Todoist Python MCP Server
Todoist MCP 服务器
一个模型上下文协议 (MCP) 服务器,允许像 Claude 这样的客户端与 Todoist 交互,从而通过自然语言实现任务管理功能。该服务器充当客户端和 Todoist API 之间的中介,处理身份验证、数据转换和命令处理。这是一个 Python 版本
特征
任务创建:创建具有必需内容和可选属性的新任务
任务检索:通过 ID 获取任务或使用过滤选项列出任务
任务管理:更新任务属性、标记任务完成、删除任务
Related MCP server: Todoist MCP Server
先决条件
Python 3.12
紫外线
Todoist 帐户和 API 令牌
如何获取 Todoist API 令牌
登录您的 Todoist 账户
前往用户设置 -> 集成 -> 开发者
复制 API 令牌
与 Claude Desktop 一起使用
通过 UVX 运行(无需克隆)
您可以使用 UVX 直接从 GitHub 运行服务器:
uvx --from https://github.com/Johnxjp/todoist-mcp-python.git mcp-server-todoist然后将此配置添加到您的 Claude 设置中:
{
"mcpServers": {
"todoist-server": {
"command": "uvx",
"args": [
"--from",
"https://github.com/Johnxjp/todoist-mcp-python.git",
"mcp-server-todoist"
],
"env": {
"TODOIST_API_TOKEN": "YOUR_API_TOKEN"
}
}
}
}从克隆的存储库运行
如果您希望克隆存储库,请使用以下命令:
git clone git@github.com:Johnxjp/todoist-mcp-python.git然后添加到您的 Claude 配置文件:
{
"mcpServers": {
"todoist-server": {
"command": "uv",
"args": [
"run",
"--with",
"mcp[cli]",
"--with",
"todoist_api_python",
"mcp",
"run",
"/full/path/to/todoist_server.py"
],
"env": {
"TODOIST_API_TOKEN": "YOUR_API_TOKEN"
}
}
}
}可用工具
服务器为Claude提供了以下工具供使用:
create_task :在 Todoist 中创建新任务
必填:内容(任务标题)
选修的:
描述,
到期日,
优先事项,
项目ID,
部分 ID,
标签
get_tasks :使用各种过滤器从 Todoist 获取任务和 ID 列表
选修的:
项目ID,
项目名称,
任务名称,
优先事项,
标签,
已过期,
限制
update_task :通过名称搜索来更新现有任务
必填:task_id
选修的:
内容,
描述,
标签,
优先事项,
到期日(YYYY-MM-DD),
截止日期(YYYY-MM-DD)
delete_task :通过名称搜索来删除任务
必填:task_id
complete_task :通过名称搜索将任务标记为完成
必填:task_id
交互示例
以下是 Claude 如何通过此 MCP 服务器与 Todoist 交互的一些示例:
“添加购买杂货的任务”
“显示我所有的紧急任务”
“今天有哪些任务要完成?”
“将洗衣任务标记为已完成”
“将我的牙医预约优先级更改为紧急”
安全注意事项
服务器通过环境变量安全地处理您的 Todoist API 令牌
切勿共享您的
.env文件或暴露您的 API 令牌服务器在本地运行,仅与 Todoist API 通信
执照
致谢
Available Tools
6 toolscomplete_taskC
Mark a task as done
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes |
TDQS
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. 'Mark a task as done' implies a mutation (changing task status), but it doesn't specify permissions required, whether the change is reversible, side effects (e.g., notifications), or error handling. It lacks details on what 'done' entails operationally, leaving behavioral traits unclear.
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's front-loaded and appropriately sized for a simple tool, avoiding unnecessary elaboration while stating the core action clearly.
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 mutation nature, lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't cover behavioral aspects like side effects, return values, or error conditions, leaving significant gaps for an agent to operate effectively in context with siblings like 'update_task'.
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 0%, so the description must compensate for undocumented parameters. The description mentions 'task' but doesn't explain the 'task_id' parameter's semantics, such as format, source, or validation rules. It adds minimal meaning beyond the schema, failing to address the coverage gap 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 'Mark a task as done' clearly states the action (marking) and resource (task), but it's somewhat vague about what 'done' means (completed, archived, etc.). It distinguishes from siblings like 'create_task' and 'delete_task' by focusing on status change rather than creation or removal, but doesn't explicitly differentiate from 'update_task' which might also handle status updates.
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. The description doesn't mention prerequisites (e.g., task must exist), when not to use it (e.g., for partial completion), or direct alternatives like 'update_task' for more complex modifications. The agent must infer usage from 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.
create_taskB
Create a new task
Args:
- content [str]: Task content. This value may contain markdown-formatted text and hyperlinks. Details on markdown support can be found in the Text Formatting article in the Help Center.
- description [str]: A description for the task. This value may contain markdown-formatted text and hyperlinks. Details on markdown support can be found in the Text Formatting article in the Help Center.
- project_id [str]: The ID of the project to add the task. If none, adds to user's inbox by default.
- labels [list[str]]: The task's labels (a list of names that may represent either personal or shared labels).
- priority [int]: Task priority from 1 (normal) to 4 (urgent).
- due_date [str]: Specific date in YYYY-MM-DD format relative to user’s timezone.
- section_id [str]: The ID of the section to add the task to
Returns:
- task_id: str:
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | ||
| description | No | ||
| project_id | No | ||
| labels | No | ||
| priority | No | ||
| due_date | No | ||
| section_id | No |
TDQS
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. While 'Create a new task' implies a write/mutation operation, the description doesn't address important behavioral aspects like authentication requirements, error conditions, rate limits, whether the operation is idempotent, or what happens with duplicate tasks. The description mentions markdown support but doesn't explain behavioral implications.
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 well-structured with clear sections for Args and Returns, making it easy to parse. While somewhat lengthy due to detailed parameter explanations, every sentence adds value. The front-loaded purpose statement is clear, though the parameter details could be more efficiently organized.
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 a 7-parameter mutation tool with no annotations and no output schema, the description does a good job with parameter semantics but falls short on behavioral context. It explains what parameters do but not how the tool behaves overall. The return value documentation is minimal ('task_id: str'), leaving the agent uncertain about the full response structure or potential errors.
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 description provides excellent parameter semantics that significantly enhance the 0% schema description coverage. For all 7 parameters, it explains their purpose, format constraints (e.g., 'YYYY-MM-DD format relative to user's timezone'), allowed values (e.g., 'priority from 1 (normal) to 4 (urgent)'), and default behaviors (e.g., 'adds to user's inbox by default'). This goes far beyond what the bare schema provides.
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 'Create a new task' which is a specific verb+resource combination. However, it doesn't distinguish this tool from its sibling 'update_task' which might also create tasks in some contexts, nor does it mention what makes this creation operation distinct from other task-related 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 description provides no guidance on when to use this tool versus alternatives like 'update_task' or 'complete_task'. There's no mention of prerequisites, when this tool is appropriate versus other task management approaches, or any contextual limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_taskC
Delete a task by its ID
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes |
TDQS
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 'Delete' which implies a destructive mutation, but doesn't clarify if deletion is permanent, reversible, requires specific permissions, or has side effects (e.g., affecting related data). This is a significant gap for a mutation tool with zero 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, direct sentence with zero waste—it states the action and key parameter concisely. It's appropriately sized for a simple tool and front-loaded with essential information, making it efficient for quick understanding.
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 destructive nature, lack of annotations, no output schema, and incomplete parameter documentation (0% coverage), the description is inadequate. It doesn't address behavioral risks, usage context, or expected outcomes, leaving critical gaps for safe and effective tool invocation by 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?
The description mentions 'by its ID', which aligns with the single parameter 'task_id' in the schema. However, with 0% schema description coverage, the schema provides no details about the ID format or constraints. The description adds minimal value by implying the parameter's role but doesn't compensate for the coverage gap with specifics like ID syntax or examples.
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 ('Delete') and target resource ('a task by its ID'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'complete_task' or 'update_task' which also modify tasks, missing an opportunity to clarify its specific destructive nature versus those 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?
The description provides no guidance on when to use this tool versus alternatives like 'complete_task' or 'update_task', nor does it mention prerequisites (e.g., task must exist) or consequences. It merely states what it does without context, leaving the agent to infer usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_projectsB
Get all todo projects. These are like folders for tasks in Todoist
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
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 'Get all todo projects' but doesn't disclose behavioral traits such as whether it requires authentication, rate limits, pagination, or the format of the return data. The analogy adds some context but lacks operational details.
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 two short sentences with zero waste. It front-loads the purpose ('Get all todo projects') and adds a clarifying analogy efficiently. Every sentence earns its place by enhancing understanding 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 has no parameters and no output schema, the description is moderately complete. It explains what the tool does and provides an analogy, but without annotations or output schema, it lacks details on behavior, return format, or error handling. It's adequate for a simple read operation but has gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. Baseline for 0 parameters is 4, as it avoids unnecessary information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get all') and resource ('todo projects'), with a helpful analogy ('like folders for tasks in Todoist') that clarifies the concept. However, it doesn't explicitly differentiate from sibling tools like 'get_tasks', which retrieves tasks rather than projects.
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 should be used for listing all projects versus filtering or if there are prerequisites like authentication. 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.
get_tasksA
Fetch user's tasks. These can be filtered by project, labels, time, etc. If no filters are provided, all tasks are returned.
Args:
- project_id: The string ID of the project to fetch tasks from. Example '1234567890'
- project_name: Name of the project to fetch tasks from. Example 'Work' or 'Inbox'
- task_name: Filter tasks by name. Example 'Buy groceries'
- labels: List of tags used to filter tasks.
- priority: Filter tasks by priority level. 4 (urgent), 3 (high), 2 (normal), 1 (low)
- due_date: Specific due date in YYYY-MM-DD format. Example '2021-12-31'
- is_overdue: Filter tasks that are overdue.
- limit: Maximum number of tasks to return. Default is all.
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | No | ||
| project_name | No | ||
| task_name | No | ||
| labels | No | ||
| due_date | No | ||
| is_overdue | No | ||
| priority | No | ||
| limit | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers limited behavioral insight. It mentions the tool fetches tasks and describes filtering logic, but doesn't disclose critical traits like authentication needs, rate limits, pagination behavior, error handling, or whether it's read-only (implied but not stated). The description doesn't contradict annotations, but fails to compensate for their absence.
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 well-structured with a concise opening sentence followed by a detailed parameter list. Every sentence adds value, and it's appropriately sized for an 8-parameter tool. Minor room for improvement in front-loading key behavioral details, but overall efficient and clear.
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 (8 parameters, no annotations, no output schema), the description is partially complete. It excels in parameter documentation but lacks output format details, error conditions, and behavioral context. For a read operation with many filters, more guidance on result structure and limitations would enhance 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 description adds substantial meaning beyond the input schema, which has 0% description coverage. It provides clear explanations for all 8 parameters, including examples (e.g., 'Example '1234567890''), format specifications ('YYYY-MM-DD format'), enumerated values ('4 (urgent), 3 (high)...'), and default behavior ('Default is all'). This fully compensates for the schema's lack of descriptions.
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 'Fetch user's tasks' (verb+resource). It distinguishes from siblings like create_task or delete_task by being a read operation, though it doesn't explicitly contrast with get_projects. The filtering scope is well-defined, making the purpose specific and actionable.
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 through the filtering context ('filtered by project, labels, time, etc.') and notes that 'If no filters are provided, all tasks are returned.' However, it lacks explicit guidance on when to use this tool versus alternatives like get_projects or search-oriented siblings, and doesn't mention prerequisites or error conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_taskB
Update an attribute of a task given its ID. Any attribute can be updated.
Args:
- task_id [str | int]: The ID of the task to update. Example '1234567890' or 1234567890
- content [str]: Task content. This value may contain markdown-formatted text and hyperlinks. Details on markdown support can be found in the Text Formatting article in the Help Center.
- description [str]: A description for the task. This value may contain markdown-formatted text and hyperlinks. Details on markdown support can be found in the Text Formatting article in the Help Center.
- labels [list[str]]: The task's labels (a list of names that may represent either personal or shared labels).
- priority [int]: Task priority from 1 (normal) to 4 (urgent).
- due_date [str]: Specific date in YYYY-MM-DD format relative to user’s timezone.
- deadline_date [str]: Specific date in YYYY-MM-DD format relative to user’s timezone.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | ||
| content | No | ||
| description | No | ||
| labels | No | ||
| priority | No | ||
| due_date | No | ||
| deadline_date | No |
TDQS
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 an update operation (implying mutation) but doesn't mention permission requirements, whether updates are partial or complete, what happens with null values, or error behavior. The description adds some context about markdown support in content/description fields, but lacks critical behavioral details for a mutation 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 appropriately sized but not optimally structured. The opening statement is clear, but the parameter documentation could be more concise. Some information (like the repeated markdown support note) is redundant. However, it's not excessively verbose and the parameter explanations are useful.
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 this is a mutation tool with 7 parameters, no annotations, and no output schema, the description is moderately complete. It excels at parameter documentation but lacks behavioral context (permissions, error handling, update semantics) and output information. For a tool that modifies data, more behavioral transparency 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?
The description provides extensive parameter semantics beyond the schema, which has 0% description coverage. For each parameter, it explains: data types (str/int for task_id), format examples, markdown support for content/description, label types, priority range (1-4), and date formats with timezone context. This fully compensates for the schema's lack of descriptions.
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 purpose: 'Update an attribute of a task given its ID. Any attribute can be updated.' This specifies the verb ('update'), resource ('task'), and scope ('any attribute'). However, it doesn't explicitly distinguish this from sibling tools like 'complete_task' or 'delete_task' beyond the basic action difference.
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 like 'complete_task' or 'create_task'. It doesn't mention prerequisites (e.g., needing an existing task ID), error conditions, or typical use cases. The only implied usage is when you need to modify task attributes.
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.
6 tool updates
- First observed
complete_task - First observed
create_task - First observed
delete_task - First observed
get_projects - First observed
get_tasks - First observed
update_task
TDQS
Scored across 6 tools
Each tool has a clearly distinct purpose with no overlap: complete_task marks tasks as done, create_task adds new tasks, delete_task removes tasks, get_projects retrieves projects, get_tasks fetches tasks with filtering, and update_task modifies existing tasks. The boundaries are well-defined and unambiguous.
All tool names follow a consistent verb_noun pattern in snake_case: complete_task, create_task, delete_task, get_projects, get_tasks, update_task. This uniformity makes the tool set predictable and easy to understand at a glance.
With 6 tools, this server is well-scoped for a Todoist integration, covering core task management operations (create, read, update, delete, complete) and project listing. Each tool earns its place without being overwhelming or insufficient for the domain.
The tool set provides excellent coverage for task CRUD operations and project listing, enabling agents to manage tasks effectively. A minor gap exists in project management (e.g., no create_project or delete_project tools), but agents can still perform core workflows without significant hindrance.
Maintenance
Related MCP Connectors
- mcpOAuthnet.todoist
Official Todoist MCP server for AI assistants to manage tasks, projects, and workflows.
Persistent context for Claude. Your AI always knows your projects and next actions across sessions.
Model Context Protocol server for todo.vu task management and time tracking.
Create projects, nodes, and tasks in UluP Spaces by conversation with Claude.
Related MCP Servers
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- AlicenseAqualityDmaintenanceA Python server that enables Claude to interact with Todoist's task management API, allowing users to create, manage, and organize tasks directly through Claude's interface.179MIT
- AlicenseNot gradedqualityFmaintenanceA Model Context Protocol server that enables advanced task and project management in Todoist via Claude Desktop and other MCP-compatible clients.334 npm2MIT
- AlicenseBqualityDmaintenanceProvides Todoist task management capabilities to AI assistants through the Model Context Protocol. Enables users to interact with their Todoist projects and tasks directly through Claude Desktop using natural language commands.33189 npmMIT