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., "@mcp-task-tools Prioritize my tasks using RICE scoring"
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.
MCP server providing task and project management tools for AI agents.
In-memory task list management. Add, complete, update status, delete, and list tasks with filtering by status, priority, and tags. Data persists within the session.
Sort tasks using established prioritization frameworks:
Eisenhower Matrix — categorize by urgent/important into four quadrants (Do First, Schedule, Delegate, Eliminate)
RICE Scoring — rank by Reach, Impact, Confidence, and Effort
MoSCoW Method — classify as Must Have, Should Have, Could Have, or Won't Have
Estimate task duration using PERT methodology. Break work into subtasks, provide optimistic/realistic/pessimistic estimates, and get weighted expected time with configurable buffer.
Generate daily standup updates from the current task list. Outputs yesterday (completed), today (in progress), and blockers with summary statistics.
Calculate sprint burndown data. Given tasks with story points and sprint duration, computes ideal vs actual burndown, velocity metrics, and completion forecast.
Related MCP server: Todo for AI MCP Server
npm install
npm run buildAdd to your Claude Desktop config:
{
"mcpServers": {
"task-tools": {
"command": "node",
"args": ["path/to/mcp-task-tools/dist/index.js"]
}
}
}MIT
Calculate sprint burndown chart data. Given tasks with story points and sprint duration, computes ideal vs actual burndown, velocity metrics, and completion forecast.
| Name | Required | Description | Default |
|---|---|---|---|
| sprint_name | No | Name of the sprint | |
| tasks | Yes | Sprint tasks with points and completion data | |
| sprint_duration_days | Yes | Total sprint duration in days | |
| current_day | Yes | Current day of the sprint (0-indexed) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden. It discloses that the tool computes ideal vs actual burndown, velocity, and forecast, suggesting read-only calculation. However, it does not confirm side effects (e.g., if it stores data) or mention permissions.
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?
Two sentences, front-loaded with purpose, no redundant words. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, and the description does not describe the return format or structure of the burndown data, velocity, or forecast. This leaves ambiguity for a complex calculation output.
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 parameters have descriptions in the schema (100% coverage), so the baseline is 3. The description adds context that parameters relate to burndown calculation but does not enhance syntax or format details beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Calculate sprint burndown chart data' with specific outputs (ideal vs actual burndown, velocity metrics, completion forecast). It distinguishes from sibling tools like priority_sorter and standup_generator.
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 context (tasks with story points and sprint duration) but does not explicitly state when to use this tool vs alternatives like time_estimator or todo_manager. No when-not or exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Sort and prioritize tasks using proven frameworks: Eisenhower matrix (urgent/important quadrants), RICE scoring (reach, impact, confidence, effort), or MoSCoW method (must/should/could/wont).
| Name | Required | Description | Default |
|---|---|---|---|
| method | Yes | Prioritization framework to use | |
| tasks | Yes | Tasks to prioritize. Fields depend on method: eisenhower needs urgent/important booleans; rice needs reach/impact/confidence/effort numbers; moscow needs moscow category string. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes input methods but does not disclose output behavior (e.g., whether tasks are reordered, returned with scores, or grouped). This lack of transparency could confuse an AI 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 sentence that effectively conveys three method options and their requirements. It is front-loaded and concise, though could benefit from bullet points for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given two required parameters and full schema coverage, the description is adequate but missing return value information. As there is no output schema, the description should explain what the tool returns (e.g., sorted list, scores) to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for each parameter and their method-specific fields. The description adds a high-level summary but does not provide meaningful extra detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's core purpose: 'Sort and prioritize tasks using proven frameworks'. It lists three specific frameworks (Eisenhower, RICE, MoSCoW), distinguishing it from sibling tools like standup_generator or time_estimator.
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 for prioritization tasks but does not provide explicit guidance on when to use this tool vs alternatives, nor does it specify criteria for choosing among the three methods.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a daily standup update from the current task list. Shows yesterday (recently completed), today (in progress), and blockers. Uses data from todo_manager.
| Name | Required | Description | Default |
|---|---|---|---|
| lookback_hours | No | Hours to look back for completed tasks (default: 24) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It states the tool generates a standup update from the task list, implying a read operation, but does not explicitly disclose side effects, authentication needs, or limitations. The behavior is implied but not fully detailed.
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 sentences, front-loaded with the primary function, and contains no redundant information. Every word serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter, no output schema), the description adequately explains what the tool does and the structure of its output. It misses details like behavior when no tasks exist, but is largely complete for its 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 parameter 'lookback_hours' is fully described in the schema (100% coverage), so the description does not add new meaning. Baseline is 3 per rubric.
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 it generates a daily standup update from the current task list, specifying the three sections (yesterday, today, blockers) and the data source (todomanager). This is specific and distinguishes it from sibling tools like priority_sorter and burndown_calculator.
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 it's for generating standups, but does not explicitly compare to siblings or provide exclusions. However, the context is clear that it's used for daily progress reporting, and no misleading guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Estimate task duration using PERT methodology. Break a task into subtasks, provide optimistic/realistic/pessimistic estimates for each. Returns weighted expected time plus configurable buffer.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | Name of the overall task | |
| subtasks | Yes | Subtasks with time estimates | |
| buffer_percent | No | Buffer percentage to add (default: 20) | |
| unit | No | Time unit: hours, days, weeks (default: hours) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It clearly indicates the tool is a computational estimator (non-destructive) and describes return values. However, it does not mention authentication, rate limits, or whether it modifies any state. For a calculation tool, this is acceptable but not exhaustive.
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?
Two sentences effectively convey purpose, methodology, inputs, and outputs. No redundant information; every phrase contributes meaning. The structure is front-loaded with the core action and follows with essential details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description adequately mentions return values. It covers key parameters and methodology. However, it could briefly note that estimates should be positive numbers or that the PERT formula is used, but this is minor. Overall, it is sufficiently complete for a tool with moderate 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?
Schema description coverage is 100%, providing baseline. The description adds context about PERT methodology and configurable buffer, which enhances understanding beyond field descriptions. It explains the purpose of optimistic/realistic/pessimistic estimates and the buffer parameter, adding 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 clearly states the tool estimates task duration using PERT methodology, specifying inputs (subtasks with optimistic/realistic/pessimistic estimates) and outputs (weighted expected time plus buffer). It distinguishes itself from sibling tools like burndown_calculator or priority_sorter by focusing on time estimation rather than tracking or sorting.
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 estimating task durations with PERT but does not explicitly state when to use this tool versus alternatives or provide exclusions. It lacks guidance on prerequisites or scenarios where this tool is inappropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Manage an in-memory task list. Add, complete, update status, delete, and list tasks. Supports filtering by status, priority, and tags. Persistent within session.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | Action to perform on the task list | |
| id | No | Task ID (required for complete, update_status, delete) | |
| title | No | Task title (required for add) | |
| description | No | Task description (optional, for add) | |
| status | No | Task status (for update_status or list filter) | |
| priority | No | Task priority (for add or list filter) | |
| points | No | Story points for the task (default: 1) | |
| tags | No | Tags for the task (for add) | |
| tag | No | Filter by tag (for list) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It notes 'in-memory' and 'persistent within session', which adds value, but does not disclose behavior beyond schema (e.g., side effects, error handling, auth needs). Behavior is adequate but not comprehensive.
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?
Two sentences, front-loaded with purpose, no redundant 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?
With 9 parameters and no output schema, the description should explain return values. It covers core actions and filtering but omits the structure of results, leaving agents uncertain about what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds context about filtering but does not explain parameter relationships or usage beyond what the schema already 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 the tool manages an in-memory task list and lists specific operations (add, complete, update status, delete, list). It distinguishes from sibling tools like burndown_calculator, which are analytical, making the purpose unambiguous.
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 for basic CRUD on tasks but does not explicitly state when to use this tool versus alternatives. No guidance on exclusions or prerequisites is provided.
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.
v1.0.0burndown_calculatorpriority_sorterstandup_generatortime_estimatortodo_managerEach tool has a clearly distinct purpose: todo_manager for CRUD, priority_sorter for frameworks, time_estimator for duration, burndown_calculator for sprint metrics, standup_generator for reports. No overlap or ambiguity.
All tool names follow a consistent 'domain_verb' pattern (e.g., burndown_calculator, priority_sorter, todo_manager). The naming style is uniform with snake_case and predictable suffixes.
Five tools is well-scoped for a task management server offering core functions (CRUD, prioritization, estimation, sprint tracking, standup). No tool seems extraneous or missing.
The tool set covers full task lifecycle (CRUD), prioritization, estimation, burndown, and standups. However, story points for burndown and subtask support for estimation are not explicitly tied into todo_manager, leaving minor gaps.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
AI-native project management + agent memory: tasks, sprints, risk, burnout, knowledge search.
AI-native project management for tasks, docs, collaboration, and agents.
Task management for teams building with AI agents. Agents claim tasks and report progress.
Task & board management for AI agents + humans. Kanban, comments, digests via MCP.
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