// Get next page using last value as cursor
mcp_SQL_mcp_execute_query({
sql: "SELECT TOP 10 * FROM Users WHERE Username > 'last_username' ORDER BY Username"
})Count with Data: Retrieve total count alongside paginated data
mcp_SQL_mcp_execute_query({
sql: "WITH TotalCount AS (SELECT COUNT(*) AS Total FROM Users) SELECT TOP 10 u.*, t.Total FROM Users u CROSS JOIN TotalCount t ORDER BY Username"
})Explore relationships between tables with join operations:
mcp_SQL_mcp_execute_query({
sql: "SELECT u.Username, u.Email, r.RoleName FROM Users u JOIN UserRoles ur ON u.Username = ur.Username JOIN Roles r ON ur.RoleId = r.RoleId ORDER BY u.Username"
})Run aggregations and analytical queries to gain insights:
mcp_SQL_mcp_execute_query({
sql: "SELECT UserType, COUNT(*) AS UserCount, SUM(CASE WHEN IsActive = 1 THEN 1 ELSE 0 END) AS ActiveUsers FROM Users GROUP BY UserType"
})The MCP server supports SQL Server-specific features:
Common Table Expressions (CTEs)
Window functions
JSON operations
Hierarchical queries
Full-text search (when configured in your database)
Connect this tool directly to Claude Desktop in a few easy steps:
Install Claude Desktop from anthropic.com
Edit Claude's configuration file:
Location: ~/Library/Application Support/Claude/claude_desktop_config.json
Add this configuration:
{
"mcpServers": {
"mssql": {
"command": "node",
"args": [
"/FULL/PATH/TO/mssql-mcp-server/server.mjs"
]
}
}
}Replace /FULL/PATH/TO/ with the actual path to where you cloned this repository
Restart Claude Desktop
Look for the tools icon in Claude Desktop - you can now use database commands directly!
Cursor is an AI-powered code editor that can leverage this tool for advanced database interactions. Here's how to set it up:
Open Cursor IDE (download from cursor.sh if you don't have it)
Start the MS SQL MCP Server using the HTTP/SSE transport:
npm run start:sseCreate a new workspace or open an existing project in Cursor
Enter Cursor Settings
Click MCP
Add new MCP server
Name your MCP server, select type: sse
Enter server URL as: localhost:3333/sse (or the port you have it running on)
Once connected, you can use MCP commands directly in Cursor's AI chat:
Ask Claude in Cursor to explore your database:
Can you show me the tables in my database?Execute specific queries:
Query the top 10 records from the Customers tableGenerate and run complex queries:
Find all orders from the last month with a value over $1000Make sure the MS SQL MCP Server is running with the HTTP/SSE transport
Check that the port is correct and matches what's in your .env file
Ensure your firewall isn't blocking the connection
If using a different IP/hostname, update the SERVER_URL in your .env file
Best for: Using directly with Claude Desktop or the bundled client
npm startBest for: Network access or when used with web applications
npm run start:sseRead-only by default: No risk of data modification
Private credentials: Database connection details stay in your .env file
SQL injection protection: Built-in validation for SQL queries
Check your .env file for correct database credentials
Make sure your SQL Server is running and accepting connections
For Azure SQL, verify your IP is allowed in the firewall settings
Run npm install again to ensure all dependencies are installed
Make sure you're using Node.js version 14 or higher
For HTTP/SSE transport, verify the PORT in your .env is available
Make sure no firewall is blocking the connection
Double-check the path in your claude_desktop_config.json
Ensure you're using absolute paths, not relative ones
Restart Claude Desktop completely after making changes
If you're new to SQL Server, here are some key concepts:
Tables: Store your data in rows and columns
Schemas: Logical groupings of tables (like folders)
Queries: Commands to retrieve or analyze data
Views: Pre-defined queries saved for easy access
This tool helps you explore all of these without needing to be a SQL expert!
The MS SQL MCP Server is built with a modular architecture that separates concerns for maintainability and extensibility:
database.mjs - Database ConnectivityManages SQL Server connection pooling
Provides query execution with retry logic and error handling
Handles database connections, transactions, and configuration
Includes utilities for sanitizing SQL and formatting errors
tools.mjs - Tool RegistrationRegisters all database tools with the MCP server
Implements tool validation and parameter checking
Provides core functionality for SQL queries, table exploration, and database discovery
Maps tool calls to database operations
resources.mjs - Database ResourcesExposes database metadata through resource endpoints
Provides schema information, table listings, and procedure documentation
Formats database structure information for AI consumption
Includes discovery utilities for database exploration
pagination.mjs - Results NavigationImplements cursor-based pagination for large result sets
Provides utilities for generating next/previous page cursors
Transforms SQL queries to support pagination
Handles SQL Server's OFFSET/FETCH pagination syntax
errors.mjs - Error HandlingDefines custom error types for different failure scenarios
Implements JSON-RPC error formatting
Provides human-readable error messages
Includes middleware for global error handling
logger.mjs - Logging SystemConfigures Winston logging with multiple transports
Provides context-aware request logging
Handles log rotation and formatting
Captures uncaught exceptions and unhandled rejections
When a tool call is received, the MCP server routes it to the appropriate handler in tools.mjs
The tool handler validates parameters and constructs a database query
The query is executed via functions in database.mjs, with possible pagination from pagination.mjs
Results are formatted and returned to the client
Any errors are caught and processed through errors.mjs
All operations are logged via logger.mjs
This architecture ensures:
Clean separation of concerns
Consistent error handling
Comprehensive logging
Efficient database connection management
Scalable query execution
The .env file controls how the MS SQL MCP Server connects to your database and operates. Here's a detailed explanation of each setting:
# Database Connection Settings
DB_USER=your_username # SQL Server username
DB_PASSWORD=your_password # SQL Server password
DB_SERVER=your_server_name_or_ip
DB_DATABASE=your_database_name
# Server Configuration
PORT=3333 # Port for the HTTP/SSE server to listen on
HOST=0.0.0.0 # Host for the server to listen on, e.g., 'localhost' or '0.0.0.0'
TRANSPORT=stdio # Connection method: 'stdio' (for Claude Desktop) or 'sse' (for network connections)
SERVER_URL=http://localhost:3333 # Base URL when using SSE transport. If HOST is '0.0.0.0', external clients use http://<your-machine-ip>:${PORT}
# Advanced Settings
DEBUG=false # Set to 'true' for detailed logging (helpful for troubleshooting)
QUERY_RESULTS_PATH=/path/to/query_results # Directory where query results will be saved as JSON filesUse when connecting directly with Claude Desktop
Communication happens through standard input/output streams
Set TRANSPORT=stdio in your .env file
Run with npm start
Use when connecting over a network (like with Cursor IDE)
Uses Server-Sent Events (SSE) for real-time communication
Set TRANSPORT=sse in your .env file
Configure SERVER_URL to match your server address
Run with npm run start:sse
DB_USER=sa
DB_PASSWORD=YourStrongPassword
DB_SERVER=localhost
DB_DATABASE=AdventureWorksDB_USER=azure_admin@myserver
DB_PASSWORD=YourStrongPassword
DB_SERVER=myserver.database.windows.net
DB_DATABASE=AdventureWorksQuery results are saved as JSON files in the directory specified by QUERY_RESULTS_PATH. This prevents large result sets from overwhelming the conversation. You can:
Leave this blank to use the default query-results directory in the project
Set a custom path like /Users/username/Documents/query-results
Access saved results using the provided UUID in the tool response
ISC
Extract and analyze business rules from check constraints
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| tableName | No | Filter by specific table name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'extract and analyze,' which implies a read-only operation, but does not specify if it requires specific permissions, how it handles errors, or what the output format looks like (e.g., structured data or summary). For a tool with no annotations, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Extract and analyze business rules from check constraints.' It is front-loaded with the core purpose, has no unnecessary words, and earns its place by clearly stating what the tool does without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (analysis of constraints), lack of annotations, and no output schema, the description is somewhat incomplete. It covers the basic purpose but does not address behavioral aspects like error handling, output format, or performance considerations. However, it is adequate as a starting point, though more detail would improve completeness for an analysis tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning all parameters are documented in the schema with clear descriptions (e.g., 'SQL Server connection string'). The description does not add any additional meaning or context beyond the schema, such as explaining interactions between parameters or default behaviors. Baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Extract and analyze business rules from check constraints.' It specifies both the action ('extract and analyze') and the resource ('business rules from check constraints'), making it easy to understand. However, it does not explicitly differentiate from sibling tools like 'list_constraints' or 'list_default_constraints,' which might also involve constraints, so it misses full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any context, prerequisites, or exclusions, such as when to prefer 'list_constraints' for a simple listing or 'analyze_database_size' for different analysis types. Without such guidance, users must infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get comprehensive database size information including data and log file sizes
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. While it states what information is returned ('database size information including data and log file sizes'), it doesn't disclose important behavioral aspects like whether this is a read-only operation, potential performance impact on the database, authentication requirements, rate limits, or what format the information is returned in.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that clearly states the tool's purpose without any wasted words. It's appropriately sized for a straightforward analysis tool and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a database analysis tool with no annotations and no output schema, the description is insufficient. It doesn't explain what format the size information is returned in, whether the operation has side effects, authentication requirements, or how it differs from other database inspection tools. Given the complexity of database operations and lack of structured metadata, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 ('database size information') with specific scope ('including data and log file sizes'). It distinguishes itself from many sibling tools that analyze other database aspects like constraints, indexes, or procedures, though it doesn't explicitly differentiate from tools like 'list_databases' which might provide different size information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools available (e.g., 'list_databases', 'analyze_table_stats', 'describe_table'), there's no indication of when this specific size analysis is preferred over other database inspection tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get data distribution patterns for columns to understand data quality and patterns
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| tableName | Yes | Name of the table to analyze | |
| schema | No | Schema name (default: dbo) | |
| columnName | No | Specific column to analyze (analyzes all if not provided) | |
| sampleSize | No | Sample size for analysis (default: 1000) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions analyzing 'data distribution patterns' but fails to describe key behaviors: what the output looks like (e.g., statistical summaries, visualizations), whether it performs read-only operations (implied but not stated), performance implications, or any limitations like data size constraints. This is inadequate for a tool with 6 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action ('Get data distribution patterns'), making it easy to parse. However, it could be slightly more structured by explicitly mentioning the target (e.g., SQL databases) to enhance clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output format, and usage context. While the schema covers parameters, the description fails to compensate for missing annotations and output schema, leaving gaps in understanding how the tool behaves and what results to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema fully documents all 6 parameters with descriptions. The tool description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain how 'sampleSize' affects analysis quality). According to scoring rules, this results in a baseline score of 3, as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get data distribution patterns for columns to understand data quality and patterns.' It specifies the verb ('Get') and resource ('data distribution patterns for columns'), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'analyze_table_stats' or 'sample_data', which might have overlapping analysis functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'analyze_table_stats' or 'sample_data' that might serve similar purposes, nor does it specify prerequisites or contexts for use. This leaves the agent without clear direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show detailed index usage statistics to identify unused or underutilized indexes
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| tableName | No | Filter by specific table name | |
| showUnusedOnly | No | Show only unused indexes (default: false) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. While it mentions the tool shows 'detailed index usage statistics', it doesn't describe what format the output takes, whether it's read-only, whether it requires specific permissions, or any performance implications. For a tool with 5 parameters and no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that clearly states the tool's purpose. It's appropriately sized for a tool with good schema documentation, with zero wasted words or redundant information. The structure is front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't address what the output looks like, how results are formatted, whether there are performance considerations for large databases, or what permissions are required. For a database analysis tool with multiple configuration options, more contextual information would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 5 parameters. The description adds no additional parameter information beyond what's in the schema - it doesn't explain relationships between parameters (e.g., connectionString vs connectionName), parameter dependencies, or usage patterns. Baseline 3 is appropriate when schema does all the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Show detailed index usage statistics to identify unused or underutilized indexes'. It specifies the verb 'show' and resource 'index usage statistics', with a clear goal of identifying unused/underutilized indexes. However, it doesn't explicitly differentiate from sibling tools like 'list_indexes' or 'find_missing_indexes', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools focused on database analysis (e.g., 'analyze_table_stats', 'find_missing_indexes'), there's no indication of when this specific index analysis tool is appropriate versus other analysis or listing tools. The description only states what it does, not when to choose it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find columns with high null percentages and analyze null patterns
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| minNullPercentage | No | Minimum null percentage to include (default: 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'find columns with high null percentages' and 'analyze null patterns,' but doesn't specify what constitutes 'high' (though the schema covers minNullPercentage), how results are returned, whether it's read-only, performance implications, or authentication needs. For a tool with 4 parameters and no annotations, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Find columns with high null percentages and analyze null patterns.' It's front-loaded with the core purpose and wastes no words. However, it could be slightly more structured by separating key actions, but overall it's concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, no output schema), the description is incomplete. It doesn't cover behavioral aspects like read-only nature, output format, or error handling. For a database analysis tool with siblings, more context is needed to help the agent understand when and how to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 4 parameters. The description adds no parameter-specific semantics beyond implying null percentage analysis. It doesn't explain interactions between parameters (e.g., connectionString vs. connectionName) or provide context beyond what the schema already states. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Find columns with high null percentages and analyze null patterns.' It specifies the verb ('find' and 'analyze') and resource ('columns'), but doesn't explicitly differentiate from siblings like 'analyze_data_distribution' or 'describe_table' which might also involve column analysis. The purpose is clear but lacks sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., data quality assessment), or exclusions. With many sibling tools for database analysis, the lack of usage guidelines leaves the agent guessing about appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get table row counts, size information, and last update statistics
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| tableName | No | Filter by specific table name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get' implies a read-only operation, it doesn't specify whether this requires specific permissions, what happens with large tables, whether results are cached, or what format the output takes. For a tool with no annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise - a single sentence that efficiently communicates the core functionality without any wasted words. It's front-loaded with the essential information and doesn't include unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only analysis tool with 4 well-documented parameters but no output schema, the description provides adequate basic context about what statistics are retrieved. However, it doesn't address important contextual aspects like performance implications for large tables, output format, or how it differs from similar sibling tools, leaving some gaps in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so all parameters are well-documented in the schema itself. The description doesn't add any meaningful parameter semantics beyond what's already in the schema (like explaining how parameters interact or providing examples). This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get table row counts, size information, and last update statistics'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'describe_table' or 'analyze_database_size', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools like 'describe_table', 'analyze_database_size', and 'list_tables', there's no indication of when this specific statistical analysis tool is preferred or what distinguishes it from other analysis tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed information about a specific stored procedure including parameters and definition
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| procedureName | Yes | Name of the stored procedure to describe | |
| schema | No | Schema name (default: dbo) | |
| includeDefinition | No | Include the procedure definition/body (default: true) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'detailed information' but doesn't disclose behavioral traits like what specific details are returned (e.g., parameter types, return values, permissions), whether it's a read-only operation, error handling, or performance implications. The description is too vague to guide an agent effectively beyond basic intent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It avoids unnecessary words and directly states the action and key details, though it could be slightly more structured by explicitly separating scope from output details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool with 5 parameters and complex database interactions. It lacks details on return format, error conditions, authentication needs, or how it differs from similar tools. For a read operation in a database context, more behavioral context is needed to ensure correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by implying that 'parameters and definition' are part of the output, which loosely relates to the 'includeDefinition' parameter but doesn't provide additional semantics beyond what the schema specifies. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 'detailed information about a specific stored procedure', including specific details like 'parameters and definition'. It distinguishes from siblings like 'list_stored_procedures' (which lists names) and 'get_stored_procedure_definition' (which might only return the definition), but doesn't explicitly name these alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention siblings like 'get_stored_procedure_definition' or 'get_all_stored_procedure_definitions', nor does it specify prerequisites such as needing a connection or schema context. The description assumes context without stating it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed schema information for a specific table including columns, data types, and constraints
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| tableName | Yes | Name of the table to describe | |
| schema | No | Schema name (default: dbo) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes a read operation ('Get'), but lacks details on permissions required, error handling (e.g., if table doesn't exist), output format, or performance considerations. For a tool with no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. Every word earns its place, with no redundancy or fluff. It's appropriately sized for a straightforward tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose but lacks behavioral context, usage guidelines, and output details. Without annotations or output schema, more completeness would be beneficial, but it's not severely deficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema (e.g., it doesn't clarify parameter interactions or provide examples). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get detailed schema information') and resource ('for a specific table'), specifying what information is retrieved ('columns, data types, and constraints'). It distinguishes from siblings like list_tables (which lists tables) or analyze_table_stats (which analyzes statistics) by focusing on schema details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., need for a connection), exclusions, or comparisons to siblings like list_constraints or list_indexes that might provide overlapping information. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed information about a specific trigger including its definition and events
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| triggerName | Yes | Name of the trigger to describe | |
| includeDefinition | No | Include the trigger definition (default: true) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool retrieves information (a read operation) but does not disclose behavioral traits such as required permissions, rate limits, error handling, or what 'detailed information' includes beyond definition and events. This leaves significant gaps for a tool with database access.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get detailed information about a specific trigger') and specifies key details ('definition and events'). There is no wasted verbiage or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a database query tool. It lacks details on return format (e.g., structure of 'detailed information'), error conditions, or connection handling. However, it adequately states the purpose and parameters are well-documented in the schema, making it minimally viable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 4 parameters. The description does not add meaning beyond the schema (e.g., it doesn't explain parameter interactions or provide examples). Baseline 3 is appropriate as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 ('detailed information about a specific trigger'), specifying what information is retrieved ('definition and events'). It distinguishes from siblings like 'list_triggers' (which lists triggers) and 'describe_stored_procedure' (which describes a different resource type).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when detailed trigger information is needed, but does not explicitly state when to use this tool versus alternatives (e.g., 'list_triggers' for a list, 'describe_table' for table details). No exclusions or prerequisites are mentioned, leaving guidance incomplete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed information about a specific view including its definition and dependencies
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| viewName | Yes | Name of the view to describe | |
| schema | No | Schema name (default: dbo) | |
| includeDefinition | No | Include the view definition (default: true) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions what information is retrieved ('detailed information... definition and dependencies'), but doesn't cover critical aspects like whether this is a read-only operation, potential performance impacts, authentication needs, error handling, or output format. The description is minimal and lacks behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It avoids unnecessary words and gets straight to the point, though it could be slightly more structured (e.g., by explicitly noting it's for SQL Server views).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't address behavioral aspects like safety, performance, or output format, and provides no usage guidance. For a tool that interacts with databases and has multiple configuration options, this minimal description leaves significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain relationships between parameters like connectionString vs. connectionName). Baseline score of 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get detailed information about a specific view including its definition and dependencies.' It specifies the verb ('Get detailed information') and resource ('a specific view'), but doesn't explicitly differentiate from sibling tools like 'describe_table' or 'describe_stored_procedure' beyond the view focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_views' (for listing views) or 'execute_query' (for custom queries about views), nor does it specify prerequisites or contextual usage scenarios beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Identify common audit trail patterns (created/modified dates, user tracking)
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on behavior: it doesn't specify if this is a read-only operation, what the output format might be (e.g., list of columns, report), whether it requires specific permissions, or if there are rate limits. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Identify common audit trail patterns (created/modified dates, user tracking)'. It is front-loaded with the core purpose and includes clarifying examples in parentheses. There is zero waste, and every word contributes to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It states the purpose clearly but lacks context on behavior, output, or usage relative to siblings. With no output schema, the description doesn't explain return values, and with no annotations, it misses safety or operational details. It's complete enough for basic understanding but has clear gaps for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters (connectionString, connectionName, schema) with their descriptions. The description adds no additional meaning beyond the schema, such as explaining how parameters interact (e.g., precedence between connectionString and connectionName) or what 'audit trail patterns' entail in terms of parameter usage. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Identify common audit trail patterns (created/modified dates, user tracking)'. It specifies the verb 'identify' and the resource 'audit trail patterns', making it distinct from siblings like 'analyze_null_patterns' or 'describe_table'. However, it doesn't explicitly differentiate from similar tools like 'find_computed_columns' or 'list_constraints', which could also involve column analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., database connection), exclusions (e.g., non-SQL Server databases), or compare it to siblings like 'analyze_null_patterns' for other column types. Usage is implied through the action 'identify', but no explicit context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute a custom SQL SELECT query with automatic limit (top 20 rows)
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| query | Yes | SQL SELECT query to execute |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context beyond the input schema by specifying 'automatic limit (top 20 rows)', which informs the agent about result truncation behavior. However, it does not cover other potential behaviors like error handling, permissions required, or execution time limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose and includes a critical behavioral detail (automatic limit). There is no wasted verbiage, and every word contributes to understanding the tool's function and constraints.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (executing arbitrary SQL queries) and lack of annotations and output schema, the description is minimally adequate. It covers the core action and a key behavioral trait (row limit), but does not address output format, error conditions, or security implications, leaving gaps for a mutation-capable tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description does not add any meaning beyond what the schema provides for parameters like 'connectionString' or 'query'. Baseline 3 is appropriate as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Execute a custom SQL SELECT query') and resource (SQL queries), distinguishing it from sibling tools that analyze, describe, list, or sample data rather than executing arbitrary queries. It precisely conveys the tool's function as a query executor.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for executing custom SELECT queries, but does not explicitly state when to use this tool versus alternatives like 'sample_data' or other analysis tools. It lacks guidance on prerequisites, exclusions, or specific scenarios favoring this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List computed columns and their formulas to understand derived business logic
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| tableName | No | Filter by specific table name |
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 states this is a listing operation but doesn't describe what the output looks like (format, structure, or content beyond 'computed columns and their formulas'). It doesn't mention whether this requires specific permissions, whether it's read-only (implied but not stated), or any rate limits or performance considerations for database queries.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise at 10 words. It's front-loaded with the core functionality ('List computed columns and their formulas') followed by the purpose ('to understand derived business logic'). Every word earns its place with zero redundancy or wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a database query tool with 4 parameters and no output schema, the description is insufficient. It doesn't explain what the output contains (beyond 'computed columns and their formulas'), how results are structured, whether there's pagination, or what happens when no computed columns exist. With no annotations and no output schema, the description should provide more behavioral context for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 4 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema - it doesn't explain how parameters interact, provide examples of connection strings, or clarify the relationship between connectionString and connectionName. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List computed columns and their formulas' (verb+resource). It distinguishes from siblings by focusing specifically on computed columns rather than other database objects like tables, indexes, or stored procedures. However, it doesn't explicitly differentiate from similar tools like 'describe_table' which might also provide column information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when this tool is preferable to 'describe_table' or other sibling tools that might provide overlapping information. There's no context about prerequisites, limitations, or typical use cases beyond the generic 'to understand derived business logic' phrase.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Identify reference/lookup tables automatically based on table patterns
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| maxRows | No | Maximum rows to consider as lookup table (default: 1000) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the action ('identify') but doesn't describe what 'identify' entails—e.g., whether it returns a list, what patterns are used, if it's read-only or has side effects, or any performance considerations. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of identifying tables based on patterns, no annotations, and no output schema, the description is incomplete. It doesn't explain what constitutes a 'lookup table,' what 'patterns' are used, or what the output looks like. This leaves the agent with insufficient context to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how 'table patterns' relate to the parameters. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Identify reference/lookup tables automatically based on table patterns.' It specifies the verb ('identify'), resource ('reference/lookup tables'), and method ('based on table patterns'). However, it doesn't explicitly distinguish itself from sibling tools like 'list_tables' or 'analyze_table_stats,' which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where this tool is preferred, such as for data analysis versus simple listing. Without any usage context, the agent must infer when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Identify potentially missing indexes based on query execution patterns
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| minImpact | No | Minimum impact score to include (default: 1000) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the tool 'identifies potentially missing indexes' but doesn't disclose behavioral traits like whether this is a read-only analysis, what permissions are required, how long it might take, whether it impacts database performance, or what format the output takes. The description is minimal and leaves critical operational context unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for the tool's complexity and is perfectly front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's analytical nature, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (e.g., recommendations, impact scores, SQL statements), how results are structured, or any prerequisites for use. For a database analysis tool with zero structured metadata beyond the input schema, the description should provide more operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all four parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('identify') and resource ('potentially missing indexes'), and specifies the basis ('based on query execution patterns'). However, it doesn't explicitly differentiate from sibling tools like 'analyze_index_usage' or 'list_indexes', which could have overlapping analysis functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools focused on database analysis (e.g., 'analyze_index_usage', 'list_indexes'), there's no indication of when this specific index-finding tool is preferable or what distinguishes it from other analysis tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get complete SQL definitions for all stored procedures in a schema
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| includeSystemProcedures | No | Include system stored procedures (default: false) | |
| maxResults | No | Maximum number of procedures to return (default: 50, max: 100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'complete SQL definitions' but doesn't disclose behavioral traits like pagination (implied by maxResults), authentication needs (connection parameters), rate limits, or whether this is a read-only operation. The description is minimal and lacks operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. Every word earns its place with no redundancy or wasted phrasing, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain return values (e.g., format of definitions), behavioral constraints (e.g., default limits), or error conditions. The minimal description leaves significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description adds no parameter-specific information beyond implying schema scope. Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate with additional semantic context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 ('complete SQL definitions for all stored procedures in a schema'), specifying scope ('all stored procedures') and output format ('SQL definitions'). It distinguishes from sibling tools like 'get_stored_procedure_definition' (singular) and 'list_stored_procedures' (names only).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 is provided. The description doesn't mention when to choose this over 'list_stored_procedures' (for names only), 'get_stored_procedure_definition' (for a single procedure), or 'get_multiple_stored_procedure_definitions' (for a subset). Usage context is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get complete SQL definitions for multiple stored procedures at once
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| procedureNames | Yes | Array of stored procedure names to get definitions for | |
| schema | No | Schema name (default: dbo) | |
| includeMetadata | No | Include metadata like creation date, modification date (default: true) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves definitions but doesn't mention whether this is a read-only operation, what permissions are required, how it handles errors, or what the return format looks like. The description is minimal and lacks important behavioral context for a database query tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a straightforward retrieval tool and gets directly to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a database query tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'complete SQL definitions' includes, how results are structured, or any behavioral aspects. The agent would need to guess about the tool's operation and output format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so all parameters are documented in the schema. The description doesn't add any meaningful parameter semantics beyond what's already in the schema - it doesn't explain relationships between parameters (e.g., connectionString vs connectionName) or provide usage examples. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('complete SQL definitions for multiple stored procedures at once'). It distinguishes from the sibling tool 'get_stored_procedure_definition' by specifying 'multiple stored procedures at once', though it doesn't explicitly mention how it differs from 'get_all_stored_procedure_definitions'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over 'get_stored_procedure_definition' (for single procedures) or 'get_all_stored_procedure_definitions' (for all procedures), nor does it discuss prerequisites or context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get foreign key relationships between tables in the database
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical behavioral details: it doesn't specify if this is a read-only operation (implied by 'Get' but not explicit), what permissions are required, how results are formatted (e.g., list of relationships with details), whether it's paginated or returns all data at once, or potential rate limits. For a database query tool with zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get foreign key relationships between tables in the database') with zero wasted words. It's appropriately sized for a straightforward tool and earns its place by clearly stating the tool's function without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (database metadata query with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like safety (read-only vs. destructive), output format, error handling, or connection requirements. For a tool that interacts with database connections and returns relationship data, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters (connectionString, connectionName, schema) with clear descriptions. The description adds no parameter-specific information beyond what's in the schema—it doesn't explain how parameters interact (e.g., precedence between connectionString and connectionName) or provide examples. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('foreign key relationships between tables in the database'), making the tool's purpose immediately understandable. It distinguishes from siblings like 'list_constraints' or 'list_tables' by specifying foreign key relationships. However, it doesn't explicitly contrast with all potential alternatives like 'describe_table' which might include relationship info.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid connection), compare it to siblings like 'list_constraints' (which might include foreign keys) or 'describe_table' (which might show relationships), or specify scenarios where this tool is preferred. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the complete SQL query/definition of a stored procedure - this is the actual source code
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| procedureName | Yes | Name of the stored procedure to get definition for | |
| schema | No | Schema name (default: dbo) | |
| formatOutput | No | Format the SQL output for better readability (default: true) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states it retrieves the 'complete SQL query/definition'. It doesn't disclose behavioral traits like whether this requires specific permissions, if it's read-only, potential rate limits, or what happens on errors. The description is minimal beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get the complete SQL query/definition') and adds clarifying detail ('actual source code'). There's zero waste, and it's appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read operation with 5 parameters and no output schema, the description is adequate but minimal. It covers the basic purpose but lacks details on return format (e.g., raw SQL string), error handling, or prerequisites. Given the schema's good coverage and no annotations, it's minimally viable but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description adds no parameter-specific information beyond what the schema provides, such as clarifying 'procedureName' format or 'connectionString' security implications. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 ('complete SQL query/definition of a stored procedure'), specifying it's the 'actual source code'. It distinguishes from siblings like 'describe_stored_procedure' (which likely provides metadata) and 'get_all_stored_procedure_definitions' (which retrieves multiple).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving SQL source code, but doesn't explicitly state when to use this vs. alternatives like 'describe_stored_procedure' or 'get_all_stored_procedure_definitions'. It provides some context but lacks clear exclusions or comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available named database connections configured in the server
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists connections but does not describe output format, pagination, error handling, or whether it requires specific permissions. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence earns its place by specifying the action, resource, and scope, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is adequate but incomplete. It explains what the tool does but lacks details on behavioral aspects like return format or error conditions, which are important for a tool with no structured data to rely on.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description does not add parameter details beyond this, but since there are no parameters, a baseline of 4 is appropriate as no compensation is needed for missing information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List all available named database connections') and resource ('configured in the server'), distinguishing it from siblings like list_databases or list_tables which focus on different resources. It precisely communicates what the tool does without being vague or tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving configured database connections, but does not explicitly state when to use this tool versus alternatives like test_connection or list_databases. It provides basic context but lacks explicit guidance on exclusions or comparisons with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all constraints (check, unique, foreign key, etc.) across tables in the database
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| constraintType | No | Filter by constraint type (default: ALL) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the action ('List all constraints') but lacks behavioral details: it doesn't specify output format (e.g., list, table, JSON), pagination, error handling, permissions required, or performance implications. The description is minimal and doesn't compensate for missing annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is efficient and front-loaded with the core purpose. No wasted words, though it could be slightly more structured (e.g., by explicitly mentioning parameters). It earns its place by clearly stating the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool with 4 parameters and database interaction. It doesn't explain what the output looks like (critical for a 'list' operation), error conditions, or connection requirements. For a read operation with potential complexity, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are fully documented in the schema. The description adds no additional meaning beyond implying filtering by constraint type ('check, unique, foreign key, etc.'), which aligns with the 'constraintType' enum. Baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all constraints'), specifying constraint types like check, unique, foreign key, etc. It distinguishes from siblings like 'list_default_constraints' by covering all constraint types, but doesn't explicitly contrast with other constraint-related tools like 'analyze_check_constraints'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 versus alternatives. It doesn't mention when to prefer 'list_constraints' over 'list_default_constraints' or 'analyze_check_constraints', nor does it provide context about prerequisites like needing a valid connection. Usage is implied by the description but not articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all databases available on the SQL Server instance
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but lacks behavioral details. It doesn't mention permissions required, rate limits, whether it returns all databases or only accessible ones, or if it includes system databases. 'List all databases' implies a read operation, but no further context is given about the behavior beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place by conveying essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing tool with no annotations and no output schema, the description is minimally adequate. It covers the basic purpose but lacks context about behavior, usage, or output format. Given the complexity is low (2 optional parameters), it's complete enough to understand what it does but not how to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain how connectionString and connectionName interact). This meets the baseline of 3 when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all databases available on the SQL Server instance'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'list_connections' or 'list_tables', which would require a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For example, it doesn't mention if this is for discovery versus analysis tools like 'analyze_database_size', or if it should be used before other listing operations. The description only states what it does, not when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all default value constraints and their definitions
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| tableName | No | Filter by specific table name |
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 states it's a list operation, implying read-only behavior, but doesn't mention any side effects, permissions required, rate limits, or what the output looks like (e.g., format, pagination). For a database query tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff. It's appropriately sized and front-loaded with the core functionality, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a database query tool with 4 parameters. It doesn't explain what 'default value constraints' are in this context, what the output format will be, or any behavioral aspects like error handling. For a tool that likely returns structured data, more context is needed to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters are documented in the schema. The description adds no additional parameter information beyond what's in the schema (it doesn't explain parameter interactions, defaults beyond schema hints, or provide examples). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('default value constraints and their definitions'), making the purpose unambiguous. It distinguishes from generic 'list_constraints' by specifying 'default' constraints, but doesn't explicitly differentiate from all sibling tools like 'analyze_check_constraints' or 'find_computed_columns' which might also relate to constraints.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over 'list_constraints' (which might include all constraint types) or how it relates to other analysis tools. There's no context about prerequisites, typical use cases, or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all user-defined functions (scalar, table-valued, etc.)
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| functionType | No | Filter by function type (default: ALL) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists functions but doesn't describe output format, pagination, error handling, or authentication requirements. The description lacks details on what 'list all' entails (e.g., scope, limitations), leaving behavioral traits unclear for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'List all user-defined functions (scalar, table-valued, etc.)'. It's front-loaded with the core purpose and includes helpful examples without unnecessary elaboration. Every word earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (listing database objects), no annotations, and no output schema, the description is minimally adequate. It states what the tool does but lacks details on behavior, output, or usage context. For a read-only listing tool with full parameter documentation, it's passable but leaves gaps in guiding an agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 4 parameters with descriptions and an enum. The description adds no parameter-specific information beyond implying filtering by function types (e.g., 'scalar, table-valued, etc.'), which aligns with the 'functionType' parameter. This meets the baseline of 3 when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List all user-defined functions (scalar, table-valued, etc.)'. It specifies the verb ('List') and resource ('user-defined functions'), and includes examples of function types. However, it doesn't explicitly differentiate from sibling tools like 'list_stored_procedures' or 'list_views', though the resource type distinction is implicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_stored_procedures' or 'describe_stored_procedure', nor does it specify prerequisites or contexts where this tool is preferred. Usage is implied by the name and purpose but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all indexes on tables with usage statistics and detailed information
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| tableName | No | Filter by specific table name | |
| includeUsageStats | No | Include index usage statistics (default: true) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'usage statistics and detailed information', implying read-only behavior and output details, but doesn't disclose critical traits like whether it requires specific permissions, potential performance impact on the database, rate limits, or error handling. For a tool with 5 parameters and no annotations, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('List all indexes on tables') and adds key details ('with usage statistics and detailed information'). There's zero waste, and every word earns its place by specifying scope and output characteristics without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, no annotations, no output schema), the description is minimally adequate. It covers the basic purpose and output scope but lacks completeness for a tool that interacts with databases: it doesn't explain return format, error conditions, or dependencies on other tools like 'list_connections'. With no output schema, the description should ideally hint at what 'detailed information' includes, but it doesn't, leaving gaps in contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description adds no additional meaning beyond the schema, such as explaining parameter interactions (e.g., how 'connectionString' and 'connectionName' relate) or providing examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List all indexes') and resource ('on tables'), specifying the scope includes 'usage statistics and detailed information'. It distinguishes from siblings like 'list_tables' or 'analyze_index_usage' by focusing on indexes rather than tables or usage analysis alone. However, it doesn't explicitly differentiate from 'find_missing_indexes' or 'list_constraints', which could overlap in purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'analyze_index_usage' for deeper analysis, 'find_missing_indexes' for optimization, or 'list_constraints' for related metadata. There's no context on prerequisites, such as needing a connection, or exclusions for specific database types.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all stored procedures, functions, and their basic information
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| includeSystemObjects | No | Include system stored procedures (default: false) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions 'basic information' but doesn't specify what that includes (e.g., names, schemas, creation dates) or behavioral aspects like pagination, error handling, or performance implications. This leaves significant gaps for a tool with 4 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, no output schema) and rich sibling context, the description is insufficient. It doesn't explain what 'basic information' includes, how results are formatted, or when to choose this over similar tools, leaving the agent with incomplete operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 4 parameters. The description adds no additional parameter semantics beyond what's in the schema, such as explaining interactions between parameters or default behaviors. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('stored procedures, functions, and their basic information'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'list_functions', 'get_all_stored_procedure_definitions', or 'search_stored_procedures_by_content', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools like 'list_functions', 'get_all_stored_procedure_definitions', and 'search_stored_procedures_by_content', the agent is left to infer usage context without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all tables in the connected database
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. While 'List all tables' implies a read-only operation, it doesn't disclose behavioral traits such as whether it requires specific permissions, how it handles large result sets, or what the output format looks like (e.g., list of names vs. detailed metadata). This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without any wasted words. It's appropriately sized for a straightforward listing tool, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (a simple list operation) and 100% schema coverage, the description is minimally adequate. However, with no output schema and no annotations, it lacks details on return values or behavioral constraints, leaving gaps that could hinder correct invocation in more complex scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with all three parameters well-documented in the input schema (e.g., connectionString, connectionName, schema). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all tables in the connected database'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_databases' or 'list_views', which would require more specificity to earn a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools like 'list_databases', 'list_views', and 'describe_table', there's no indication of context, prerequisites, or exclusions, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all triggers in the database with their associated tables
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| tableName | No | Filter by specific table name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it lists triggers 'with their associated tables,' hinting at output structure, but lacks details on permissions needed, rate limits, pagination, or error handling for a database tool, which is a significant gap for safe operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste—it directly states the action and scope. It's front-loaded and appropriately sized for a listing tool, earning full marks for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is minimally adequate. It clarifies the purpose but lacks usage guidelines and behavioral details, making it incomplete for safe and effective use, though not critically so.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 4 parameters. The description adds no additional meaning beyond implying filtering by table via 'associated tables,' but this is already covered in the schema's 'tableName' description. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all triggers in the database'), making the purpose evident. It distinguishes from siblings like 'describe_trigger' by focusing on listing rather than describing details, though it doesn't explicitly contrast with other listing tools like 'list_tables' or 'list_constraints'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a connection), compare to siblings like 'describe_trigger' for detailed info, or specify scenarios where filtering by table is useful, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all user-defined data types and their definitions
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral context. It states it's a listing operation but doesn't disclose pagination, rate limits, permissions required, or what 'definitions' include. For a tool with zero annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a simple listing tool and front-loads the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what 'definitions' entail, the return format, or behavioral aspects like error handling. For a tool with 3 parameters and rich sibling context, it should provide more guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all three parameters. The description adds no parameter-specific information beyond what's in the schema, resulting in the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('user-defined data types and their definitions'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'list_functions' or 'list_stored_procedures', which would require a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a connection), exclusions, or how it differs from other listing tools like 'list_tables' or 'list_functions'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all views in the database with their basic information
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| schema | No | Schema name (default: dbo) | |
| includeSystemViews | No | Include system views (default: false) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'basic information' but doesn't specify what that includes (e.g., view names, schemas, creation dates). It also doesn't disclose behavioral aspects like whether this is a read-only operation, if it requires specific permissions, or how results are formatted/paginated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with no wasted words. It could potentially be improved with more specific information about what 'basic information' includes, but it's appropriately concise for its current content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'basic information' includes in the return values, doesn't mention behavioral constraints, and provides no context about how this differs from related tools. The 100% schema coverage helps, but the description itself lacks completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 4 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema, which is acceptable but not exceptional - meeting the baseline 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all views in the database'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'describe_view' or 'list_tables', which would require more specific scope information to earn a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'describe_view' (detailed view info) and 'list_tables' (similar listing for tables), there's no indication of when this listing tool is preferred over other analysis or description tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve sample data from a table (top 10 rows by default)
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| tableName | Yes | Name of the table to sample | |
| schema | No | Schema name (default: dbo) | |
| limit | No | Number of rows to return (default: 10, max: 100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the default row limit (10) and maximum (100), which is useful behavioral context. However, it doesn't disclose important traits like whether this is a read-only operation (implied but not stated), potential performance impact on large tables, authentication requirements through connection parameters, or what happens with invalid table names. For a data retrieval tool with zero annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise - a single sentence that immediately communicates the core functionality. Every word earns its place: 'Retrieve' (action), 'sample data' (what), 'from a table' (where), and '(top 10 rows by default)' (key behavioral detail). No wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (5 parameters, database operations) and lack of both annotations and output schema, the description is minimally adequate. It covers the basic purpose and default behavior but misses important context about authentication, error handling, performance considerations, and return format. For a data retrieval tool that could have significant implications depending on the database accessed, more completeness would be expected.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value beyond the schema - it implies the 'limit' parameter exists through mentioning 'top 10 rows by default', but doesn't provide additional semantic context about parameter interactions or usage patterns. The baseline of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Retrieve sample data') and resource ('from a table'), making the purpose immediately understandable. It distinguishes from siblings like 'describe_table' or 'execute_query' by focusing on sampling rather than metadata or arbitrary queries. However, it doesn't explicitly differentiate from 'analyze_data_distribution' which might also involve data sampling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'describe_table' (metadata), 'execute_query' (custom queries), and 'analyze_data_distribution' (statistical analysis), there's no indication of when sampling is preferred over these other approaches. The default limit (10 rows) is mentioned but without context about why this default exists or when to override it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for stored procedures containing specific text or patterns in their SQL definition
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') | |
| searchText | Yes | Text or pattern to search for in procedure definitions | |
| schema | No | Schema name (default: dbo) | |
| caseSensitive | No | Case sensitive search (default: false) | |
| includeDefinitions | No | Include full procedure definitions in results (default: false) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the basic operation. It doesn't disclose behavioral traits like whether this is a read-only operation, performance implications, authentication requirements, rate limits, or what the results look like. For a search tool with database access, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose without waste. It's appropriately sized for this tool and front-loads the essential information. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a database search tool with no annotations and no output schema, the description is insufficient. It doesn't explain what results to expect, how they're formatted, whether this is a safe read operation, or any performance considerations. The description alone doesn't provide enough context for an agent to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter semantics beyond what's in the schema - it mentions 'text or patterns' which aligns with searchText parameter but provides no additional context about parameter interactions or usage patterns.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('search for') and resource ('stored procedures') with specific scope ('containing specific text or patterns in their SQL definition'). It distinguishes from siblings like list_stored_procedures (which lists all) and get_stored_procedure_definition (which retrieves specific ones).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over list_stored_procedures, get_all_stored_procedure_definitions, or other search-related tools. No prerequisites, exclusions, or comparative context is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Test the database connection and return basic server information
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | No | SQL Server connection string (uses default if not provided) | |
| connectionName | No | Named connection to use (e.g., 'production', 'staging') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool tests a connection and returns server information, but it doesn't cover critical aspects like whether this is a read-only operation, if it requires authentication, potential side effects (e.g., logging or network traffic), error handling, or rate limits. For a tool that interacts with a database, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core functionality without any wasted words. It's front-loaded with the main action and outcome, making it easy for an agent to parse quickly. This is an excellent example of conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of database operations and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'basic server information' includes, how errors are handled, or any dependencies. For a tool that could involve network calls and authentication, more context is needed to ensure safe and effective use by an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('connectionString' and 'connectionName') with descriptions. The tool description adds no additional parameter semantics beyond what's in the schema, such as examples or usage tips. According to the rules, with high schema coverage, the baseline is 3 even without param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Test the database connection and return basic server information.' It specifies the verb ('test') and resource ('database connection') with an outcome ('return basic server information'). However, it doesn't explicitly differentiate from sibling tools like 'list_connections' or 'execute_query', which might involve connection testing indirectly, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as whether a connection must be established first, or compare it to siblings like 'list_connections' for checking available connections or 'execute_query' for testing with a query. This lack of context leaves the agent to guess based on the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Most tools have distinct purposes, but there is some overlap that could cause confusion. For example, 'describe_stored_procedure' and 'get_stored_procedure_definition' both provide stored procedure details, and 'list_stored_procedures' overlaps with 'search_stored_procedures_by_content' in listing procedures. However, descriptions help clarify differences, and most tools target specific analysis or listing tasks without significant ambiguity.
Tool names follow a highly consistent verb_noun pattern throughout, such as 'analyze_check_constraints', 'describe_table', 'list_databases', and 'find_missing_indexes'. All tools use snake_case without deviation, and verbs like 'analyze', 'describe', 'list', 'find', and 'get' are applied predictably across similar resource types, making the naming scheme clear and uniform.
With 33 tools, the count is borderline high for a database analysis server, feeling somewhat heavy and potentially overwhelming. While the tools cover a wide range of analysis and listing tasks, the number could be streamlined by consolidating overlapping functions (e.g., stored procedure tools). It's reasonable but leans toward excessive for typical agent use.
The tool set provides comprehensive coverage for database analysis and exploration, including listing resources, describing schemas, analyzing performance and data patterns, and executing queries. There are no obvious gaps; it supports full lifecycle tasks from connection testing to in-depth analysis, ensuring agents can handle most database-related workflows without dead ends.
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