Manages Angular project tasks such as build, serve, and test.
Integrates CircleCI workflow tasks into mise for CI/CD pipeline management.
Manages Cypress end-to-end test tasks within mise task architecture.
Manages Docker container build and deployment tasks.
Manages ESLint code quality tasks for JavaScript/TypeScript projects.
Manages GitHub Actions workflow tasks for CI/CD integration.
Manages GitLab CI pipeline tasks within mise task management.
Manages Jenkins build and deploy tasks in the mise workflow.
Manages Jest test tasks for unit and integration testing.
Manages Kubernetes deployment and management tasks.
Manages MongoDB database tasks such as migrations and seeding.
Manages MySQL database tasks within the mise ecosystem.
Manages .NET project tasks including build, test, and publish.
Manages Netlify deployment tasks for frontend projects.
Manages Next.js development, build, and deployment tasks.
Manages Node.js application tasks including server start, test, and build.
Manages Nuxt.js development and build tasks.
Manages PHP project tasks like serve, test, and build.
Manages PostgreSQL database tasks including migrations and queries.
Manages Prisma migration and schema generation tasks.
Manages Pytest test tasks for Python projects.
Manages React development and build tasks.
Manages Redis tasks such as cache clearing and server management.
Manages Rust project tasks including build, test, and format.
Manages Svelte development and build tasks.
Manages API documentation generation tasks from OpenAPI specs.
Manages TypeORM database migration and schema tasks.
Manages TypeScript type-checking and compilation tasks.
Manages Vercel deployment tasks for frontend applications.
Manages Vite dev server and build tasks.
Manages Vitest test tasks for unit and integration testing.
Manages Webpack bundling and build tasks.
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., "@Wise Mise MCP Analyze my project for mise task suggestions"
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.
The intelligent MCP server that transforms mise task management with AI-powered analysis and domain expertise
Stop wrestling with mise configuration. Wise Mise MCP brings enterprise-grade intelligence to your mise workflow, automatically analyzing your project structure and creating perfectly organized, maintainable task architectures that scale with your development needs.
🧠 Intelligent Task Analysis
Automatically analyzes your project structure to extract meaningful tasks
Understands 10+ technology domains (build, test, lint, deploy, CI/CD, etc.)
Suggests optimal task organization and dependency patterns
🏗️ Architecture-Aware
Follows mise best practices with hierarchical task organization
Supports complex dependency graphs with source/output tracking
Optimizes for incremental builds and performance
🔧 Developer Experience
Integrates seamlessly with any MCP-compatible client
Provides detailed explanations and recommendations
Reduces cognitive load of task management
Related MCP server: Trellis MCP
# Just run this to start Wise Mise MCP with UVX
uvx wise-mise-mcp
# Or install globally
uv tool install wise-mise-mcppip install wise-mise-mcpAdd to your MCP client configuration (e.g., Claude Desktop):
{
"mcpServers": {
"wise-mise-mcp": {
"command": "uvx",
"args": ["wise_mise_mcp"]
}
}
}Deploy Wise Mise MCP instantly to Smithery's cloud infrastructure with one click. Smithery provides managed MCP server hosting with built-in monitoring, scaling, and zero-configuration deployment.
Quick Smithery Deployment:
Click the "Deploy to Smithery" badge above
Connect your GitHub account (if needed)
Your MCP server will be automatically deployed and configured
Use the provided endpoint in your MCP client configuration
Benefits of Smithery Deployment:
✅ Zero server management overhead
✅ Automatic scaling and load balancing
✅ Built-in monitoring and health checks
✅ Global CDN for low-latency access
✅ Automatic SSL/TLS encryption
✅ Integration with popular MCP clients
Wise Mise MCP goes beyond simple task creation. It brings intelligence to your mise configuration:
# Analyzes your entire project structure
analyze_project_for_tasks("/path/to/project")
# Returns strategically organized tasks based on your tech stack# Traces complex task relationships
trace_task_chain("/path/to/project", "build:prod")
# Visualizes the complete execution flow# Intelligently places tasks in the right domain
create_task(
project_path="/path/to/project",
task_description="Run TypeScript type checking",
# Automatically suggests: lint:types with proper dependencies
)Build: Frontend/Backend build systems, bundlers, compilers
Test: Unit, integration, e2e testing strategies
Lint: Code quality, formatting, static analysis
Deploy: CI/CD, containerization, release management
Database: Migrations, seeding, schema management
Development: Local dev servers, hot reloading, debugging
Complexity Assessment: Automatically categorizes tasks as Simple, Moderate, or Complex
Dependency Detection: Identifies natural task relationships
Source/Output Tracking: Optimizes incremental builds
Redundancy Elimination: Finds and removes duplicate tasks
Tool | Purpose |
| Extract strategic tasks from project structure |
| Map task dependencies and execution flow |
| Add new tasks with intelligent placement |
| Remove outdated or redundant tasks |
| Ensure configuration follows best practices |
| Get suggestions for optimization |
# Let Wise Mise MCP analyze your project
> analyze_project_for_tasks("./my-app")
✅ Detected: Next.js + TypeScript + Prisma
📋 Suggested Tasks:
├── build:dev (next dev)
├── build:prod (next build)
├── test:unit (jest)
├── test:e2e (playwright)
├── lint:code (eslint)
├── lint:types (tsc --noEmit)
├── db:migrate (prisma migrate)
└── deploy:vercel (vercel deploy)# Trace the execution flow
> trace_task_chain("./my-app", "deploy:prod")
🕸️ Task Chain for deploy:prod:
1. lint:types (TypeScript check)
2. test:unit (Unit tests)
3. build:prod (Production build)
4. deploy:prod (Deploy to production)
💡 Recommendation: Add test:e2e before deploy:prod# Describe what you want, get intelligent suggestions
> create_task(
project_path="./my-app",
task_description="Generate API documentation from OpenAPI spec"
)
🧠 Analysis: Documentation generation task
📍 Suggested Placement: docs:api
🔗 Dependencies: build:prod (for spec generation)
📝 Suggested Implementation:
[tasks.docs.api]
run = "swagger-codegen generate -i ./openapi.json -l html2 -o ./docs/api"
sources = ["src/api/**/*.ts", "openapi.json"]
outputs = ["docs/api/**/*"]Wise Mise MCP follows a Domain-Driven Design approach to task organization:
Level 1: Domain (build, test, lint, etc.)
Level 2: Environment/Type (dev, prod, unit, e2e)
Level 3: Specific Implementation (server, client, api)
Sequential: lint → test → build → deploy
Parallel: test:unit + test:e2e → deploy
Conditional: deploy:staging → test:smoke → deploy:prod
Source Tracking: Only rebuild when sources change
Output Caching: Reuse previous build artifacts
Incremental Builds: Support for modern build tools
Wise Mise MCP includes expert knowledge for:
Frontend: React, Vue, Angular, Svelte, Next.js, Nuxt, Vite, Webpack Backend: Node.js, Python, Go, Rust, Java, .NET, PHP Databases: PostgreSQL, MySQL, MongoDB, Redis, Prisma, TypeORM Testing: Jest, Vitest, Cypress, Playwright, PyTest, Go Test CI/CD: GitHub Actions, GitLab CI, CircleCI, Jenkins Deployment: Docker, Kubernetes, Vercel, Netlify, AWS, GCP
We welcome contributions! See our Contributing Guide for details.
# Clone and setup with UV
git clone https://github.com/delorenj/wise-mise-mcp
cd wise-mise-mcp
uv sync
# Run tests
uv run pytest
# Format code
uv run black .
uv run ruff check --fix .MIT License - see LICENSE for details.
Documentation: Full API Documentation
Issues: GitHub Issues
Discussions: GitHub Discussions
Built with ❤️ by Jarad DeLorenzo and the open source community
Analyze a project structure and extract strategic task recommendations.
This tool examines the project's package managers, languages, frameworks, and structure to recommend useful tasks organized by domain. It identifies what build systems, testing frameworks, and development tools are in use to suggest practical, actionable tasks.
| Name | Required | Description | Default |
|---|---|---|---|
| project_path | Yes | Path to the project directory |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool examines project structure, package managers, languages, and frameworks, but does not explicitly state that it is read-only or has no side effects. It adds context beyond the name but could be more precise.
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 concise with two paragraphs, front-loading the main purpose and then providing details. Every sentence adds value without unnecessary repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description does not need to explain return values. It covers the tool's inputs and behavior sufficiently for an analysis tool, though it could mention any prerequisites or limitations.
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 coverage is 100% with a description for project_path. The tool description does not add any additional meaning or formatting guidance beyond what the schema provides, so the baseline of 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's purpose: analyzing a project structure and extracting strategic task recommendations. It mentions specific elements like package managers, languages, frameworks, and domains, distinguishing it from siblings like create_task or remove_task.
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 explicit guidance on when to use this tool versus siblings like get_task_recommendations or trace_task_chain. It implies usage through its description but lacks a direct directive or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new task with intelligent placement and configuration.
This tool analyzes the task description and project context to suggest the best domain, complexity level, and implementation for a new task. It follows mise best practices for task organization and naming.
| Name | Required | Description | Default |
|---|---|---|---|
| domain_hint | No | Hint about which domain this task belongs to | |
| project_path | Yes | Path to the project directory | |
| suggested_name | No | Suggested task name | |
| force_complexity | No | Force complexity level (simple/moderate/complex) | |
| task_description | Yes | Description of what the task should do |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It mentions intelligent analysis and best practices, but does not detail side effects (e.g., file modifications), required permissions, or error conditions. This is moderately informative but leaves gaps.
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 with no wasted words. The first sentence front-loads the primary action, and the second adds relevant detail. Every sentence 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?
Given the tool has 5 parameters and an output schema, the description is moderately complete. It explains the intelligent behavior but omits prerequisites (e.g., valid project path), output expectations, or post-conditions. It does not leverage the output schema to explain return values, but that is not required.
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 5 parameters have descriptions in the schema (100% coverage), so the description adds little beyond stating that parameters like domain_hint and force_complexity influence intelligent behavior. The baseline of 3 is appropriate as the description does not compensate for any missing schema details.
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 task with intelligent placement and configuration, which distinguishes it from sibling tools like remove_task or get_task_recommendations. However, it emphasizes 'suggests' which could imply it only recommends rather than creates, causing slight ambiguity.
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 explicit guidance on when to use this tool versus alternatives like get_task_recommendations or analyze_project_for_tasks. The description implies it is for creating tasks, but does not specify when creation is appropriate versus when to use analysis or validation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the architectural rules and patterns that Wise Mise MCP follows.
This tool explains the design principles, domain organization, and architectural decisions that guide intelligent task management.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It accurately indicates a read operation that returns architectural information. While it does not detail potential side effects or performance implications, the description is sufficient for a read-only tool with an output schema.
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 extremely concise, consisting of two sentences. The first sentence front-loads the verb and resource, and the second provides additional detail. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and an output schema, the description effectively communicates its purpose. It could potentially mention how the architectural rules relate to sibling tools, but this is not essential for 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 tool has no parameters, so schema coverage is 100% by default. According to the guidelines, the baseline score is 4. The description does not need to add parameter information, but it also does not detract.
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 retrieves architectural rules and patterns, explaining design principles, domain organization, and architectural decisions. The verb 'Get' and the resource 'architectural rules' are specific, and the tool is well-differentiated from sibling tools that focus on task management 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 does not provide any guidance on when to use this tool versus alternatives, nor does it mention prerequisites or contexts where it is most appropriate. It only describes what the tool does without usage recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get general recommendations for mise task organization and best practices.
This tool provides guidance on how to structure tasks effectively, naming conventions, dependency patterns, and optimization strategies.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden. It states the tool provides recommendations and guidance, implying a read-only, safe operation. However, it does not disclose behavioral details such as side effects, rate limits, or authentication needs. The description is adequate but lacks explicit transparency.
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 composed of three concise sentences, with the first sentence front-loading the purpose. Every sentence adds value 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 there are no parameters and an output schema exists, the description provides a complete overview of the tool's purpose. It covers the key topics the agent can expect recommendations on.
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?
There are no parameters, so the input schema is fully covered. The description adds meaning by listing the areas of guidance (structure, naming, dependencies, optimization), which helps the agent understand what kind of recommendations to expect. Baseline 4 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 clearly states 'Get general recommendations for mise task organization and best practices' and elaborates on specific aspects like structure, naming, dependencies, and optimization. This distinguishes it from siblings such as analyze_project_for_tasks (analysis) and trace_task_chain (tracing).
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 does not provide any guidance on when to use this tool versus alternatives. It lacks explicit statements about when it is appropriate, when not, or how it compares to sibling tools like get_mise_architecture_rules or mise_task_expert_guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get expert guidance on mise task management and optimization.
This tool provides advanced tips, troubleshooting advice, and expert recommendations for getting the most out of mise task management.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool provides advice and tips, but does not disclose any behavioral traits such as response format, limitations, or whether it requires external resources.
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 concise with two sentences. The first sentence captures the core purpose, and the second adds detail. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the tool has no parameters and an output schema exists, the description does not mention what the output contains (e.g., text, examples). For a guidance tool, stating the return type would improve 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?
There are no parameters, so the description does not need to add meaning beyond the schema. Schema coverage is 100%.
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 provides 'expert guidance', 'advanced tips', 'troubleshooting advice', and 'expert recommendations' for mise task management. This distinguishes it from sibling tools like create_task or remove_task, which perform specific actions.
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 getting guidance, but does not explicitly state when to use it versus siblings, nor does it mention when not to use it. It lacks clear decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Remove redundant or outdated tasks from the project.
This tool identifies tasks that are no longer needed, have become redundant, or are not following current best practices. It can either report what would be pruned (dry run) or actually remove the tasks.
| Name | Required | Description | Default |
|---|---|---|---|
| dry_run | No | Whether to only report what would be pruned | |
| project_path | Yes | Path to the project directory |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the dry-run safety option, but does not explain criteria for pruning, whether removal is permanent, authentication needs, or side effects. Incomplete behavioral disclosure for a destructive 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?
Three sentences, front-loaded with the primary action. Slight redundancy ('redundant or outdated' then 'no longer needed, have become redundant') but overall clear and efficient.
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?
Covers the main action and dry-run option. With an output schema present, return values are handled separately. However, missing details on input prerequisites or pruning criteria leave gaps for full contextual understanding.
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 coverage is 100% with clear parameter descriptions. The description adds no new meaning beyond schemas, merely restating dry-run functionality. Baseline score of 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 'Remove redundant or outdated tasks from the project', specifying the verb, resource, and scope. It distinguishes from siblings like 'remove_task' by describing a bulk, analysis-driven cleanup and offering a dry-run mode.
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 bulk cleanup and mentions dry-run vs actual removal, but lacks explicit guidance on when to use this tool versus alternatives like 'remove_task' or 'analyze_project_for_tasks'. No when-not or exclusion criteria are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Remove a specific task from the project.
This tool removes a named task from the mise configuration, handling any cleanup needed and warning about dependent tasks that might be affected.
| Name | Required | Description | Default |
|---|---|---|---|
| task_name | Yes | Name of the task to remove | |
| project_path | Yes | Path to the project directory |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It mentions cleanup and warnings, which is helpful, but does not specify whether removal is permanent, if permission is required, or what the output contains (output schema exists but is not referenced).
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 short sentences, no fluff. The first sentence provides the core action, and the second adds key behavioral details (cleanup, warnings). 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?
Given an output schema exists, the description need not detail return values. It covers the main action, cleanup, and dependency warnings. However, it could mention that it operates on the mise configuration early in the first sentence for immediate clarity.
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 description adds no new meaning beyond the schema. Both 'task_name' and 'project_path' are self-explanatory. The description's mention of 'named task' slightly reinforces the parameter but does not add depth.
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 removes a specific task from a project, specifically from the mise configuration. This is a unique purpose among siblings like 'create_task' and 'prune_tasks', which handle creation or bulk removal respectively.
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 mentions cleanup and dependency warnings but does not provide explicit guidance on when to use this tool versus alternatives like 'prune_tasks' or 'validate_task_architecture'. The context of sibling tools implies it's for removing a single named task, but this is not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze and optimize task execution chains for better performance.
This tool provides insights into task execution patterns, identifies bottlenecks, and suggests optimizations for complex task workflows.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It states the tool provides insights, identifies bottlenecks, and suggests optimizations, but it does not clarify whether the tool performs mutations (e.g., 'optimize' might imply changes) or is purely analytical. It also omits details on required permissions, rate limits, or side effects.
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 concise, consisting of two sentences with no waste. However, it could be slightly more structured by front-loading the primary action. It is appropriate for a simple tool with no parameters.
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 an output schema exists (per context signals), the description does not need to detail return values. It adequately explains the tool's function for a user familiar with the domain. Minor gap: it doesn't hint at the output's structure, but the output schema compensates.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema coverage is 100% (trivially). Per the rubric, 0 parameters yields a baseline of 4. The description adds no parameter-specific information, but that is acceptable since none exist.
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: analyzing and optimizing task execution chains. It uses specific verbs ('analyze', 'optimize') and a defined resource ('task execution chains'). It distinguishes from siblings like 'trace_task_chain' (tracing only) and 'validate_task_architecture' (validation) by covering both analysis and optimization.
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 does not specify when to use this tool versus alternatives like 'analyze_project_for_tasks' or 'trace_task_chain'. It lacks explicit guidance on prerequisites, limitations, or scenarios where other tools would be more appropriate. The purpose is implied but no direct comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Trace the dependency chain for a specific task.
This tool analyzes task dependencies to show the complete execution flow for a given task. It helps understand what tasks will run and in what order, making it easier to optimize build processes and debug issues.
| Name | Required | Description | Default |
|---|---|---|---|
| task_name | Yes | Name of the task to trace | |
| project_path | Yes | Path to the project directory |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It implies read-only behavior (analyze, show) but does not disclose potential behavioral traits like output format, authentication needs, or rate limits. Adequate but not rich.
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 paragraphs with a clear first sentence stating the purpose. It is efficient but could be slightly more front-loaded; no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (not shown) and 100% schema coverage, the description sufficiently covers the purpose and context for a simple trace tool. It is complete without needing to explain return values.
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 additional meaning beyond the schema's parameter descriptions, which are already clear.
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 'Trace the dependency chain for a specific task' and explains it analyzes dependencies to show execution flow, which is distinct from sibling tools like 'analyze_project_for_tasks' or 'task_chain_analyst'.
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 mentions it helps understand task execution order for optimizing builds and debugging, providing implied usage context. However, it does not explicitly state when to use this tool over alternatives or give conditions for not using it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate the current task architecture against best practices.
This tool analyzes the existing task structure to identify issues like circular dependencies, missing dependencies, redundant tasks, and opportunities for better organization.
| Name | Required | Description | Default |
|---|---|---|---|
| project_path | Yes | Path to the project directory |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains the types of issues identified (circular dependencies, missing dependencies, etc.), but fails to disclose whether the tool modifies state, requires special permissions, or has side effects. Without annotations, this gap leaves some behavioral uncertainty.
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 extremely concise: two sentences with no filler. The first sentence states the purpose, and the second expands on the types of issues found. 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?
For a single-parameter tool with an output schema, the description covers the core functionality and typical findings. It lacks mention of best practices sources or range of analysis scope, but is largely sufficient for the given 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 single parameter 'project_path' is fully described by the input schema (100% coverage). The tool description adds no additional semantic detail about the parameter, so the baseline score of 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 identifies the tool as validating task architecture against best practices, specifying it analyzes for concrete issues like circular dependencies and missing dependencies. This effectively communicates the purpose, though it does not explicitly differentiate from sibling tools like 'analyze_project_for_tasks'.
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 given on when to use this tool versus alternatives (e.g., when to run validation instead of general analysis or recommendations). The description implies it is for architecture validation, but lacks explicit context for tool selection.
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.
v2.1.1analyze_project_for_taskscreate_taskget_mise_architecture_rulesget_task_recommendationsmise_task_expert_guidanceprune_tasksremove_tasktask_chain_analysttrace_task_chainvalidate_task_architectureTools generally have distinct purposes, but 'get_task_recommendations' and 'mise_task_expert_guidance' both offer advice, and 'trace_task_chain' overlaps slightly with 'task_chain_analyst'. Descriptions help differentiate them.
Most tools use a consistent verb_noun pattern (e.g., create_task, prune_tasks), but 'task_chain_analyst' and 'mise_task_expert_guidance' break the pattern with noun-first or lengthy names.
10 tools cover the domain well without being excessive. Each tool has a clear role in task management, from creation to analysis to cleanup.
The set includes creation, removal, pruning, validation, tracing, and guidance. However, an explicit tool for listing all tasks or updating existing tasks is missing, which is a minor gap.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
MCP server for generating rough-draft project plans from natural-language prompts.
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
Task management your AI agents can actually run. One line becomes a context-ready task over MCP.
Work management where AI agents are first-class members: tasks, projects, memory over hosted MCP
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