mcp-prompt-server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-prompt-serverlist available prompts"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🚀 MCP Prompt Server
A lightweight Model Context Protocol server for dynamic prompt management
✨ Features
📁 Dynamic Prompt Loading — Automatically discover and register markdown files from the
prompts/directory📚 Resource Management — Serve documentation and reference materials via MCP resources
⚡ Zero Configuration — Add prompts and resources by simply creating markdown files with YAML frontmatter
🔒 Security First — Built-in path traversal protection, file size limits, and input validation
🎯 MCP Compliant — Full implementation of MCP prompt and resource primitives via FastMCP framework
🌐 Remote Installation — Run directly from GitHub without cloning
Related MCP server: Prompts MCP Server
🔧 Requirements
Python >=3.11
Package manager:
uv(installs automatically withuvx)
Quick Start
Using with Claude Cli
Add this configuration to your Claude Cli MCP settings:
{
"mcpServers": {
"prompt-server": {
"type": "stdio",
"command": "uvx",
"args": [
"--from",
"git+https://github.com/marcmodin/mcp-prompt-server@v0.1.0",
"mcp-prompt-server"
]
}
}
}Using with GitHub Copilot
Create .vscode/mcp.json in your project:
{
"mcpServers": {
"prompt-server": {
"type": "stdio",
"command": "uvx",
"args": [
"--from",
"git+https://github.com/marcmodin/mcp-prompt-server@v0.1.0r",
"mcp-prompt-server"
]
}
}
}Running Remotely
# Run directly from GitHub
uvx --from git+https://github.com/marcmodin/mcp-prompt-server@v0.1.0 mcp-prompt-server
# Debug with MCP Inspector
npx @modelcontextprotocol/inspector uvx --from git+https://github.com/marcmodin/mcp-prompt-server@v0.1.0 mcp-prompt-server📚 Documentation
Architecture — System design and security model
Contributing — Development workflow and guidelines
Prompt Template — Creating prompt files
Available Tools
1 toolpingA
Simple ping tool that returns pong
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It states 'returns pong', implying a response but does not explicitly mention side effects (none expected) or that it is read-only. For a ping tool, this is adequate but could be more explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no extraneous information. Every word is purposeful and 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's simplicity (no parameters, an output schema exists), the description is nearly complete. It could hint at the return value format, but 'returns pong' is sufficient for a standard ping tool.
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 coverage is 100% by default. The description does not need to add parameter details. Baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it's a 'simple ping tool that returns pong', which is a standard health-check tool. The verb 'returns' and resource 'pong' make the purpose unmistakable. No siblings exist to cause confusion.
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 when to use or alternatives are given, but as a ping tool, its usage is universally understood as a connectivity or liveness check. The lack of guidance is acceptable given the tool's simplicity and lack of siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of confusion between tools. The single 'ping' tool is unambiguous.
Only one tool exists, so naming consistency is not an issue. The name 'ping' follows a common, clear convention.
A single 'ping' tool is far too minimal for a server named 'mcp-prompt-server'. The server does not fulfill its implied purpose of providing prompt-related functionality.
The server completely lacks any prompt-related tools. A single ping-pong tool leaves a vast gap in functionality for a server that should handle prompts.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Portable AI memory shared across models and harnesses - plain markdown you own.
Search and reason over your Obsidian-style Markdown vault, right from ChatGPT.
Securely search and manage workspace context files for AI agents and teams.
Serve a folder of Markdown notes as an MCP server: hybrid search, reading, and sourced answers.
Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceFacilitates access and management of Langfuse prompts through the Model Context Protocol, enabling prompt discovery, retrieval, and integration within clients like Claude Desktop and Cursor.173MIT
- AlicenseAqualityDmaintenanceA Model Context Protocol server for managing prompt templates as markdown files with YAML frontmatter, allowing users and LLMs to easily add, retrieve, and manage prompts.52116MIT
- AlicenseNot gradedqualityDmaintenanceProvides custom prompts for Amazon Q by serving markdown files from the local filesystem as prompt templates through the Model Context Protocol.8MIT
- AlicenseNot gradedqualityFmaintenanceEnables loading and serving markdown files as prompts from local folders or GitHub repositories. Supports automatic repository synchronization and YAML frontmatter for prompt metadata.3MIT
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