The Spec MCP Server is an AI-assisted development workflow automation tool that analyzes codebases, generates implementation plans, and coordinates task execution through automated verification.
Core Capabilities:
Codebase Analysis: Generates foundational steering documents (product.md, tech.md, structure.md) in .spec/steering/ that capture product features, technology stack, and project structure
Implementation Planning: Creates detailed tasks.md files with numbered requirements (EARS format), implementable tasks with IDs (T-1, T-2, etc.), dependencies, phases, and testing guidance based on user requests
Task Orchestration: Coordinates systematic task execution by analyzing dependencies, identifying parallelization opportunities, and managing workflow based on tasks.md
Task Execution: Provides detailed implementation guidance for specific tasks including requirements, acceptance criteria, and code patterns using the task-executor
Task Verification: Validates task completion by checking acceptance criteria against evidence and providing clear PASS/FAIL decisions using the task-checker
Complex Workflow Support: Handles framework migrations, feature implementations, and refactoring projects through dependency-aware task management
Multi-Platform Integration: Works with VS Code, Zed, Claude Code CLI, and Claude Desktop
Force Regeneration: Optionally regenerates analysis documents for updated codebase understanding
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., "@Spec MCP Server analyze the codebase and generate a migration plan from React to Next.js"
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.
A Model Context Protocol server designed to streamline development workflows through AI-assisted analysis, planning, and verification.
Tech Steering: Automated codebase analysis and documentation
Plan Generation: Comprehensive requirement and design documentation
Task Breakdown: Detailed task generation with dependencies
Implementation Verification: Automated code review and compliance checking
Related MCP server: ralph-loop-mcp
Configure your AI client (Claude Desktop, Cursor, etc.)
Add the server to your MCP configuration
Start using the tools through your AI interface
Add to your VS Code MCP configuration file:
Regular VS Code: ~/Library/Application Support/Code/User/mcp.json
VS Code Insiders: ~/Library/Application Support/Code - Insiders/User/mcp.json
{
"servers": {
"spec": {
"command": "npx",
"args": [
"-y",
"spec-mcp@latest"
]
}
}
}Open Zed > Settings > Open Settings (it will open ~/. config/zed/settings.json)
Add a context_servers section to your configuration:
{
"context_servers": {
"spec": {
"source": "custom",
"command": "npx",
"args": ["-y", "spec-mcp@latest"],
"env": {}
}
}
}For Claude Code CLI, use the following command:
claude mcp add spec-mcp --scope user -- npx -y spec-mcp@latestAdd to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"spec": {
"command": "npx",
"args": [
"-y",
"spec-mcp@latest"
]
}
}
}The Spec MCP workflow guides you through complex development tasks like framework migrations, feature implementation, or refactoring. Follow these steps:
Analyze the existing codebase to create steering documents that guide all future operations.
Prompt: Use Spec MCP to analyze the codebase
This creates three steering documents in .spec/steering/:
product.md - Product overview and business context
tech.md - Technology stack and development guidelines
structure.md - Project organization and patterns
Note: You can recreate these documents anytime with force_regenerate=true
Search documentation for relevant information about frameworks, APIs, or migration guides using e.g. Context7 MCP.
Prompt: Search docs with Context7 MCP for [topic/framework/library]
Ask the Spec MCP server to generate a clear, evidence‑driven task list from your goal.
Having your previous analysis in context window you can create tasks from it.
Example prompt:
Generate tasks from the analysis using Spec MCP
Output:
.spec/specs/tasks.md (includes overview, requirements with EARS, implementable tasks, dependencies, and tests)
Execute tasks systematically using the task orchestrator, which handles dependencies and parallelization.
Prompt: Implement tasks from tasks.md using Spec MCP task orchestrator
The orchestrator will:
Identify ready tasks based on dependencies
Execute tasks through task-executor
Verify completion with task-checker
Report progress and next available tasks
Analyzes codebase and generates three foundational analysis documents: product.md, tech.md, and structure.md in .spec/steering/ directory. These documents provide comprehensive analysis of the product features, technology stack, and project structure.
Spec MCP: Generate tasks.md (Plan & Task Breakdown)
Generate tasks.md (implementation plan & task breakdown) from your request and codebase. Writes .spec/specs/tasks.md with overview, numbered requirements using EARS, implementable tasks (T‑1…), dependencies/phases, and testing guidance. Use for “task breakdown”, “create tasks.md”, “implementation plan”, or “roadmap”. Inputs:
user_request (required)
project_path (optional, defaults to .)
Analyzes tasks.md to identify dependencies, parallelization opportunities, and coordinate task execution. Returns a structured execution plan for deploying task executors efficiently.
Executes a specific task from tasks.md by providing detailed implementation guidance, requirements, acceptance criteria, and code patterns. This tool focuses on implementing one task thoroughly.
Checks if a task can be marked as done by verifying all acceptance criteria checkboxes are checked and optionally running build/tests. Returns PASS or FAIL with clear reasons.
MIT
Analyzes codebase and generates three foundational analysis documents: product.md, tech.md, and structure.md in .spec/steering/ directory. These documents provide comprehensive analysis of the product features, technology stack, and project structure. Checks if files already exist and skips generation unless force_regenerate is true.
| Name | Required | Description | Default |
|---|---|---|---|
| additional_context | No | Additional context about the project | |
| force_regenerate | No | Force regeneration even if files exist (default: false) | |
| project_path | No | Path to the project directory (defaults to current directory) |
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 effectively describes the tool's behavior: it analyzes codebases, generates specific documents, checks for existing files, and respects the force_regenerate parameter. The description doesn't mention potential side effects, error conditions, or performance characteristics, but provides solid operational context for a documentation 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 efficiently structured in two sentences: the first states the core purpose and outputs, the second explains the file existence behavior. Every word serves a purpose with no redundancy, and the most important information (what the tool does) comes first.
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 3-parameter tool with no annotations and no output schema, the description provides good coverage of the tool's purpose, behavior, and key parameter semantics. It explains what documents are generated, where they go, and the conditional regeneration logic. The main gap is lack of information about return values or error conditions, but given the tool's documentation-generation nature, the description is reasonably 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?
With 100% schema description coverage, the baseline is 3. The description mentions the force_regenerate parameter's purpose ('skips generation unless force_regenerate is true'), which adds some semantic context beyond the schema's technical description. However, it doesn't elaborate on how additional_context influences the analysis or what project_path defaults mean in practice.
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 specific action ('analyzes codebase and generates three foundational analysis documents') and identifies the exact output files (product.md, tech.md, structure.md) with their location (.spec/steering/ directory). It distinguishes this tool from sibling tools like generate-tasks or task-executor by focusing on documentation generation rather than task management.
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 clear context for when to use the tool (analyzing codebases to create documentation) and includes an important behavioral guideline about file existence checking and the force_regenerate parameter. However, it doesn't explicitly state when NOT to use this tool or mention alternatives among the sibling tools, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate tasks.md (implementation plan & task breakdown) from your request and codebase. Writes .spec/specs/tasks.md with overview, numbered requirements using EARS, implementable tasks (T-1…), dependencies/phases, and testing guidance. Use for “task breakdown”, “create tasks.md”, “implementation plan”, or “roadmap”.
| Name | Required | Description | Default |
|---|---|---|---|
| project_path | No | Path to the project directory (defaults to current directory) | |
| user_request | Yes | Feature request or requirement to plan for |
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 discloses key behavioral traits: it writes a file ('.spec/specs/tasks.md'), generates structured content (overview, requirements, tasks, dependencies, testing guidance), and processes user requests and codebase. However, it doesn't mention potential side effects (e.g., file overwriting), error handling, or performance considerations, leaving gaps for a tool that performs file operations.
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 and front-loaded, starting with the core purpose. Each sentence adds value: the first defines the action, the second details the output content, and the third provides usage examples. There's no redundant information, though it could be slightly more streamlined.
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 (file generation with structured planning), no annotations, and no output schema, the description is moderately complete. It covers the purpose, output format, and usage context, but lacks details on behavioral aspects like error handling, file overwriting risks, or output validation. For a tool that writes files, more transparency would be beneficial.
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 schema already documents both parameters ('project_path' and 'user_request') adequately. The description adds minimal value beyond the schema by implying how parameters are used ('from your request and codebase'), but doesn't provide additional syntax, format details, or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.
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: 'Generate tasks.md (implementation plan & task breakdown) from your request and codebase.' It specifies the verb ('Generate'), resource ('tasks.md'), and what it contains ('overview, numbered requirements using EARS, implementable tasks...'). However, it doesn't explicitly differentiate from sibling tools like 'task-checker' or 'task-orchestrator' beyond listing use-case synonyms.
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 clear usage context with phrases like 'Use for “task breakdown”, “create tasks.md”, “implementation plan”, or “roadmap”' and specifies the input source ('from your request and codebase'). It doesn't explicitly state when NOT to use this tool or name alternatives among siblings, but the context is sufficiently clear for typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Prompt-only verification: instructs the agent to verify acceptance criteria checkboxes for a task with quoted evidence. Returns a PASS/FAIL decision rule to apply.
| Name | Required | Description | Default |
|---|---|---|---|
| project_path | No | Path to project (defaults to current directory) | |
| task_id | Yes | Task ID to check (e.g., T-1, T-2) |
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 states the tool returns a 'PASS/FAIL decision rule to apply,' but doesn't explain how the verification works (e.g., what 'quoted evidence' entails, whether it's a read-only check, potential side effects, or error handling). For a verification tool with zero annotation coverage, this is a significant gap in 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 concise and front-loaded, consisting of two clear sentences that convey the core purpose and outcome. There's no unnecessary repetition or fluff, making it efficient. However, it could be slightly more structured by explicitly separating the action from the result for enhanced 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 the complexity of a verification tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., how verification is performed, what 'quoted evidence' means), usage context, and return values. This makes it inadequate for an agent to fully understand the tool's operation and implications.
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 description coverage is 100%, so the schema already documents both parameters ('project_path' and 'task_id') adequately. The description doesn't add any parameter-specific details beyond what the schema provides, such as examples or constraints. Baseline 3 is appropriate when the schema handles parameter documentation effectively.
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: 'instructs the agent to verify acceptance criteria checkboxes for a task with quoted evidence. Returns a PASS/FAIL decision rule to apply.' It specifies the verb ('verify'), resource ('acceptance criteria checkboxes for a task'), and outcome ('PASS/FAIL decision rule'). However, it doesn't explicitly differentiate from sibling tools like 'task-executor' or 'task-orchestrator', which might handle task execution rather than verification.
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 minimal guidance on when to use this tool. It mentions 'Prompt-only verification' and 'instructs the agent to verify acceptance criteria checkboxes,' but doesn't specify when to choose this over alternatives like 'task-executor' or 'task-orchestrator,' nor does it outline prerequisites or exclusions. This lack of explicit context leaves usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Executes a specific task from tasks.md by providing detailed implementation guidance, requirements, acceptance criteria, and code patterns. This tool focuses on implementing one task thoroughly.
| Name | Required | Description | Default |
|---|---|---|---|
| project_path | No | Path to the project directory (defaults to current directory) | |
| task_id | Yes | Task ID to execute (e.g., T-1, T-2) | |
| update_status | No | Whether to include instructions for updating task status (default: true) |
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 describes the tool as executing a task by 'providing detailed implementation guidance', which implies a read-only or advisory role rather than direct code execution, but it doesn't clarify permissions, side effects, or output format. For a tool named 'executor' with zero annotation coverage, this is a significant gap in transparency about its actual behavior and limitations.
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 highly concise and well-structured in two sentences. The first sentence clearly states the purpose and key components (guidance, requirements, etc.), and the second sentence emphasizes the focus on thorough implementation. Every word earns its place with no redundancy or fluff, making it easy for an agent to parse quickly.
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 moderate complexity (3 parameters, no output schema, no annotations), the description is partially complete. It adequately explains the purpose and scope but lacks details on behavioral aspects like output format, error handling, or integration with sibling tools. Without annotations or output schema, the description should do more to compensate, but it provides a basic foundation that is minimally viable.
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 description coverage is 100%, so the schema already documents all three parameters (project_path, task_id, update_status) with clear descriptions. The tool description adds no additional parameter semantics beyond what's in the schema, such as examples for task_id format or implications of update_status. This meets the baseline for high schema coverage but doesn't enhance understanding.
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: 'Executes a specific task from tasks.md by providing detailed implementation guidance, requirements, acceptance criteria, and code patterns.' It specifies the verb ('executes'), resource ('task from tasks.md'), and scope ('one task thoroughly'), distinguishing it from siblings like 'task-checker' or 'task-orchestrator'. However, it doesn't explicitly contrast with all siblings (e.g., 'generate-codebase-analysis'), keeping it from a perfect score.
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 minimal guidance on when to use this tool. It mentions 'focuses on implementing one task thoroughly', which implies usage for detailed task execution, but offers no explicit when-to-use rules, prerequisites, or alternatives among siblings like 'task-orchestrator' or 'task-checker'. This lack of comparative context leaves the agent with insufficient guidance for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use this to implement/execute existing tasks from tasks.md that have already been defined. This tool assumes tasks already exist and focuses on coordinating their implementation. DO NOT use this for generating new plans or tasks - only for executing tasks that have already been created.
| Name | Required | Description | Default |
|---|---|---|---|
| project_path | No | Path to the project directory (defaults to current directory) |
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 discloses that the tool coordinates implementation of existing tasks, which implies mutation or execution behavior, but does not detail permissions, side effects, error handling, or output format. The description adds some context about task existence but lacks comprehensive behavioral traits.
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 with two sentences that are front-loaded and zero waste. The first sentence states the purpose, and the second provides critical usage restrictions, with every sentence earning its place by adding essential information.
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 complexity (coordination of tasks), lack of annotations, and no output schema, the description is reasonably complete for guiding usage but could benefit from more behavioral details. It covers purpose and guidelines well but does not fully address execution outcomes or error scenarios, leaving some gaps in context.
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 schema already documents the single parameter ('project_path'). The description does not add any meaning beyond what the schema provides regarding parameters, as it focuses on tool usage rather than parameter details. With high schema coverage, 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 states the specific verb ('implement/execute') and resource ('existing tasks from tasks.md'), and explicitly distinguishes this tool from its siblings by stating it should not be used for generating new plans or tasks. It directly addresses what the tool does and how it differs from related tools.
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 explicit guidance on when to use this tool ('for implementing/executing existing tasks') and when not to use it ('DO NOT use this for generating new plans or tasks'). It also implies alternatives by mentioning tasks that 'have already been defined,' suggesting other tools might handle task creation.
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.
v1.0.0generate-codebase-analysisgenerate-taskstask-checkertask-executortask-orchestratorThe tools have overlapping purposes that could cause confusion, particularly between task-executor and task-orchestrator which both handle task implementation with unclear boundaries. However, descriptions help clarify that task-orchestrator is for coordinating multiple existing tasks while task-executor focuses on single-task implementation, and the other tools have more distinct roles.
Tool names follow a consistent verb_noun pattern (e.g., generate-codebase-analysis, generate-tasks, task-checker) with clear, descriptive naming. The only minor deviation is that 'task-checker' and 'task-executor' use hyphenated compound nouns rather than separate verb_noun pairs, but the pattern remains readable and predictable throughout.
With 5 tools, the count is well-scoped for the server's purpose of specification and task management. Each tool earns its place by covering distinct phases: analysis generation, task planning, verification, and implementation, making the set neither too thin nor overloaded for the domain.
The tool surface covers the core workflow from codebase analysis to task execution with minor gaps. It includes analysis generation, task planning, verification, and implementation tools, but lacks explicit tools for updating or deleting generated documents or tasks, which agents might need to work around by regenerating files.
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AI-powered spec-to-task decomposition and execution orchestration for coding agents.
Transform project ideas into paint-by-numbers development plans with phases, tasks, and subtasks.
AI work orchestration for plans, tasks, teams, and coding-agent dispatch.
Adaptive plan/build/review cycles for AI coding assistants, persisted across sessions.
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