Un puente fácil de usar que permite a asistentes de IA como Claude consultar y explorar directamente bases de datos de Microsoft SQL Server. ¡No se requiere experiencia en programación!
Esta herramienta permite a los asistentes de IA:
Descubra tablas en su base de datos de SQL Server
Ver estructuras de tablas (columnas, tipos de datos, etc.)
Ejecute consultas SQL de solo lectura de forma segura
Generar consultas SQL a partir de solicitudes en lenguaje natural
Related MCP server: MS SQL MCP Server
No se requiere codificación : brinde a Claude y otros asistentes de IA acceso directo a sus bases de datos de SQL Server sin escribir código de integración complejo
Mantener el control : todas las consultas son de solo lectura de forma predeterminada, lo que garantiza que sus datos permanezcan seguros.
Privado y seguro : las credenciales de su base de datos permanecen locales y nunca se envían a servicios externos.
Ahorre horas de trabajo manual : ya no es necesario copiar y pegar datos ni resultados de consultas para compartir con IA.
Análisis más profundo : la IA puede navegar por todo el esquema de su base de datos y brindar información en múltiples tablas
Interfaz de lenguaje natural : haga preguntas sobre sus datos en un inglés sencillo
Ponga fin al problema del límite de contexto : acceda a grandes conjuntos de datos que superarían las ventanas de contexto de IA normales
Analistas de datos que desean que la IA ayude a interpretar datos SQL sin compartir credenciales
Desarrolladores que buscan una forma rápida de explorar la estructura de la base de datos a través de una conversación natural.
Analistas de negocios que necesitan información sin experiencia en SQL
Administradores de bases de datos que desean proporcionar acceso controlado a herramientas de IA
Instalar Node.js (versión 14 o superior)
Tener acceso a una base de datos de Microsoft SQL Server (local o 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 .envEdite el archivo .env con las credenciales de su base de datos:
DB_USER=your_username
DB_PASSWORD=your_password
DB_SERVER=your_server_name_or_ip
DB_DATABASE=your_database_name
PORT=3333
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 clientExplora la estructura de tu base de datos sin escribir SQL
mcp_SQL_mcp_discover_database()Obtenga información detallada sobre una tabla específica
mcp_SQL_mcp_table_details({ tableName: "Customers" })Ejecutar una consulta segura
mcp_SQL_mcp_execute_query({ sql: "SELECT TOP 10 * FROM Customers", returnResults: true })Buscar tablas por patrón de nombre
mcp_SQL_mcp_discover_tables({ namePattern: "%user%" })Utilice la paginación para navegar por grandes conjuntos de resultados
// 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
})Paginación basada en cursor para un rendimiento óptimo
// 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
})Haga preguntas en lenguaje natural
"Show me the top 5 customers with the most orders in the last month"Análisis del rendimiento de ventas : "Muéstrame las tendencias de ventas mensuales del último año e identifica nuestros productos con mejor rendimiento por región".
Segmentación de clientes : "Analizar nuestra base de clientes por frecuencia de compra, valor promedio de pedido y ubicación geográfica".
Informes financieros : "Crear un informe trimestral de ganancias y pérdidas comparando este año con el año pasado".
Optimización del esquema : "Ayúdeme a identificar tablas con índices faltantes examinando los datos de rendimiento de las consultas".
Auditoría de calidad de datos : "Encuentre todos los registros de clientes con información incompleta o valores no válidos".
Análisis de uso : "Muéstrame qué tablas son las que se acceden con más frecuencia y qué consultas consumen más recursos".
Exploración de API : "Estoy creando una API. Ayúdenme a analizar el esquema de la base de datos para diseñar puntos finales apropiados".
Optimización de consultas : "Revise esta consulta compleja y sugiera mejoras de rendimiento".
Documentación de base de datos : "Crear documentación completa de la estructura de nuestra base de datos con explicaciones de las relaciones".
El cliente incluido proporciona una interfaz sencilla basada en menús:
Lista de recursos disponibles : vea qué información está disponible
Lista de herramientas disponibles : vea qué acciones puede realizar
Ejecutar consulta SQL : ejecuta una consulta SQL de solo lectura
Obtener detalles de la tabla : ver la estructura de cualquier tabla
Leer el esquema de la base de datos : ver todas las tablas y sus relaciones
Generar consulta SQL : convertir lenguaje natural a SQL
Al trabajar con Claude u otros asistentes de IA a través de este servidor MCP, la forma en que formulas tus solicitudes influye significativamente en los resultados. Así es como puedes ayudar a la IA a usar las herramientas de la base de datos eficazmente:
Al solicitarle a una IA que use esta herramienta, siga esta estructura:
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 monthAquí están las herramientas principales y su sintaxis correcta:
// 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" })Cuándo utilizar cada herramienta:
Descubrimiento de base de datos : comience con esto cuando la IA no esté familiarizada con la estructura de su base de datos.
Detalles de la tabla : Úselo cuando se centre en una tabla específica antes de escribir consultas.
Ejecución de consultas : cuando necesita recuperar o analizar datos reales.
Descubrimiento de tablas por patrón : al buscar tablas relacionadas con un dominio específico.
Para tareas complejas, guíe a la IA a través de una serie de pasos:
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.Recuerde a la IA la sintaxis específica de SQL Server:
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"Si la IA utiliza una sintaxis incorrecta, puedes ayudarla con lo siguiente:
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
})Si la IA tiene dificultades con una tarea de base de datos, pruebe estos enfoques:
Sea más específico acerca de las tablas: "Antes de escribir esa consulta, verifique si la tabla CustomerOrders existe y qué columnas tiene".
Divida las tareas complejas en pasos: "Abordemos esto paso a paso. Primero, observe la estructura de la tabla Productos. Luego, revise la tabla Pedidos..."
Solicitar resultados intermedios: "Ejecute primero una consulta simple en esa tabla para que podamos verificar el formato de los datos antes de intentar un análisis más complejo".
Solicitar explicaciones de la consulta: "Después de escribir esta consulta, explique qué hace cada parte para que pueda verificar que hace lo que necesito".
El servidor MCP proporciona herramientas potentes para explorar la estructura de su base de datos:
Descubrimiento de tablas basado en patrones : encuentre tablas que coincidan con patrones específicos
mcp_SQL_mcp_discover_tables({ namePattern: "%order%" })Descripción general del esquema : obtenga una vista de alto nivel de las tablas por esquema
mcp_SQL_mcp_execute_query({
sql: "SELECT TABLE_SCHEMA, COUNT(*) AS TableCount FROM INFORMATION_SCHEMA.TABLES GROUP BY TABLE_SCHEMA"
})Exploración de columnas : Examine los metadatos de las columnas de cualquier tabla
mcp_SQL_mcp_table_details({ tableName: "dbo.Users" })El servidor admite múltiples métodos de paginación para gestionar grandes conjuntos de datos:
Paginación de desplazamiento/obtención : paginación SQL estándar mediante OFFSET y FETCH
mcp_SQL_mcp_execute_query({
sql: "SELECT * FROM Users ORDER BY Username OFFSET 0 ROWS FETCH NEXT 10 ROWS ONLY"
})Paginación basada en cursor : más eficiente para grandes conjuntos de datos
// 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"
})Contar con datos : recuperar el recuento total junto con los datos paginados
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"
})Explora las relaciones entre tablas con operaciones de unión:
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"
})Ejecute agregaciones y consultas analíticas para obtener información:
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"
})El servidor MCP admite características específicas de SQL Server:
Expresiones de tabla comunes (CTE)
Funciones de ventana
Operaciones JSON
Consultas jerárquicas
Búsqueda de texto completo (cuando esté configurada en su base de datos)
Conecte esta herramienta directamente a Claude Desktop en unos sencillos pasos:
Instalar Claude Desktop desde anthropic.com
Editar el archivo de configuración de Claude:
Ubicación: ~/Library/Application Support/Claude/claude_desktop_config.json
Añade esta configuración:
{
"mcpServers": {
"mssql": {
"command": "node",
"args": [
"/FULL/PATH/TO/mssql-mcp-server/server.mjs"
]
}
}
}Reemplace /FULL/PATH/TO/ con la ruta real a donde clonó este repositorio
Reiniciar Claude Desktop
Busque el ícono de herramientas en Claude Desktop: ¡ahora puede usar comandos de base de datos directamente!
Cursor es un editor de código basado en IA que permite usar esta herramienta para interacciones avanzadas con bases de datos. Aquí te explicamos cómo configurarlo:
Abra Cursor IDE (descárguelo desde cursor.sh si no lo tiene)
Inicie el servidor MS SQL MCP mediante el transporte HTTP/SSE:
npm run start:sseCree un nuevo espacio de trabajo o abra un proyecto existente en Cursor
Ingresar a la configuración del cursor
Haga clic en MCP
Agregar nuevo servidor MCP
Nombre su servidor MCP, seleccione el tipo: sse
Ingrese la URL del servidor como: localhost:3333/sse (o el puerto en el que lo tiene ejecutándose)
Una vez conectado, puedes usar los comandos MCP directamente en el chat de IA de Cursor:
Pídele a Claude en Cursor que explore tu base de datos:
Can you show me the tables in my database?Ejecutar consultas específicas:
Query the top 10 records from the Customers tableGenerar y ejecutar consultas complejas:
Find all orders from the last month with a value over $1000Asegúrese de que el servidor MS SQL MCP se esté ejecutando con el transporte HTTP/SSE
Verifique que el puerto sea correcto y coincida con el que figura en su archivo .env
Asegúrese de que su firewall no esté bloqueando la conexión
Si usa una IP/nombre de host diferente, actualice SERVER_URL en su archivo .env
Ideal para: usar directamente con Claude Desktop o el cliente incluido
npm startIdeal para: acceso a la red o cuando se utiliza con aplicaciones web
npm run start:sseSolo lectura por defecto : sin riesgo de modificación de datos
Credenciales privadas : Los detalles de conexión a la base de datos permanecen en su archivo .env
Protección contra inyección SQL : validación integrada para consultas SQL
Verifique su archivo .env para verificar las credenciales de base de datos correctas
Asegúrese de que su SQL Server esté ejecutándose y aceptando conexiones
Para Azure SQL, verifique que su IP esté permitida en la configuración del firewall
Ejecute npm install nuevamente para asegurarse de que todas las dependencias estén instaladas
Asegúrate de estar utilizando Node.js versión 14 o superior
Para el transporte HTTP/SSE, verifique que el PUERTO en su .env esté disponible
Asegúrese de que ningún firewall esté bloqueando la conexión
Verifique nuevamente la ruta en su claude_desktop_config.json
Asegúrese de utilizar rutas absolutas, no relativas
Reinicie Claude Desktop por completo después de realizar cambios
Si eres nuevo en SQL Server, aquí hay algunos conceptos clave:
Tablas : almacene sus datos en filas y columnas
Esquemas : agrupaciones lógicas de tablas (como carpetas)
Consultas : Comandos para recuperar o analizar datos
Vistas : Consultas predefinidas guardadas para facilitar el acceso
¡Esta herramienta te ayuda a explorar todo esto sin necesidad de ser un experto en SQL!
El servidor MS SQL MCP está construido con una arquitectura modular que separa las preocupaciones por la capacidad de mantenimiento y la extensibilidad:
database.mjs - Conectividad de bases de datosAdministra la agrupación de conexiones de SQL Server
Proporciona ejecución de consultas con lógica de reintento y manejo de errores.
Maneja conexiones de bases de datos, transacciones y configuración.
Incluye utilidades para desinfectar errores de formato y SQL.
tools.mjs - Registro de herramientasRegistra todas las herramientas de base de datos con el servidor MCP
Implementa la validación de herramientas y la verificación de parámetros.
Proporciona funcionalidad básica para consultas SQL, exploración de tablas y descubrimiento de bases de datos.
Llamadas de la herramienta Mapas a operaciones de base de datos
resources.mjs - Recursos de base de datosExpone metadatos de la base de datos a través de puntos finales de recursos
Proporciona información de esquema, listados de tablas y documentación de procedimientos.
Formatea la información de la estructura de la base de datos para el consumo de IA
Incluye utilidades de descubrimiento para la exploración de bases de datos.
pagination.mjs - Navegación de resultadosImplementa paginación basada en cursor para conjuntos de resultados grandes
Proporciona utilidades para generar cursores de página siguiente/anterior
Transforma las consultas SQL para admitir la paginación
Maneja la sintaxis de paginación OFFSET/FETCH de SQL Server
errors.mjs - Manejo de erroresDefine tipos de errores personalizados para diferentes escenarios de falla.
Implementa el formato de error JSON-RPC
Proporciona mensajes de error legibles por humanos
Incluye middleware para el manejo global de errores
logger.mjs - Sistema de registroConfigura el registro de Winston con múltiples transportes
Proporciona registro de solicitudes según el contexto
Maneja la rotación y el formato del registro
Captura excepciones no detectadas y rechazos no controlados
Cuando se recibe una llamada de herramienta, el servidor MCP la enruta al controlador apropiado en tools.mjs
El controlador de herramientas valida los parámetros y construye una consulta de base de datos
La consulta se ejecuta a través de funciones en database.mjs , con posible paginación desde pagination.mjs
Los resultados se formatean y se devuelven al cliente.
Cualquier error se detecta y procesa mediante errors.mjs
Todas las operaciones se registran mediante logger.mjs
Esta arquitectura garantiza:
Separación clara de preocupaciones
Manejo consistente de errores
Registro completo
Gestión eficiente de la conexión a bases de datos
Ejecución de consultas escalable
El archivo .env controla cómo se conecta y opera el servidor MS SQL MCP a su base de datos. A continuación, se detalla cada configuración:
# Database Connection Settings
DB_USER=your_username # SQL Server username
DB_PASSWORD=your_password # SQL Server password
DB_SERVER=your_server_name # Server hostname or IP address (example: localhost, 10.0.0.1, myserver.database.windows.net)
DB_DATABASE=your_database_name # Name of the database to connect to
# Server Configuration
PORT=3333 # Port for the HTTP/SSE server to listen on
TRANSPORT=stdio # Connection method: 'stdio' (for Claude Desktop) or 'sse' (for network connections)
SERVER_URL=http://localhost:3333 # Base URL when using SSE transport (must match your PORT setting)
# 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 filesÚselo al conectarse directamente con Claude Desktop
La comunicación se realiza a través de flujos de entrada/salida estándar.
Establezca TRANSPORT=stdio en su archivo .env
Ejecutar con npm start
Úselo cuando se conecta a través de una red (como con Cursor IDE)
Utiliza eventos enviados por el servidor (SSE) para la comunicación en tiempo real
Establezca TRANSPORT=sse en su archivo .env
Configure SERVER_URL para que coincida con la dirección de su servidor
Ejecutar con 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=AdventureWorksLos resultados de la consulta se guardan como archivos JSON en el directorio especificado por QUERY_RESULTS_PATH . Esto evita que grandes conjuntos de resultados saturen la conversación. Puedes:
Deje este campo en blanco para utilizar el directorio query-results predeterminado en el proyecto.
Establezca una ruta personalizada como /Users/username/Documents/query-results
Acceda a los resultados guardados utilizando el UUID proporcionado en la respuesta de la herramienta
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, the description carries full burden for behavioral disclosure. It only states that it lists connections without mentioning auth requirements, rate limits, side effects, or whether the listing is scoped to the current user. For a read operation, minimal transparency is provided beyond the action itself.
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 of 8 words. It is front-loaded with the action and resource, containing no fluff or repetition. Every word adds value.
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 low complexity (no params, no output schema), the description covers the essential purpose but lacks details on what the list contains (e.g., connection names, types, status). For a list tool, some context about the output would enhance usability.
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 zero parameters and schema coverage is 100% (empty schema). Per guidelines, baseline for 0 parameters is 4. The description does not need to add parameter info but provides concise purpose that aligns with the schema.
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 uses a specific verb 'list' and resource 'database connections', clearly distinguishing it from sibling list tools like list_databases and list_tables. It adds clarity by specifying 'named database connections configured in the server', avoiding any ambiguity.
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 like test_connection or other list tools. The description does not mention any prerequisites, exclusions, or context-dependent usage.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0list_connections1 field changedInput schema / additionalPropertiesRemoved value: -falseanalyze_check_constraintsanalyze_data_distributionanalyze_database_sizeanalyze_index_usageanalyze_null_patternsanalyze_table_statsdescribe_stored_proceduredescribe_tabledescribe_triggerdescribe_viewdetect_audit_columnsexecute_queryfind_computed_columnsfind_lookup_tablesfind_missing_indexesget_all_stored_procedure_definitionsget_multiple_stored_procedure_definitionsget_relationshipsget_stored_procedure_definitionlist_connectionslist_constraintslist_databaseslist_default_constraintslist_functionslist_indexeslist_stored_procedureslist_tableslist_triggerslist_user_defined_typeslist_viewssample_datasearch_stored_procedures_by_contenttest_connectionScored across 33 tools
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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