Enables installing and configuring MCP servers from GitHub repositories, with support for authentication via personal access tokens
Provides configuration support for the MCP installer on macOS systems
Provides installation of MCP servers published as npm packages
Supports installation of MCP servers published as Python packages on PyPI
Mentioned as a potential MCP server that can be installed via this installer
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 Installer install the MCP server named mcp-server-fetch for me"
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.
This server is a server that installs other MCP servers for you. Install it, and you can ask Claude to install MCP servers hosted in npm or PyPi for you. Requires npx and uv to be installed for node and Python servers respectively.
Put this into your claude_desktop_config.json (either at ~/Library/Application Support/Claude on macOS or C:\Users\NAME\AppData\Roaming\Claude on Windows):
"mcpServers": {
"mcp-installer": {
"command": "npx",
"args": [
"@anaisbetts/mcp-installer"
]
}
}Hey Claude, install the MCP server named mcp-server-fetch
Hey Claude, install the @modelcontextprotocol/server-filesystem package as an MCP server. Use ['/Users/anibetts/Desktop'] for the arguments
Hi Claude, please install the MCP server at /Users/anibetts/code/mcp-youtube, I'm too lazy to do it myself.
Install the server @modelcontextprotocol/server-github. Set the environment variable GITHUB_PERSONAL_ACCESS_TOKEN to '1234567890'
Install an MCP server whose code is cloned locally on your computer
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | The path to the MCP server code cloned on your computer | |
| args | No | The arguments to pass along | |
| env | No | The environment variables to set, delimited by = |
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 action ('Install') but doesn't explain what installation entails—whether it modifies system files, requires permissions, has side effects, or what the expected outcome is. This leaves critical behavioral traits unspecified.
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 is appropriately sized and front-loaded, making it easy to 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?
Given the complexity of an installation tool with no annotations and no output schema, the description is insufficient. It doesn't cover behavioral aspects like what 'Install' does operationally, potential errors, or system impacts, leaving gaps in understanding for safe and effective use.
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, so parameters are well-documented in the schema. The description adds no additional meaning beyond implying that 'path' points to locally cloned code, but this is redundant with the schema's description. Baseline 3 is appropriate as 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 verb ('Install') and resource ('an MCP server whose code is cloned locally on your computer'), making the purpose understandable. However, it doesn't explicitly differentiate from its sibling tool 'install_repo_mcp_server', which likely handles remote repositories rather than local clones.
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 sibling 'install_repo_mcp_server' or other alternatives. It lacks context about prerequisites, such as needing pre-cloned code, or exclusions for when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Install an MCP server via npx or uvx
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | The package name of the MCP server | |
| args | No | The arguments to pass along | |
| env | No | The environment variables to set, delimited by = |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Install' implies a system modification, the description doesn't address critical aspects like required permissions, potential side effects, whether installation is reversible, or any rate limits/constraints. It mentions execution methods (npx/uvx) but lacks operational context.
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 at just 7 words, front-loading the essential information with zero wasted language. Every word earns its place by specifying the action, target, and execution methods without unnecessary elaboration.
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 an installation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address what happens after installation, potential errors, system requirements, or the relationship to the sibling tool. The agent lacks critical context needed to properly understand and use this 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?
With 100% schema description coverage, all three parameters are documented in the schema. The description adds no additional parameter information beyond what's already in the structured fields, so it meets the baseline expectation without providing extra value. The schema adequately covers parameter purposes and formats.
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 action ('Install') and target ('an MCP server via npx or uvx'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from its sibling tool 'install_local_mcp_server', leaving some ambiguity about when to use each installation method.
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 about when to use this tool versus its sibling 'install_local_mcp_server', nor does it mention any prerequisites, constraints, or alternative approaches. The agent receives no contextual direction about appropriate usage scenarios.
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.
install_local_mcp_serverinstall_repo_mcp_serverThe two tools have clearly distinct purposes: one installs from local code, the other installs via package managers (npx/uvx). There is no overlap or ambiguity between them.
Both tools follow a consistent verb_noun pattern with 'install' as the verb and descriptive suffixes ('local_mcp_server', 'repo_mcp_server'). The naming is perfectly uniform and predictable.
With only 2 tools, the server feels thin for an 'Installer' domain. While it covers two installation methods, it lacks tools for managing installations (e.g., list, update, remove), making the surface incomplete for typical installer workflows.
The toolset is severely incomplete for an installer. It provides installation methods but lacks essential operations like listing installed servers, updating them, uninstalling, or checking status. This will cause agent failures for common management tasks.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Hosted MCP server connecting claude.ai, ChatGPT and other AI apps to your own computer
Augments MCP Server - A comprehensive framework documentation provider for Claude Code
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
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/ZNCOU/mcp-installer'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
