Skip to main content
Glama
deenesjakoruzh
kiliczsh

MongoDB

by kiliczsh
OverviewSchemaSourceRelated ServersScoreDiscussions
TypeScript
Hybrid

MCP MongoDB 服务器

NPM 版本NPM 下载NPM 许可证 铁匠徽章

模型上下文协议 (MLM) 服务器，使 LLM 能够与 MongoDB 数据库交互。该服务器提供通过标准化接口检查集合架构和执行 MongoDB 操作的功能。

演示

MCP MongoDB 服务器演示 | Claude 桌面

Related MCP server: MongoDB MCP Server for LLMs

主要特点

智能 ObjectId 处理

  • 字符串ID与MongoDB ObjectId之间的智能转换

  • 可使用objectIdMode参数配置：

    • "auto" ：根据字段名称转换（默认）

    • "none" ：无转换

    • "force" ：强制所有字符串 ID 字段为 ObjectId

灵活配置

  • 环境变量：

    • MCP_MONGODB_URI ：MongoDB 连接 URI

    • MCP_MONGODB_READONLY ：设置为“true”时启用只读模式

  • 命令行选项：

    • --read-only或-r ：以只读模式连接

只读模式

  • 防止写入操作（更新、插入、创建索引）

  • 使用 MongoDB 的辅助读取偏好来获得最佳性能

  • 非常适合安全连接到生产数据库

MongoDB 操作

  • 读取操作：

    • 查询文档并进行可选的执行计划分析

    • 执行聚合管道

    • 统计符合条件的文档数量

    • 获取集合架构信息

  • 写入操作（不处于只读模式时）：

    • 更新文档

    • 插入新文档

    • 创建索引

LLM 整合

  • 收集完成以增强 LLM 交互

  • 模式推理可改善上下文理解

  • 收集分析以获取数据洞察

安装

全局安装

npm install -g mcp-mongo-server

为了发展

# Clone repository
git clone https://github.com/kiliczsh/mcp-mongo-server.git
cd mcp-mongo-server

# Install dependencies
npm install

# Build
npm run build

# Development with auto-rebuild
npm run watch

用法

基本用法

# Start server with MongoDB URI
npx -y mcp-mongo-server mongodb://muhammed:kilic@localhost:27017/database

# Connect in read-only mode
npx -y mcp-mongo-server mongodb://muhammed:kilic@localhost:27017/database --read-only

环境变量

您可以使用环境变量配置服务器，这对于 CI/CD 管道、Docker 容器或当您不想在命令参数中公开连接详细信息时特别有用：

# Set MongoDB connection URI
export MCP_MONGODB_URI="mongodb://muhammed:kilic@localhost:27017/database"

# Enable read-only mode
export MCP_MONGODB_READONLY="true"

# Run server (will use environment variables if no URI is provided)
npx -y mcp-mongo-server

在 Claude Desktop 配置中使用环境变量：

{
  "mcpServers": {
    "mongodb-env": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-mongo-server"
      ],
      "env": {
        "MCP_MONGODB_URI": "mongodb://muhammed:kilic@localhost:27017/database",
        "MCP_MONGODB_READONLY": "true"
      }
    }
  }
}

在 Docker 中使用环境变量：

# Build
docker build -t mcp-mongo-server .

# Run
docker run -it -d -e MCP_MONGODB_URI="mongodb://muhammed:kilic@localhost:27017/database" -e MCP_MONGODB_READONLY="true" mcp-mongo-server

# or edit docker-compose.yml and run
docker-compose up -d

与 Claude Desktop 集成

手动配置

将服务器配置添加到 Claude Desktop 的配置文件中：

MacOS ： ~/Library/Application Support/Claude/claude_desktop_config.json Windows ： %APPDATA%/Claude/claude_desktop_config.json

命令行参数方法：

{
  "mcpServers": {
    "mongodb": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-mongo-server",
        "mongodb://muhammed:kilic@localhost:27017/database"
      ]
    },
    "mongodb-readonly": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-mongo-server",
        "mongodb://muhammed:kilic@localhost:27017/database",
        "--read-only"
      ]
    }
  }
}

环境变量方法：

{
  "mcpServers": {
    "mongodb": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-mongo-server"
      ],
      "env": {
        "MCP_MONGODB_URI": "mongodb://muhammed:kilic@localhost:27017/database"
      }
    },
    "mongodb-readonly": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-mongo-server"
      ],
      "env": {
        "MCP_MONGODB_URI": "mongodb://muhammed:kilic@localhost:27017/database",
        "MCP_MONGODB_READONLY": "true"
      }
    }
  }
}

GitHub 包使用情况：

{
  "mcpServers": {
    "mongodb": {
      "command": "npx",
      "args": [
        "-y",
        "github:kiliczsh/mcp-mongo-server",
        "mongodb://muhammed:kilic@localhost:27017/database"
      ]
    },
    "mongodb-readonly": {
      "command": "npx",
      "args": [
        "-y",
        "github:kiliczsh/mcp-mongo-server",
        "mongodb://muhammed:kilic@localhost:27017/database",
        "--read-only"
      ]
    }
  }
}

与 Windsurf 和 Cursor 集成

MCP MongoDB 服务器可以与 Windsurf 和 Cursor 一起使用，方式与 Claude Desktop 类似。

风帆冲浪配置

将服务器添加到您的 Windsurf 配置：

{
  "mcpServers": {
    "mongodb": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-mongo-server",
        "mongodb://muhammed:kilic@localhost:27017/database"
      ]
    }
  }
}

游标配置

对于 Cursor，将服务器配置添加到您的设置中：

{
  "mcpServers": {
    "mongodb": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-mongo-server",
        "mongodb://muhammed:kilic@localhost:27017/database"
      ]
    }
  }
}

您还可以将环境变量方法与 Windsurf 和 Cursor 一起使用，遵循 Claude Desktop 配置中显示的相同模式。

自动安装

使用 Smithery ：

npx -y @smithery/cli install mcp-mongo-server --client claude

使用 mcp-get ：

npx @michaellatman/mcp-get@latest install mcp-mongo-server

可用工具

查询操作

  • 查询：执行 MongoDB 查询

    {
  collection: "users",
  filter: { age: { $gt: 30 } },
  projection: { name: 1, email: 1 },
  limit: 20,
  explain: "executionStats"  // Optional
}

  • 聚合：运行聚合管道

    {
  collection: "orders",
  pipeline: [
    { $match: { status: "completed" } },
    { $group: { _id: "$customerId", total: { $sum: "$amount" } } }
  ],
  explain: "queryPlanner"  // Optional
}

  • count ：统计匹配的文档数量

    {
  collection: "products",
  query: { category: "electronics" }
}

写入操作

  • 更新：修改文档

    {
  collection: "posts",
  filter: { _id: "60d21b4667d0d8992e610c85" },
  update: { $set: { title: "Updated Title" } },
  upsert: false,
  multi: false
}

  • 插入：添加新文档

    {
  collection: "comments",
  documents: [
    { author: "user123", text: "Great post!" },
    { author: "user456", text: "Thanks for sharing" }
  ]
}

  • createIndex ：创建集合索引

    {
  collection: "users",
  indexes: [
    {
      key: { email: 1 },
      unique: true,
      name: "email_unique_idx"
    }
  ]
}

系统操作

  • serverInfo ：获取 MongoDB 服务器详细信息

    {
  includeDebugInfo: true  // Optional
}

调试

由于 MCP 服务器通过 stdio 进行通信，调试起来可能比较困难。使用 MCP 检查器可以更好地了解情况：

npm run inspector

这将提供一个 URL 来访问浏览器中的调试工具。

执照

此 MCP 服务器采用 MIT 许可证。这意味着您可以自由使用、修改和分发该软件，但须遵守 MIT 许可证的条款和条件。更多详情，请参阅项目仓库中的 LICENSE 文件。

Available Tools

8 tools
aggregateB

Execute a MongoDB aggregation pipeline with optional execution plan analysis

ParametersJSON Schema
NameRequiredDescriptionDefault
explainNoOptional: Get aggregation execution information (queryPlanner, executionStats, or allPlansExecution)
pipelineYesAggregation pipeline stages
collectionYesName of the collection to aggregate
objectIdModeNoControl how 24-character hex strings are handledauto

TDQS

B3.2/5.0
Behavior2/5

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

No annotations provided, so description carries full burden. It does not disclose whether the tool is read-only, can write via stages like $merge, or any potential side effects, performance implications, or required permissions.

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

Conciseness5/5

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

Single sentence of 12 words directly states the core functionality. No extraneous information, efficiently front-loads the action and optional feature.

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

Completeness2/5

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

Despite being a complex tool (aggregation pipeline), the description lacks details on output format, error handling, potential performance costs, or the impact of the objectIdMode parameter. No output schema compounds the incompleteness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All four parameters are fully described in the input schema (100% coverage). The description adds minimal extra context, only mentioning 'optional execution plan analysis' which maps to the explain parameter. No further clarification on pipeline construction or objectIdMode behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool executes a MongoDB aggregation pipeline with optional execution plan analysis. It uses specific verb 'execute' and resource 'aggregation pipeline', distinguishing it from sibling tools like query, count, or insert.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives such as 'query' or 'count'. The description does not mention scenarios or limitations, leaving the agent to infer usage context.

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

countC

Count documents in a collection matching a query

ParametersJSON Schema
NameRequiredDescriptionDefault
hintNoIndex hint
skipNoDocs to skip before counting
limitNoMax documents to count
queryNoQuery filter to count
collationNoCollation rules for comparison
maxTimeMSNoMax execution time
collectionYesCollection name
readConcernNoRead concern option
objectIdModeNoControl how 24-character hex strings are handledauto

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only states the basic operation without detailing any side effects (none expected for a count), return format, or constraints (e.g., counting behavior with skip/limit). The agent cannot infer that this is a read-only operation or what the output structure is.

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

Conciseness4/5

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

The description is a single, efficient sentence that directly states the core function. However, it is overly terse and lacks structured details that would aid the agent, such as separating purpose from usage notes. Score reflects conciseness but slight under-information.

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

Completeness2/5

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

Given 9 parameters, no output schema, and no annotations, the description is insufficiently complete. It omits essential information like the return type (a count number), whether the count is approximate or exact, and behavior with optional parameters like skip/limit. A more complete description would provide contextual completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage with descriptions for all 9 parameters. The description adds no additional parameter semantics beyond what is already in the schema. Per guidelines, baseline is 3 when schema coverage is high, and no extra value is provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'count' and the resource 'documents in a collection matching a query', which accurately defines the tool's purpose. However, it does not explicitly distinguish this tool from sibling tools like 'aggregate' or 'query', which could also perform counting.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives (e.g., 'aggregate' for more complex aggregations). There are no examples, prerequisites, or exclusions mentioned, leaving the agent without sufficient context to choose appropriately.

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

createIndexB

Create one or more indexes on a MongoDB collection

ParametersJSON Schema
NameRequiredDescriptionDefault
indexesYesArray of index specifications
collectionYesName of the collection to create indexes on
commitQuorumNoNumber of members required to create the index
objectIdModeNoControl how 24-character hex strings are handledauto
writeConcernNoWrite concern for index creation

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the burden of behavioral disclosure. It only states the action without mentioning side effects like blocking operations, performance implications, or permission requirements. This is inadequate for a mutation tool that can significantly affect database behavior.

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

Conciseness4/5

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

The description is a single concise sentence that is front-loaded with the core action. However, it is extremely brief given the tool's complexity. It earns its place but could benefit from a bit more structure.

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

Completeness2/5

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

With 5 parameters including nested objects and no output schema, the description is insufficient. It does not explain that multiple indexes can be created, the return behavior, or important aspects like background building. The agent lacks key information for correct usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with each parameter having a description. The tool description adds no additional semantics beyond the schema. Baseline 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.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the purpose: creating one or more indexes on a MongoDB collection. The verb 'create' and resource 'indexes on a MongoDB collection' are specific, and the tool is distinct from siblings like 'aggregate' or 'insert' that do not create indexes.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives, or prerequisites for index creation. The description lacks context about when indexes should be created or potential impacts, leaving the agent without decision support.

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

insertB

Insert one or more documents into a MongoDB collection

ParametersJSON Schema
NameRequiredDescriptionDefault
orderedNoIf true, perform ordered insert. If false, insert unordered
documentsYesArray of documents to insert
collectionYesName of the collection to insert into
objectIdModeNoControl how 24-character hex strings are handledauto
writeConcernNoWrite concern for the insert operation
bypassDocumentValidationNoAllow insert to bypass schema validation

TDQS

B3.1/5.0
Behavior2/5

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

No annotations provided, so description must disclose behavior. It lacks details on handling duplicate _id errors, return values, or performance implications. Only states basic insert action.

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

Conciseness4/5

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

One short sentence with no waste, but could be more informative without being verbose. Front-loads the core action.

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

Completeness2/5

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

With 6 parameters including nested objects and no output schema, the description is insufficient. It should mention error handling, return format, and parameter default behaviors.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds no extra meaning beyond the schema; it doesn't explain parameters like ordered, objectIdMode, or writeConcern.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Insert' and the resource 'MongoDB collection', with 'one or more documents' specifying scope. It distinguishes from siblings like query (read) and update (modify) with a specific insert action.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives (e.g., update for modifying, query for reading). No prerequisites or context about required permissions or collection existence.

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

listCollectionsB

List all collections in the MongoDB database

ParametersJSON Schema
NameRequiredDescriptionDefault
filterNoFilter for collections
nameOnlyNoIf true, return only collection names
objectIdModeNoControl how 24-character hex strings are handledauto

TDQS

B3.1/5.0
Behavior2/5

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

No annotations provided, so the description carries full burden. It describes 'list' implying read-only, but fails to disclose behavior like pagination, authentication requirements, or how parameters like 'objectIdMode' affect execution.

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

Conciseness4/5

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

A single concise sentence that gets to the point. However, it could front-load more critical information like the database context, but for a simple tool it is appropriately sized.

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

Completeness2/5

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

No output schema exists, and the description does not explain return values (e.g., list of names vs full documents). The 'nameOnly' parameter hints at different outputs, but this is not clarified. For a list tool with multiple parameters, more detail is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema; it simply restates the tool's purpose without detailing parameter usage or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific verb 'List' and resource 'collections in the MongoDB database', which distinguishes it from sibling tools like 'aggregate' or 'insert' that perform different operations.

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

Usage Guidelines2/5

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

No guidance on when to use this tool vs alternatives. It does not mention contexts where other tools like 'query' might be more appropriate, nor any conditions for using this tool.

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

queryB

Execute a MongoDB query with optional execution plan analysis

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of documents to return
filterNoMongoDB query filter
explainNoOptional: Get query execution information
collectionYesName of the collection to query
projectionNoFields to include/exclude
objectIdModeNoControl how 24-character hex strings are handledauto

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description must disclose behaviors. It only states execution of a query, but does not specify if this is read-only, side effects, permission needs, or performance implications.

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

Conciseness5/5

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

Single sentence that is direct and front-loaded with the core action. No unnecessary words, and the optional analysis is mentioned immediately.

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

Completeness2/5

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

Despite having 6 parameters and no output schema, the description does not explain return values, pagination, or behavior for required parameters. It is too minimal for a complex MongoDB query operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so each parameter is already documented. The description adds minor value by linking 'execution plan analysis' to the explain parameter, but does not provide additional semantic context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool executes a MongoDB query with optional execution plan analysis. The verb 'Execute' and resource 'MongoDB query' are specific, and the mention of execution plan analysis distinguishes it from basic query tools.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus siblings like aggregate or count. The description does not mention appropriate scenarios, prerequisites, or exclusions.

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

serverInfoA

Get MongoDB server information including version, storage engine, and other details

ParametersJSON Schema
NameRequiredDescriptionDefault
includeDebugInfoNoInclude additional debug information about the server

TDQS

A3.7/5.0
Behavior3/5

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

No annotations exist, so the description carries full burden. It correctly implies a read-only, non-destructive operation but does not disclose any specific behavioral traits such as authentication needs or rate limits. The effect of the optional parameter is not elaborated beyond the schema.

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

Conciseness4/5

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

The description is a single, efficient sentence that conveys the core purpose. It is front-loaded and concise, though it could be slightly more structured.

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

Completeness4/5

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

For a simple info tool with one optional parameter and no output schema, the description is mostly complete, stating the type of information returned. It lacks details about return format or explicit read-only guarantee, but is still adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the parameter already has a description. The tool description adds no additional meaning about the parameter beyond what the schema provides, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets MongoDB server information and lists examples like version and storage engine, distinguishing it from sibling tools that perform data operations (query, insert, etc.).

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

Usage Guidelines3/5

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

The description gives a clear purpose but does not explicitly state when or when not to use this tool versus alternatives. Context from sibling tools suggests usage, but no direct guidance is provided.

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

updateC

Update documents in a MongoDB collection

ParametersJSON Schema
NameRequiredDescriptionDefault
multiNoUpdate multiple documents that match the filter
filterYesFilter to select documents to update
updateYesUpdate operations to apply ($set, $unset, $inc, etc.)
upsertNoCreate a new document if no documents match the filter
collectionYesName of the collection to update
objectIdModeNoControl how 24-character hex strings are handledauto

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations, the description solely must clarify behavior. It only states the operation type (update) but omits details like whether the tool returns the updated document, handles no-matches, or requires authentication. The rich schema parameters (multi, upsert) are not elaborated beyond their definitions.

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

Conciseness3/5

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

The description is a single short sentence, which is concise but at the cost of informative detail. It does not front-load key behavioral cues (e.g., upsert support) and is thus minimal rather than optimally structured.

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

Completeness2/5

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

For a tool with 6 parameters, nested objects, and no output schema, the description lacks completeness. It does not explain return values, error scenarios, or behavior of multi/upsert combinations, leaving significant gaps for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so each parameter has a description. The tool description adds no extra parameter context, meeting the baseline for high coverage. However, no additional semantic elaboration is provided for complex parameters like 'update' (MongoDB operators) or 'objectIdMode'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool updates documents in a MongoDB collection using a clear verb-resource structure. However, it does not distinguish this tool from siblings like 'insert' (creates) or 'query' (reads), leaving some ambiguity about the specific operation scope.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives such as 'insert' for new documents or 'aggregate' for transformations. There is no mention of prerequisites, idempotency, or context-specific conditions.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 8 tool updatesv1.0.0
    • First observedaggregate
    • First observedcount
    • First observedcreateIndex
    • First observedinsert
    • First observedlistCollections
    • First observedquery
    • First observedserverInfo
    • First observedupdate
Install Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.4/5.0
Disambiguation5/5

Each tool targets a distinct database operation: aggregation, counting, indexing, inserting, listing collections, querying, server info, and updating. No two tools have overlapping purposes.

Naming Consistency5/5

All tool names use lowercase with camelCase for multi-word terms (e.g., 'createIndex', 'listCollections', 'serverInfo'), following a consistent pattern of verb or verb_noun.

Tool Count5/5

8 tools cover core MongoDB operations (CRUD, aggregation, indexing, metadata) without being too few or excessive for a general-purpose database server.

Completeness2/5

The set lacks a tool for deleting documents or collections, which is a fundamental operation. Without 'delete' or 'remove', agents cannot complete typical data lifecycle actions, leaving a significant gap.

Maintenance

ActivitySlowing
ResponsivenessSlow

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • MongoDB

    QuantGeekDev
    A
    license
    Not graded
    quality
    F
    maintenance
    A Model Context Protocol (MCP) server that enables LLMs to interact directly with MongoDB databases. Query collections, inspect schemas, and manage data seamlessly through natural language.
    22
    175
    MIT

  • MongoDB MCP Server for LLMs

    nickiiitu
    A
    license
    Not graded
    quality
    D
    maintenance
    A Model Context Protocol server that enables LLMs to interact directly with MongoDB databases, allowing users to query collections, inspect schemas, and manage data through natural language.
    22
    2
    MIT

  • MongoDB MCP Server for LLMs

    vivek1612
    A
    license
    Not graded
    quality
    D
    maintenance
    A Model Context Protocol server that enables LLMs to interact directly with MongoDB databases, allowing users to query collections, inspect schemas, and manage data through natural language.
    22
    MIT

  • MCP Database Server

    manpreet2000
    A
    license
    D
    quality
    D
    maintenance
    A Model Context Protocol server that enables LLMs to interact with databases (currently MongoDB) through natural language, supporting operations like querying, inserting, deleting documents, and running aggregation pipelines.
    5
    14
    MIT

Appeared in Searches

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/kiliczsh/mcp-mongo-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server