This server is a beginner-friendly guide to the Model Context Protocol (MCP), offering three main capabilities:
📚 Learn MCP Concepts: Get explanations of concepts like tools, resources, prompts, and framework components using the explain_concept tool.
💡 Explore MCP Features: See practical examples of features such as tool_call, resource_read, and prompt_template with the show_example tool.
🔍 Browse MCP Servers: List and explore available MCP servers organized by categories like browser, cloud, database, security, and more using the list_servers tool.
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-Guide explain what MCP tools are"
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 beginner-friendly Model Context Protocol (MCP) server that helps users understand MCP concepts, provides interactive examples, and lists available MCP servers. This server is designed to be a helpful companion for developers working with MCP.
Author: qpd-v
📚 Concept Explanations: Get clear, beginner-friendly explanations of MCP concepts like tools, resources, prompts, and more
🔍 Server Directory: Browse a comprehensive list of available MCP servers organized by category
💡 Interactive Examples: See practical examples of MCP features in action
🛠️ Tutorial Prompts: Step-by-step guides for creating your first MCP tools and resources
Related MCP server: Learn MCP Server
# Using npm
npm install -g mcp-guide
# Using yarn
yarn global add mcp-guideAdd the server to your Claude Desktop configuration (claude_desktop_config.json):
{
"mcpServers": {
"mcp-guide": {
"command": "node",
"args": ["path/to/mcp-guide/dist/index.js"]
}
}
}Restart Claude Desktop
Use the available tools:
explain_concept: Get explanations of MCP concepts
show_example: See practical examples of MCP features
list_servers: Browse available MCP servers by category
# Start the server
mcp-guide
# Or if installed locally
npx mcp-guideGet a beginner-friendly explanation of an MCP concept.
Example concepts:
tools
resources
prompts
server
client
server_types
frameworks
clients
Show a practical example of an MCP feature.
Example features:
tool_call
resource_read
prompt_template
List available MCP servers by category.
Categories:
browser
cloud
command_line
communication
customer_data
database
developer
data_science
filesystem
finance
knowledge
location
monitoring
search
travel
version_control
other
# Clone the repository
git clone https://github.com/qpd-v/mcp-guide.git
cd mcp-guide
# Install dependencies
npm install
# Build the project
npm run build
# Start the server
npm startContributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Interactive server installation from the server list
More interactive examples and tutorials
Enhanced server categorization and search
Get a beginner-friendly explanation of an MCP concept
| Name | Required | Description | Default |
|---|---|---|---|
| concept | Yes | The MCP concept to explain (e.g., 'tools', 'resources', 'prompts', 'server', 'client', 'server_types', 'frameworks', 'clients') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get' implies a read operation, the description doesn't address important behavioral aspects like whether this requires authentication, rate limits, what format the explanation returns, or if it's cached. For a tool with zero annotation coverage, this is a significant 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?
The description is a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a simple tool and front-loads the 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 low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate but lacks completeness. It doesn't explain what the output looks like (text format, length, structure) or provide behavioral context needed since annotations are absent. A 3 reflects the minimum viable level for this simple 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?
Schema description coverage is 100%, so the schema already documents the single parameter 'concept' with examples. The description doesn't add any parameter-specific information beyond what's in the schema, such as explaining the 'beginner-friendly' aspect relates to the output rather than parameter handling. Baseline 3 is appropriate when 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 with a specific verb ('Get') and resource ('explanation of an MCP concept'), and specifies the target audience ('beginner-friendly'). However, it doesn't distinguish this tool from its sibling tools (list_servers, show_example), which would require a 5.
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 guidance on when to use this tool versus its siblings (list_servers, show_example). It doesn't mention any prerequisites, alternatives, or exclusions, leaving the agent with no contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List available MCP servers by category
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes | Server category to list (e.g., 'browser', 'cloud', 'command_line', 'communication', 'database', 'developer', 'filesystem', 'search', 'all') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists servers by category but doesn't describe what the output includes (e.g., server names, statuses, details), whether it's a read-only operation, potential rate limits, or error conditions. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action ('List available MCP servers') and includes the key constraint ('by category'). Every part of the sentence contributes to understanding, making it highly concise and well-structured.
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 annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't cover what the output looks like (e.g., list format, data included), error handling, or behavioral traits like whether it's safe or has side effects. For a tool that likely returns structured data, more context is needed to use it effectively.
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 description mentions 'by category', which aligns with the single parameter 'category' in the input schema. Since schema description coverage is 100% (the parameter has a clear description and enum values), the description adds minimal value beyond what the schema provides. It doesn't explain the semantics of categories (e.g., what 'all' means) or usage nuances, so it meets the baseline for high schema coverage.
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 verb ('List') and resource ('available MCP servers'), specifying they are organized 'by category'. It distinguishes the tool's purpose from its siblings (explain_concept, show_example) by focusing on listing servers rather than explaining concepts or showing examples. However, it doesn't specify what 'available' means (e.g., installed, running, or discoverable), 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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for selecting categories, or how it differs from potential sibling tools in usage scenarios. The agent must infer usage based solely on the tool name and description without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show a practical example of an MCP feature
| Name | Required | Description | Default |
|---|---|---|---|
| feature | Yes | The MCP feature to demonstrate (e.g., 'tool_call', 'resource_read', 'prompt_template') |
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 'shows' an example, implying a read-only or display operation, but doesn't clarify aspects like output format, interactivity, or any constraints (e.g., rate limits, authentication needs). This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.
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, clear sentence that directly states the tool's purpose without unnecessary words or complexity. It is front-loaded and efficiently conveys the essential information, making it easy to parse 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 tool with one well-documented parameter and no output schema, the description is minimally adequate but lacks depth. It doesn't explain what the output entails (e.g., text, code snippet, interactive demo) or address behavioral traits, which is a gap given the absence of annotations. This makes it functional but incomplete for guiding an agent fully.
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 has 100% description coverage, clearly documenting the 'feature' parameter with examples. The description adds no additional meaning beyond what the schema provides, such as elaborating on feature options or usage context. Given the high schema coverage, a baseline score of 3 is appropriate, as the schema adequately handles parameter semantics without extra description value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('show') and resource ('practical example of an MCP feature'), making it understandable. However, it doesn't explicitly distinguish itself from sibling tools like 'explain_concept' or 'list_servers', which might also involve demonstrating or explaining MCP features, leaving some ambiguity about its unique role.
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 guidance on when to use this tool versus alternatives such as 'explain_concept' or 'list_servers'. It lacks explicit context, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone, which is insufficient for optimal 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.
v1.0.0explain_conceptlist_serversshow_exampleEach tool has a clearly distinct purpose with no overlap: explain_concept provides explanations, list_servers enumerates available servers, and show_example demonstrates practical usage. An agent can easily distinguish between these three functions.
All tools follow a consistent verb_noun pattern (explain_concept, list_servers, show_example) with clear, descriptive names. There are no deviations in naming style or conventions.
With only 3 tools, the server feels somewhat thin for a 'guide' purpose, which might suggest broader coverage. However, the tools are well-defined and cover core functions, making this borderline but not severely lacking.
The tools cover key aspects of an MCP guide: explanation, listing, and examples. Minor gaps might include advanced tutorials or troubleshooting, but the surface supports basic learning workflows effectively.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
MCP server for AI dialogue using various LLM models via AceDataCloud
Search and install 4,000+ security-scanned MCP servers from inside any MCP-aware AI client.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/qpd-v/mcp-guide'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
