This MCP server enables AI assistants to connect to and operate a SQL Server database. Capabilities include:
Test Connection: Verify connectivity and retrieve server version, current database, and connection config with password masked.
Configure Connection: Dynamically update connection settings (server, port, instance, database, credentials, authentication mode, timeouts, etc.), optionally persisting changes to config.json.
List Databases: Retrieve all databases on the connected SQL Server instance.
List Tables: List tables and optionally views in a specified database, with schema filtering.
Describe Table: Get table structure including column names, data types, nullability, default values, and primary keys.
Execute Query: Run any SQL statement (SELECT, INSERT, UPDATE, DELETE, DDL) with named parameters; SELECT results stream with a configurable row limit (default 500, max 10,000).
Execute Script: Run multi-statement SQL scripts with GO separators, with transactional execution and rollback on failure by default.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@SQLServer MCP Server Show me the top 10 records from the Orders table"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
一个基于 MCP 协议的 SQL Server 数据库操作服务器，可以让 AI 助手直接连接本地（或任意）SQL Server，执行任意 SQL 操作：查询、增删改、建表、跑脚本等。
工具 | 说明 |
| 测试连接，返回服务器版本、当前数据库、连接配置（密码脱敏） |
| 运行时修改连接配置（服务器/端口/库/账号/密码等），可选持久化 |
| 列出实例上所有数据库 |
| 列出指定数据库的表（可含视图、指定架构） |
| 查看表结构：列、类型、可空、默认值、主键 |
| 执行任意 SQL，支持命名参数，流式接收 SELECT 结果（默认保留 500 行） |
| 批量执行 SQL 脚本，支持 |
Related MCP server: Python MSSQL MCP Server
SQLServerMCP/
├── index.js # MCP 服务器主程序（入口）
├── config.json # SQL Server 连接配置（不含真实密码）
├── test-connection.js # 独立连接测试脚本（不经过 MCP）
├── package.json
└── node_modules/cd D:\DATA\Desktop\SQLServerMCP
npm install仓库已提供不含密码的 config.json。拉取代码后直接修改该文件：
{
"server": "localhost", // 服务器地址
"port": 1433, // 端口
"instanceName": "", // 命名实例，如 SQLEXPRESS
"database": "master", // 默认数据库
"user": "sa", // SQL 登录名
"password": "你的密码", // SQL 登录密码
"windowsAuth": false, // true = Windows 集成认证（需装 msnodesqlv8）
"odbcDriver": "ODBC Driver 17 for SQL Server", // Windows 认证用的 ODBC 驱动名
"encrypt": true,
"trustServerCertificate": true,
"requestTimeout": 60000
}提示：Windows 认证模式下，程序会自动改写连接串使用
odbcDriver指定的驱动（mssql 默认的SQL Server Native Client 11.0在 Win 上常未安装）。若本机装的是 Driver 18，把这里改成ODBC Driver 18 for SQL Server即可。
修改完成后，执行下面的命令，让 Git 在本机日常提交时忽略 config.json 的修改：
git update-index --skip-worktree config.json如果以后需要提交公共配置结构的调整，先执行：
git update-index --no-skip-worktree config.json提交公共配置后，再重新执行 git update-index --skip-worktree config.json。
配置优先级为：环境变量 > config.json > 默认值。
也可以用环境变量覆盖（优先级最高）：SQLSERVER_SERVER、SQLSERVER_PORT、SQLSERVER_DATABASE、SQLSERVER_USER、SQLSERVER_PASSWORD、SQLSERVER_WINDOWS_AUTH 等。
configure_connection 使用 persist=true 时会写入 config.json，因此配置真实密码后务必执行上面的 skip-worktree 命令。
node test-connection.js看到 ✔ 连接成功 即表示配置正确。
打开 WorkBuddy 连接器管理页，右上角「自定义连接」入口
对 sqlserver-mcp 点击 信任（Trust） 启用
在对话中直接说"查询 X 表的数据"即可使用
「测试一下 SQL Server 连接」
「列出所有数据库」
「查看数据库 MyDB 里有哪些表」
「查看表 [dbo].[Orders] 的结构」
「执行 SQL：SELECT TOP 10 * FROM Orders WHERE Status = 1」
「在 MyDB 里建一张表并插入几条测试数据」
连接失败 / 登录失败
确认 SQL Server 服务已启动（服务名 MSSQLSERVER 或 MSSQL$<实例名>）
在「SQL Server 配置管理器 → SQL Server 网络配置」中启用 TCP/IP，并重启服务
若用 SQL 账号登录，需在服务器属性中启用「SQL Server 和 Windows 身份验证模式」
本机默认实例端口 1433；命名实例用 instanceName 配置（如 SQLEXPRESS），无需写端口
Windows 集成认证
windowsAuth: true 需要额外的原生驱动：npm install msnodesqlv8（本项目已装好）
如果报 SQL Server Native Client 11.0 相关错误，说明本机没装该旧驱动，程序已自动改用 odbcDriver 指定的驱动（默认 ODBC Driver 17 for SQL Server），确认本机 ODBC 驱动名后可在配置中调整
查询结果被截断
execute_query 会流式读取结果，默认仅在内存和响应中保留每个结果集的前 500 行；用 maxRows 参数调大（上限 10000）
timeout 会使用具有对应请求超时的独立连接池，不会修改或复用错误超时的现有连接
多语句脚本
用 execute_script 执行，支持 GO 分隔符；默认 atomic=true，任一批次失败会停止并回滚
CREATE DATABASE 等不允许在事务中执行的语句，可明确传入 atomic=false；此时失败前已完成的批次不会回滚
单个 GO 的重复次数和脚本总批次数上限均为 1000
本服务器被设计为全权限直连，AI 拥有与配置账号等同的数据库权限。请：
使用权限受控的专用账号，避免直接使用 sa
生产库操作前先备份；删除/清空类语句务必确认
修改 SQL Server 连接配置（server/port/instanceName/database/user/password 等）。设置后立即生效，下次数据库操作使用新配置；persist=true 时写入 config.json 永久保存。
| Name | Required | Description | Default |
|---|---|---|---|
| port | No | 端口，默认 1433 | |
| user | No | SQL 登录名 | |
| server | No | 服务器地址，如 localhost 或 192.168.1.10 | |
| encrypt | No | 是否加密连接，默认 true | |
| persist | No | 是否写入 config.json 永久保存，默认 false | |
| database | No | 默认数据库，如 master | |
| password | No | SQL 登录密码 | |
| odbcDriver | No | Windows 认证使用的 ODBC 驱动名，默认 "ODBC Driver 17 for SQL Server" | |
| windowsAuth | No | 是否使用 Windows 集成认证（需安装 msnodesqlv8） | |
| instanceName | No | 命名实例名，如 SQLEXPRESS | |
| requestTimeout | No | 单条 SQL 超时毫秒数，默认 60000 | |
| trustServerCertificate | No | 是否信任服务器证书，默认 true |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
无注解，描述承担了行为披露责任。它说明了设置后立即生效、下次数据库操作使用新配置，以及 persist=true 时持久化到 config.json。这些是核心行为，但未提及权限要求、验证过程或返回值。
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?
描述简洁，两句话，信息密度高，无冗余。先说明用途，再说明行为和持久化，结构清晰。
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?
核心行为（生效时机、持久化）已说明，但缺少返回值、错误处理、验证行为等。由于无输出 schema，描述未说明返回内容，对于配置变更工具来说信息不够完整。
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?
输入 schema 覆盖 100%，每个参数都有详细描述。工具描述列举了部分参数，并额外说明了 persist 的行为，但未提供超出 schema 的语法或格式细节，baseline 为 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?
描述明确说明工具用于修改 SQL Server 连接配置，并列举了 server/port/instanceName/database/user/password 等关键字段。与兄弟工具（list_databases、execute_query 等）有清晰区分，动词+资源明确。
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?
描述未显式说明何时使用或排除替代工具，但从名称和功能可清晰判断这是配置变更工具，与数据查询类工具区分明显。上下文暗示了使用场景，但缺少显式替代方案说明。
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
查看表结构：列名、类型、是否可空、默认值、是否主键。
| Name | Required | Description | Default |
|---|---|---|---|
| table | Yes | 表名 | |
| schema | No | 架构名，默认 dbo | |
| database | No | 目标数据库名，默认当前连接数据库 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the output content (column names, types, nullable, default values, primary key), indicating a read-only inspection behavior. However, it does not explicitly state that it performs no modification or any permission requirements, but for a describe operation this is minimally adequate.
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?
A single, front-loaded sentence that entirely conveys the tool's purpose with no redundant words.
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?
The description is sufficient for a simple describe-table tool, mentioning the return fields. Though there is no output schema, the listed attributes cover the expected output. It omits details like error conditions, but given the simplicity and schema coverage of parameters, it is adequately 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?
The schema descriptions for all three parameters (table, schema, database) provide full coverage, so the description need not add parameter details. The description itself does not reference parameters, which is acceptable given the schema completeness.
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 ('查看' = view) and resource ('表结构' = table structure), and explicitly lists the returned attributes (column names, types, nullable, default, primary key). This clearly distinguishes it from siblings like list_tables, which lists table names.
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 about when to use this tool versus alternatives. The description lacks any mention of when to prefer describe_table over list_tables or execute_query, nor does it state exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
执行任意 SQL 语句（SELECT/INSERT/UPDATE/DELETE/DDL 等）。支持命名参数：SQL 中写 @name，params 传 { name: value }（键不需要 @ 前缀）。SELECT 返回行数据，非查询语句返回受影响行数。
| Name | Required | Description | Default |
|---|---|---|---|
| sql | Yes | 要执行的 SQL 语句 | |
| params | No | 命名参数，例如 { name: "张三", age: 30 } | |
| maxRows | No | SELECT 最多返回行数，默认 500 | |
| timeout | No | 超时毫秒数，默认 60000 | |
| database | No | 目标数据库名，默认当前连接数据库 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosure. It does explain return value behavior (rows for SELECT, affected row count for non-query) and the named parameter syntax, which is useful. However, it fails to warn about the destructive nature of DELETE/UPDATE/DDL statements, does not mention permissions or transactionality, and omits any note about irreversibility or safety measures.
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 three short sentences, each earning its place: purpose, parameter syntax, and return behavior. It is front-loaded with the core action and avoids any redundant filler.
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 general SQL executor, the description adequately covers what it does, parameter usage, and return semantics. Given the complexity of arbitrary SQL, it would be more complete with safety warnings or transaction behavior, but the essentials for invoking the tool are present. The schema already provides parameter details, so the description need not repeat them.
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 covers all 5 parameters (100% coverage), but the description adds critical usage semantics beyond the schema: the @name convention in SQL and the corresponding key format in params (no @ prefix). It also clarifies the return behavior of SELECT vs. non-query statements, which is not stated in the schema. This adds meaningful value.
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 begins with '执行任意 SQL 语句' (execute arbitrary SQL statements) and lists statement types (SELECT/INSERT/UPDATE/DELETE/DDL), making the tool's role crystal clear. This general-purpose executor is clearly distinguished from sibling tools like list_databases or describe_table, which handle metadata and connection operations.
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 purpose is unambiguous: this tool runs arbitrary SQL, implying it is for direct database manipulation. However, it does not explicitly mention when to prefer execute_script over this tool, nor does it state exclusions like 'use list_tables for schema metadata'. The context is clear but lacks explicit alternatives or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
批量执行一段 SQL 脚本，支持多条语句与 GO 分隔符（按顺序执行，任一批次失败即停止并报告）。适合建表、初始化数据等场景。
| Name | Required | Description | Default |
|---|---|---|---|
| sql | Yes | SQL 脚本内容（多条语句用 ; 或 GO 分隔） | |
| timeout | No | 每个批次的超时毫秒数 | |
| database | No | 目标数据库名，默认当前连接数据库 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states that statements execute in order and stop on first failure ('按顺序执行，任一批次失败即停止并报告'), which is useful. However, it does not disclose potential side effects (e.g., whether it can modify data), transactional behavior, or permission requirements—critical for a script execution 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 exceptionally concise: one sentence stating the main action and execution behavior, plus a short use-case clause. It is front-loaded with the core purpose and contains no redundant or filler 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?
The tool is moderately complex (executes arbitrary SQL scripts) and has no annotations or output schema. The description covers the execution model and intended use but lacks safety warnings, return-value expectations, or notes about whether the script runs in a transaction. Given the potential for data modification, this omission makes the description incomplete, though not severely.
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?
All three parameters have descriptions in the schema (100% coverage), so the baseline is 3. The description does not add significant meaning beyond the schema; it only restates that the script supports multiple statements and GO delimiters, which is already in the sql parameter description. No additional insight is provided for timeout or database parameters.
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 function: '批量执行一段 SQL 脚本' (batch execute a SQL script). It specifies the resource (SQL script) and adds distinct features like support for multiple statements and GO separators, which differentiates it from the sibling execute_query tool. The use case ('适合建表、初始化数据') further clarifies its 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 gives clear context for when to use the tool: for batch operations like creating tables and initializing data. It implicitly distinguishes from execute_query by emphasizing batch execution and GO delimiter support. However, it does not explicitly mention when not to use it or name alternative tools, which would be needed for a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
列出 SQL Server 实例上的所有数据库。
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states that the tool performs a read-only listing of all databases, but does not mention return format, error conditions, or permission requirements. This is adequate but minimal.
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 in Chinese, front-loaded with the action and scope. There is no unnecessary content 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 the tool's simplicity (no parameters, no output schema), the description is mostly complete for its intended purpose. It clearly defines the scope ('all databases') but could be improved by briefly indicating what information is returned for each database.
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, so the baseline is 4. The description does not need to add parameter meaning, and the empty schema is consistent with the stated behavior of listing all databases without filters.
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 lists all databases on a SQL Server instance, using a specific verb and resource. It is easily distinguished from sibling tools like list_tables and describe_table.
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, nor are any exclusions or prerequisites mentioned. The usage context is only implied by the tool's name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
列出指定数据库中的表（可含视图）。
| Name | Required | Description | Default |
|---|---|---|---|
| schema | No | 架构名，默认 dbo | |
| database | No | 目标数据库名，默认使用当前连接数据库 | |
| includeViews | No | 是否包含视图，默认 false |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only mentions that views may be included, which is a parameter detail already in the schema. It does not address permissions, error cases, or output format.
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, front-loaded sentence that clearly conveys the tool's main function without wasted words. It is appropriately sized for the simplicity of the operation.
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 simple nature of the tool and full parameter documentation, the description is minimally viable. However, since there is no output schema, it should ideally mention return format or error behavior, which it does not.
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 baseline is 3. The description does not add meaning beyond what the schema already provides for the three optional parameters; it only restates the includeViews option.
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 explicitly states the tool lists tables in a specified database, with an option to include views. This clearly distinguishes it from sibling tools like list_databases and describe_table.
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 use when needing to enumerate tables in a database, but it does not mention alternatives or exclusions, such as when to use describe_table for schema details. It provides clear context but no explicit guidance on when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
测试与 SQL Server 的连接，返回服务器版本、当前数据库、当前连接配置（密码已脱敏）。无参数。
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 discloses return values and notes that passwords are masked, which is important security-related context. It does not explicitly state read-only behavior, but the 'test' wording strongly implies non-destructive 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 concise sentence with all necessary information: purpose, return values, and parameter note. No filler or repetition.
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 zero-parameter tool, the description fully explains purpose and return values. No output schema exists, so listing the returned data adds completeness. It is sufficient for an agent to decide when to invoke.
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 the description confirms this with '无参数' (no parameters). The schema is empty with 100% coverage, so no additional parameter explanation is needed. The baseline for 0 params is 4.
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 tests SQL Server connection and returns specific information (server version, current database, connection config with masked password). This distinguishes it from sibling tools like list_databases and execute_query, which have different purposes.
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 verifying connection status, but it does not explicitly specify when to use this tool over alternatives or provide exclusions. Sibling context suggests it is a diagnostic tool, but no direct guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool targets a unique aspect: database listing, table listing, schema details, ad-hoc queries, batch scripts, connection testing, and configuration changes. The two execute tools are clearly distinguished by single vs multi-statement batch execution.
All tools follow a consistent verb_noun pattern using lowercase snake_case, making the API predictable and easy to learn. No mixed conventions or vague verbs.
With 7 tools, the set is well-scoped for a SQL Server integration, covering essential operations without redundancy or bloat. It fits squarely in the ideal 3-15 range.
The tool set provides complete coverage for a SQL Server MCP: connection management, database/table discovery, schema inspection, and arbitrary SQL execution (both single statements and batch scripts). There are no obvious gaps that would cause agent failures.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
The BigQuery remote MCP server is a fully managed service that uses the Model Context Protocol to connect AI applications and LLMs to BigQuery data sources. It provides secure, standardized tools for AI agents to list datasets and tables, retrieve schemas, generate and execute SQL queries through natural language, and analyze data—enabling direct access to enterprise analytics data without requiring manual SQL coding.
Connects AI assistants to CloudQuell multi-cloud and AI cost, savings, anomaly, and budget data.
A Model Context Protocol server for Wix AI tools
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