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., "@server-anthropic Ask Claude to explain quantum computing"
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 that provides access to Anthropic's AI models through their official API. List available models and send messages to Claude using a secure, standardized interface. More about MCP.
List all available Anthropic models
Send messages to any Anthropic model
Secure API key management
Support for workspace segmentation
Automatic API versioning
Comprehensive error handling
Experimental context window management
Related MCP server: Perplexity Insight MCP Server
Unlike direct API integration, this server:
Provides a standardized MCP interface for Anthropic's API
Handles authentication and versioning automatically
Supports workspace isolation for different use cases
Integrates seamlessly with Claude Desktop and other MCP clients
Includes detailed error messages and validation
Potentially extends conversation context windows through distributed processing
This server explores efficient context window management through distributed processing:
Claim: Offloading processing to separate API calls through the MCP server may reduce context window pressure on the main Claude Desktop conversation.
Primary Hypothesis: By branching conversation processing across multiple isolated API instances, the total effective context window of a conversation can be extended beyond standard limitations.
Key Mechanisms:
Main conversation (trunk) retains only final outputs and discussion
Individual API calls maintain isolated contexts
Processing can be distributed across multiple model instances
Testing Scenarios:
Context Window Extension
Compare context limits with and without API offloading
Measure conversation longevity in both scenarios
Track rate limit encounters
Processing Distribution
Haiku for rapid initial processing
Sonnet for medium-sized aggregation
Opus for comprehensive analysis
npm install server-anthropiclist_modelsLists all available Anthropic models and their capabilities.
Arguments:
{
// No arguments required
}Returns:
{
"models": [
{
"name": "claude-3-opus-20240229",
"description": "Most powerful model for highly complex tasks",
...
},
...
]
}send_messageSend a message to an Anthropic model using the Messages API.
Arguments:
{
"messages": {
"type": "array",
"description": "Array of messages to send",
"items": {
"role": "user | assistant",
"content": "string"
},
"required": true
},
"model": {
"type": "string",
"description": "Model ID to use",
"default": "claude-3-opus-20240229"
},
"max_tokens": {
"type": "number",
"description": "Maximum tokens to generate",
"default": 1024
}
}Returns:
{
"content": [
{
"type": "text",
"text": "Model response..."
}
]
}Add to your claude_desktop_config.json:
{
"mcpServers": {
"anthropic": {
"command": "npx",
"args": ["-y", "server-anthropic"],
"env": {
"ANTHROPIC_API_KEY": "your-api-key-here",
"ANTHROPIC_WORKSPACE_ID": "optional-workspace-id"
}
}
}
}Variable | Required | Description |
ANTHROPIC_API_KEY | Yes | Your Anthropic API key from console.anthropic.com |
ANTHROPIC_WORKSPACE_ID | No | Optional workspace ID for usage segmentation |
@modelcontextprotocol/sdk - Core MCP functionality
@anthropic-ai/sdk - Official Anthropic API client
The server provides detailed error messages for common issues:
Invalid API key
Missing required parameters
Rate limiting
Model-specific errors
Network connectivity issues
Clone the repository
Install dependencies: npm install
Set up environment variables in .env:
ANTHROPIC_API_KEY=your-api-key
ANTHROPIC_WORKSPACE_ID=optional-workspaceStart the server: npm start
Current beta version focuses on:
Core MCP server functionality
Context window management testing
API integration stability
Initial hypothesis validation
Known limitations:
Context window behavior requires further testing
Rate limiting patterns under investigation
Workspace segmentation impact on context management undefined
MIT
List all available Anthropic models and their capabilities. Access requires a valid API key configured through the ANTHROPIC_API_KEY environment variable.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It accurately discloses the need for an API key, which is a critical behavioral constraint. No contradictions are present.
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, well-structured sentence that front-loads the action and resource, with no wasted words.
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 no output schema and zero parameters, the description adequately covers purpose and access requirements. It hints at output (capabilities) but does not detail structure, which is acceptable for a simple list 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 zero parameters, the schema description coverage is 100%. The description does not add parameter information as there are none, which is appropriate. Baseline for 0 parameters is 4.
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 'list' and the resource 'all available Anthropic models and their capabilities', effectively distinguishing it from the sibling tool 'send_message' which is for messaging.
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 explicitly mentions a prerequisite (valid API key) and implies usage for listing models. While it does not explicitly exclude other scenarios, the sibling context aids in differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Send a message to an Anthropic model using the Messages API. Requires authentication via ANTHROPIC_API_KEY environment variable. Optionally supports workspace segmentation via ANTHROPIC_WORKSPACE_ID.
| Name | Required | Description | Default |
|---|---|---|---|
| messages | Yes | Array of messages to send | |
| model | No | Model ID to use (e.g., claude-3-opus-20240229) | claude-3-opus-20240229 |
| max_tokens | No | Maximum tokens to generate |
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 only mentions authentication requirements, omitting important behavioral traits such as rate limits, error handling, whether the operation is destructive, or what the response contains. This is insufficient for an agent to fully understand tool 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 concise with two sentences, no redundant information, and front-loads the core purpose. Every word contributes value.
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), the description adequately covers purpose and authentication but fails to mention the return value or behavior. It is minimally viable but has clear gaps in completeness.
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 the schema already documents all parameters. The description adds no additional parameter semantics beyond the schema, warranting a baseline score of 3.
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: 'Send a message to an Anthropic model using the Messages API.' It identifies the specific verb (send), resource (message to Anthropic model), and API context. It differentiates from the sibling tool 'list_models' which lists models, not sending messages.
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 by mentioning authentication requirements (ANTHROPIC_API_KEY) and optional workspace segmentation. However, it does not explicitly state when not to use this tool or mention alternatives, limiting guidance for the AI agent.
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.
v0.1.0list_modelssend_messageThe two tools have clearly distinct purposes: list_models provides information about available models, while send_message executes a message to a model. There is no overlap or ambiguity.
Both tool names follow a consistent verb_noun pattern with underscores: list_models and send_message. The naming is clear and predictable.
With only 2 tools, the server feels thin for its intended purpose of interacting with the Anthropic API. While the tools are essential, additional tools like streaming or model details would be expected.
The server covers basic operations (list models and send a message) but lacks common features like streaming, message history, or batch operations. Notable gaps exist for a full API surface.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Use AI models for chat, image, and video generation from Claude Code and other MCP hosts.
Persistent context for Claude. Your AI always knows your projects and next actions across sessions.
Connect AI agents to Replynodes over the Model Context Protocol.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/emzimmer/server-anthropic'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
