This server provides on-demand access to GodPrompt's AI software-development workflow guidance and tools for routing tasks through its methodology.
Retrieve the full GodPrompt.md system prompt in one shot (get_god_prompt)
Load lean, always-on core skill content covering the 6-phase protocol, Iron Laws, and auto-classification (get_core_skill)
Fetch deep execution guides for BUILD, DEBUG, REFACTOR, CONTENT, DESIGN, SHIP, ANALYZE, AUTOMATE, and PLAN (get_protocols)
Get verification checklists, THE GATE, and report templates to validate work before completion (get_gates)
Review red flags, rationalizations, and recovery patterns to avoid common anti-patterns (get_anti_patterns)
Classify a task description into one of 9 GodPrompt task types and get the matching protocol name (classify_task)
Check the current GodPrompt version and server metadata (get_version)
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., "@GodPrompt MCP Server get the core skill for AI software development"
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 (MCP) server for GodPrompt — AI software-development workflow guidance with task routing, TDD, debugging protocols, verification gates, and progressive disclosure.
Tool | Description |
| Full GodPrompt.md single-file payload (~40KB) |
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| Classify a task into one of 9 GodPrompt task types |
| Version info and server metadata |
Related MCP server: prompts-mcp-server
For minimum context usage, start with get_core_skill, then load get_protocols, get_gates, or get_anti_patterns only when the task requires deeper guidance. Use get_god_prompt when you want everything in one shot.
"Classify this refactor and tell me which GodPrompt workflow applies."
"Show me the verification gates I should satisfy before I claim this bug fix is complete."
"Load the debugging protocol for a failing integration test without loading the full GodPrompt."
GodPrompt's benchmark methodology, deterministic task corpus, evaluator logic, and published run artifacts live in the source project: GodPrompt Bench.
The MCP server does not run the benchmark or claim model-level superiority itself; it distributes the GodPrompt content evaluated by that suite.
GodPrompt MCP is published on Glama. Use the server page to inspect the tools and connect it to a supported MCP client.
Kiro installs and builds the stdio server directly from this GitHub repository. The package remains unpublished on npm; Node.js 20+ and Git are required.
The repository includes a Cursor plugin manifest and mcp.json for Marketplace or local plugin installation. Cursor runs the same local stdio server through npx -y github:AKzar1el/god-prompt-mcp.
GitHub releases include a .mcpb bundle for one-click local installation in MCPB-compatible clients such as Claude Desktop. The bundle runs the same local stdio server and does not require an API key or account.
npm install
npm run build
node dist/stdio.jsExample client configuration:
{
"mcpServers": {
"god-prompt": {
"command": "node",
"args": ["/absolute/path/to/god-prompt-mcp/dist/stdio.js"]
}
}
}GodPrompt MCP is a local stdio server. Its content tools return static GodPrompt material bundled with the installed extension, and classify_task evaluates the supplied task description in the local Node.js process.
Data collection and use: the server has no telemetry or analytics and does not collect account data. Tool input is used only to produce the requested local response.
Storage and retention: the server has no server-side persistence and does not retain tool inputs after a request completes.
Third-party sharing: the server does not transmit tool inputs or bundled GodPrompt content to an external API or third party. The MCP host or AI client may process conversation and tool data under its own policies independently of this server.
Contact and policy: see tomiseregi.si/privacy for the current privacy policy and contact information. For support or bug reports, use GitHub Issues.
npm install
npm run build
npm testnpm test builds the stdio server, performs the MCP initialization handshake, and verifies the expected tool list.
Repository discovery metadata is kept in server.json for MCP Registry-compatible consumers and glama.json for Glama.
To update the embedded GodPrompt content:
Pull the latest content from the GodPrompt repository.
Run npm run generate-content.
Run npm test before publishing a new server release.
The generator reads the current source layout: GodPrompt.md, SKILL.md, and the three files under references/.
Project page: GodPrompt — AI software-development system prompt · GodPrompt source
Related MCP servers: Google Search Console · GEO Tracker · Web Validator · Google News & Trends
Classify a task description into one of GodPrompt's 9 task types (BUILD, DEBUG, REFACTOR, CONTENT, DESIGN, SHIP, ANALYZE, AUTOMATE, PLAN) and return the matching protocol name. Useful for routing tasks through the right workflow.
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | The task description to classify, e.g. 'fix the login bug' or 'build a REST API for user auth' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It clearly states the transformation (task description in, protocol name out) and names the fixed classification set. It does not detail edge cases, confidence, or output format beyond the protocol name, but for a pure classification operation this is reasonably transparent.
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 filler. The core behavior, the full enumeration of task types, and the return value are all packed efficiently. 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?
For a one-parameter classifier with full schema coverage, the description is complete. It states the input, the closed set of classification categories, and the output. No output schema exists, but the description's statement of the return value covers what an agent needs to know.
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%: the input schema already fully documents the sole 'description' parameter with examples. The tool description adds no parameter-specific meaning beyond restating that it classifies a task description, 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 uses a specific verb ('Classify') and a clear resource ('a task description'), enumerates all 9 possible output task types, and states the return value ('matching protocol name'). This clearly differentiates it from sibling retrieval tools like get_god_prompt or get_protocols, which fetch content rather than classify.
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: it is 'useful for routing tasks through the right workflow,' which tells the agent when this classifier should be invoked. It does not explicitly mention when not to use it or compare it to sibling tools, but the routing context is sufficient guidance for this straightforward tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns core/03-ANTI-PATTERNS.md — red flags, rationalizations, and recovery patterns. Covers the 10 most dangerous anti-patterns that lead to broken code, scope creep, and false confidence. Load when you catch yourself rationalizing. ~9KB.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and delivers: it discloses the returned resource, the nature of the content, the number of items, and the file size. It doesn't describe output formatting, but for a simple markdown file retrieval that is a minor 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?
Three sentences, each earning its place: resource path, content summary, and usage trigger. The size note is efficient extra orientation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a zero-parameter tool with no output schema, the description provides all necessary context: what file is returned, what it contains, when to use it, and expected size. Nothing essential is missing.
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 is empty (0 parameters), so parameter semantics are trivially satisfied. The description adds useful context about the content within the file, which is more than necessary.
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?
States a specific verb ('Returns') and a precise resource (core/03-ANTI-PATTERNS.md), then summarizes the content. It clearly distinguishes itself from sibling docs like get_protocols or get_gates by focusing on anti-patterns and rationalization.
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?
Explicitly tells the agent when to load: 'when you catch yourself rationalizing.' It doesn't name sibling alternatives or exclusions, but the trigger context is clear enough for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns core/00-THE-SKILL.md — the core protocol that should be loaded on every message. This is the lean base context (~10KB) covering the universal 6-phase protocol, Three Iron Laws, and task auto-classification. Start here for progressive disclosure.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It clearly explains what the tool returns, the approximate size (~10KB), and the contained content, which is strong context for a simple read-only retrieval 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?
Two sentences deliver the essential information with no waste: what is returned, its purpose, its size, its contents, and how to use it. The key fact is front-loaded and 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?
For a zero-parameter, read-only file retrieval tool, the description is complete. It explains what is returned, why it matters, and when to load it, leaving no meaningful gap for an agent deciding to call it.
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, so the schema provides no meaningful information. The description adds no parameter details, but with no parameters required, the baseline of 4 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 states a specific verb and resource ('Returns core/00-THE-SKILL.md') and clearly identifies what the file contains. It distinguishes itself from sibling tools by framing this as the lean base context that should be loaded on every message.
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?
It gives explicit usage guidance: the file 'should be loaded on every message' and is the starting point for progressive disclosure. It does not explicitly name alternatives or when-not-to-use cases, but for a universal base-context tool the guidance is clear and sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns core/02-GATES.md — verification checklists, THE GATE (pre-completion verification), and structured report templates for every deliverable type. Load when you need to verify work before claiming completion. ~9KB.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It transparently states the tool returns a file, enumerates its contents, and notes the size (~9KB). It does not explicitly say it is read-only, but the nature of returning a markdown file makes the behavior clear and complete enough.
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 what the tool returns and followed by a crisp usage trigger. Every word earns its place; no filler or 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?
For a parameterless, read-only file retrieval tool with no output schema, the description is complete: it names the file, summarizes the content, gives a usage trigger, and even indicates size. There is nothing critical missing for an agent to select and invoke it correctly.
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, so the baseline is 4. The description does not need to explain parameters; it adds value by describing what the returned document contains, which is more useful than parameter details would be.
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 states a specific action and resource: 'Returns core/02-GATES.md' and details its contents (verification checklists, THE GATE, report templates). This clearly distinguishes it from the sibling get_* tools, which target different files.
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?
Explicitly tells the agent when to load it: 'when you need to verify work before claiming completion.' It does not mention specific exclusions or directly compare to siblings, but the trigger condition is clear enough to route to this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the complete GodPrompt.md — a single-file universal system prompt for AI software development (40KB, ~1145 lines). Use this when you want the full payload in one shot. For progressive disclosure (smaller context), use the core/* tools instead.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It discloses the large payload (40KB, ~1145 lines), which is a key behavioral trait affecting context usage. It also implies a simple read-only retrieval with no parameters, though it does not explicitly mention side-effect-freedom or failure modes; for this simple static-file retrieval, the disclosure is sufficient.
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 main action and file details, then immediately provides usage conditions and the alternative. Every sentence earns its place and there is no 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?
For a no-parameter, no-output-schema retrieval tool, the description is complete. It states what is returned, the size/context impact, when to use it, and the alternative for smaller context. The agent has all the information needed to invoke it correctly.
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 is already fully descriptive (empty properties). The baseline for zero-parameter tools is 4, and the description adds no conflicting or missing parameter guidance. There is nothing more to document.
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 states a specific verb ('Returns') and a specific resource ('the complete GodPrompt.md'), and characterizes it as a single-file universal system prompt. It also distinguishes this tool from siblings by naming the progressive-disclosure core/* tools as the alternative for smaller context, so an agent can tell them apart.
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 explicitly gives the trigger condition: 'Use this when you want the full payload in one shot.' It also names the alternative strategy, progressive disclosure via core/* tools, so the agent knows exactly when this tool is appropriate versus when it is not.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns core/01-PROTOCOLS.md — deep execution guides for each task type (BUILD, DEBUG, REFACTOR, CONTENT, DESIGN, SHIP, ANALYZE, AUTOMATE, PLAN). Load this when the task requires detailed protocol steps beyond the core skill. ~13KB.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It does state what is returned and even the approximate size (~13KB), which is useful. However, it does not explicitly mention that the operation is read-only, has no side effects, or describe the output format beyond being a markdown file.
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 with no filler. The first sentence front-loads the core information: what is returned and what it contains. The second sentence gives the usage trigger and size, both of which earn their 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?
For a simple retrieval tool with no parameters and no output schema, this description is complete. It identifies the file, summarizes its contents, enumerates coverage, and states when to load it. An agent has enough information to decide and invoke correctly.
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, so there is no parameter semantics burden for the description to carry. The baseline for a zero-parameter tool is 4, and the description appropriately avoids introducing any phantom parameters.
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 opens with a specific verb ('Returns') and names the exact resource (core/01-PROTOCOLS.md), then lists the task types covered. It also signals differentiation from the sibling get_core_skill by saying 'beyond the core skill', so an agent can tell what makes this tool distinct.
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 gives an explicit trigger condition: 'Load this when the task requires detailed protocol steps beyond the core skill.' This tells the agent when to use it, though it does not explicitly name alternatives or state when not to use it, which keeps it just below a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the current GodPrompt version and a summary of what's included.
| Name | Required | Description | Default |
|---|---|---|---|
No 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. 'Returns' implies a read-only operation, but the description does not explicitly state that there are no side effects, what the response format is, or whether any authentication is needed.
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. Every word contributes to the tool's purpose, making it easy to scan and understand 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?
For a simple, zero-parameter version tool, the description provides adequate context by stating what is returned. It could be slightly more explicit about the output shape, but the low complexity and clear purpose make the description nearly 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?
The tool has zero parameters and schema description coverage is 100%, so there are no parameter semantics to clarify. Per the rubric, a no-parameter tool earns a baseline of 4 because parameter ambiguity is nonexistent.
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 ('Returns') and a clear resource ('the current GodPrompt version') plus a summary, which makes the tool's purpose immediately understandable. It is clearly distinguishable from sibling tools like get_god_prompt, which would return the prompt itself rather than version metadata.
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 clearly implies this tool is for retrieving version information and an included summary, giving an agent enough context to know when to call it. It does not explicitly list exclusions or alternative tools, but the unique resource makes routing straightforward.
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.
v1.0.0classify_taskget_anti_patternsget_core_skillget_gatesget_god_promptget_protocolsget_versionScored across 7 tools
Every tool has a clearly distinct purpose: full-file retrieval, individual progressive-disclosure components, task classification, and version info. The only potential overlap is get_god_prompt versus the core/* tools, but the descriptions explicitly frame them as full payload versus progressive disclosure.
The naming pattern is highly consistent: most tools use get_<resource> for retrieval, and classify_task is a clear verb_noun action. The names map directly to their content and follow a predictable convention across the entire set.
Seven tools is well-scoped for this server's purpose: five content access points, one classification utility, and one version endpoint. Each tool earns its place without redundancy or bloat.
The server covers the complete GodPrompt payload, every core progressive-disclosure file, versioning, and task-type routing. There are no obvious dead ends or missing lifecycle operations for the stated purpose.
Generate contextual prompts and reusable agent skills, evaluate prompts with the 16-dimension Prompt Score, and manage saved work in PromptDrive. Twelve MCP tools also provide authorized access to private Memory for source-grounded answers. Connect over Streamable HTTP using OAuth 2.1 and PKCE. Generation consumes account quota and automatically saves successful results; Memory access follows account permissions and plan limits.
Your prompt library inside your AI: 1,000+ pro templates, frameworks, vocab & pipelines.
Public agentic AI doctrine tools plus authenticated architecture, design, and spec validators.
