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., "@sloppybee Create a packing list for a week-long beach vacation"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP (Model Context Protocol) server that enables Claude Desktop and other MCP-compatible clients to create and manage shareable task lists using the Sloppybee API.
create_list - Create shareable task lists with optional AI-generated tasks
get_list - Retrieve existing lists by slug
generate_tasks - Generate task suggestions without creating a list
Related MCP server: Google Tasks MCP Server
Node.js 18 or higher
A Sloppybee API key (Get one here)
npm install -g @sloppybee/mcp-servergit clone https://github.com/sloppybee/mcp-server
cd mcp-server
npm install
npm run buildAdd to your Claude Desktop configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"sloppybee": {
"command": "npx",
"args": ["@sloppybee/mcp-server"],
"env": {
"SLOPPYBEE_API_KEY": "sb_live_your_api_key_here"
}
}
}
}On Windows, use npx.cmd instead of npx:
{
"mcpServers": {
"sloppybee": {
"command": "npx.cmd",
"args": ["@sloppybee/mcp-server"],
"env": {
"SLOPPYBEE_API_KEY": "sb_live_your_api_key_here"
}
}
}
}If installed globally with npm install -g @sloppybee/mcp-server:
macOS/Linux:
{
"mcpServers": {
"sloppybee": {
"command": "sloppybee-mcp",
"env": {
"SLOPPYBEE_API_KEY": "sb_live_your_api_key_here"
}
}
}
}Windows:
{
"mcpServers": {
"sloppybee": {
"command": "sloppybee-mcp.cmd",
"env": {
"SLOPPYBEE_API_KEY": "sb_live_your_api_key_here"
}
}
}
}The server uses stdio transport and expects the SLOPPYBEE_API_KEY environment variable to be set.
Once configured, you can ask Claude to create task lists:
"Create a packing list for a week-long beach vacation"
"Make a shopping list for Thanksgiving dinner for 12 people"
"Generate task suggestions for planning a birthday party"
Create a new shareable task list.
Parameters:
title (required): Title of the list
tasks: Array of tasks with task and optional comment
ai_generate: Set to true for AI-generated tasks
ai_prompt: Prompt for AI generation (required if ai_generate is true)
expires_in_days: Days until expiration (default: 7, max: 30)
Example:
{
"title": "Beach Vacation Packing",
"ai_generate": true,
"ai_prompt": "Essential items for a week at the beach"
}Retrieve an existing list.
Parameters:
slug (required): The list slug from the URL
password: Password if list is protected
Generate task suggestions without creating a list.
Parameters:
prompt (required): Description of tasks to generate
existing_tasks: Tasks to avoid duplicating
count: Number of tasks (default: 10, max: 50)
# Install dependencies
npm install
# Run in development mode
npm run dev
# Build for production
npm run build
# Run production build
npm startFree tier: 100 requests/hour
Contact support@sloppybee.com for higher limits
MIT
Create a new shareable task list. Can use AI to generate tasks or accept manual tasks.
| Name | Required | Description | Default |
|---|---|---|---|
| tasks | No | Array of tasks (use this OR ai_generate, not both) | |
| title | Yes | Title of the task list | |
| ai_prompt | No | Prompt for AI task generation (required if ai_generate is true) | |
| ai_generate | No | Set to true to use AI to generate tasks based on ai_prompt | |
| expires_in_days | No | Number of days until the list expires (default: 7, max: 30) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral disclosure burden. It mentions creation and shareability but does not explain side effects, authentication requirements, rate limits, or what the tool returns. For a mutation tool, 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, well-structured sentence that conveys the core functionality without unnecessary words. Every phrase adds value, earning a perfect score 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?
The schema is rich and fully documented, but the description lacks guidance on return values (no output schema) and does not clarify the mutual exclusion between tasks and ai_generate, which is only hinted at in the schema. It is adequate for basic understanding but not fully complete given the tool's complexity.
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 provides 100% coverage of all five parameters with clear descriptions. The tool description adds minor context by referencing the two modes (manual tasks vs. AI generation) but does not meaningfully enhance parameter understanding beyond the schema, so the baseline 3 is appropriate.
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 creates a new shareable task list and distinguishes between AI-generated and manually provided tasks. It is specific enough to differentiate from siblings like get_list (read) and generate_tasks (task generation 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?
The description implies usage for creating a list with either manual or AI‑generated tasks, but does not explicitly state when to prefer this over generate_tasks or get_list. No exclusion criteria or alternative guidance is provided, so it falls into implied usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate task suggestions using AI without creating a list. Useful for brainstorming.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | Number of tasks to generate (default: 10, max: 50) | |
| prompt | Yes | Description of what tasks to generate | |
| existing_tasks | No | Existing tasks to avoid duplicating |
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 does disclose the non-mutating nature ('without creating a list') and that AI is involved, but it omits output format, non-determinism, latency, and error behavior. This is insufficient for a generation 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, front-loaded sentence with no filler. 'Useful for brainstorming' adds usage context, though not essential, keeping it concise and readable.
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?
With no output schema and no annotations, the description does not explain what the tool returns, how to interpret results, or potential caveats. For an AI suggestion tool with three parameters, this is an incomplete picture.
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 adds no extra parameter-specific meaning beyond what the schema already provides for prompt, count, and existing_tasks.
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 generates task suggestions using AI without creating a list, using a specific verb and object. It distinguishes itself from siblings create_list and get_list by explicitly noting it does not create a list.
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 phrase 'Useful for brainstorming' gives a clear use case, and 'without creating a list' indirectly differentiates it from create_list. However, it does not explicitly mention when not to use it or name alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve an existing task list by its slug/URL identifier
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The list slug (from the URL, e.g., 'bbq-party-abc123') | |
| password | No | Password if the list is password-protected |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for disclosing behavior. It only states that the tool retrieves an existing list, which implies a read-only operation, but it does not mention any side effects, error conditions, authentication requirements, or how the password parameter is used. This leaves significant behavioral ambiguity 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, concise sentence that states the verb, resource, and method in a direct manner. Every word contributes meaning, and there is no redundant or filler 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?
Given the simple nature of the tool (read by slug) and abundant schema coverage, the description is reasonably complete for basic usage. However, with no output schema and no annotations, it does not explain what the response looks like, what happens if the list is not found, or the role of the optional password in protecting the list, leaving some gaps for 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?
The input schema provides 100% description coverage for both parameters (slug and password), so the schema fully documents what each parameter means. The description adds a minor clarification by saying "by its slug/URL identifier," reinforcing the slug parameter's purpose, but it adds no semantics beyond the schema. Baseline 3 applies.
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 ("Retrieve") and resource ("existing task list") with a clear identifier method ("by slug/URL identifier"). This clearly distinguishes it from sibling tools like create_list (which creates) and generate_tasks (which generates), making the purpose unmistakable.
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 the tool is for fetching an existing list by slug, which is a clear usage context. However, it does not explicitly mention when to avoid using it or compare it with alternatives like create_list or generate_tasks. Since the context is clear but no exclusions or alternatives are named, it stops short of a 5.
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. Dates show when Glama detected each change.
v1.0.0create_listgenerate_tasksget_listScored across 3 tools
Each tool serves a distinct purpose: create_list creates a new list, get_list retrieves an existing one, and generate_tasks provides standalone task brainstorming without list creation. While create_list can also generate tasks, the descriptions clearly separate the standalone generation use case.
All tool names follow a consistent verb_noun pattern: create_list, get_list, generate_tasks. The style is uniformly lowercase with underscores, making the set predictable and easy to navigate.
With only 3 tools, the set is minimal but appropriate for a focused task-list service focused on creation and retrieval. It slightly underutilizes the potential for a full CRUD surface, but the count is still within a reasonable range.
The domain implies task list management, yet the tools only support create and get operations. Missing update, delete, and list operations represent significant gaps that would prevent an agent from fully managing lists, leading to likely failures for common workflows.
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