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Glama
cagataycali

Strands MCP Server

by cagataycali
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
mcp_clientA

Test and interact with MCP servers.

This tool provides a complete MCP client implementation for testing and using MCP servers from within a Strands Agent.

Returns: Result dictionary with status and content

Examples: # Connect to HTTP server mcp_client( action="connect", connection_id="my-server", transport="http", server_url="http://localhost:8000/mcp" )

# Connect to stdio server
mcp_client(
    action="connect",
    connection_id="stdio-server",
    transport="stdio",
    command="python",
    args=["mcp_server_stdio.py"]
)

# List tools from connection
mcp_client(action="list_tools", connection_id="my-server")

# Call a tool
mcp_client(
    action="call_tool",
    connection_id="my-server",
    tool_name="calculator",
    tool_args={"expression": "2 + 2"}
)

# List all connections
mcp_client(action="list_connections")

# Disconnect
mcp_client(action="disconnect", connection_id="my-server")

Notes: - stdio transport: Server must be launchable as subprocess - HTTP transport: Server must be already running - Connections are maintained in global state for reuse

greetC

Greet someone with different styles.

Returns: str: A greeting message

invoke_agentA

Invoke the full strands-mcp-cli agent with a natural language prompt. Use this for complex queries that require reasoning across multiple tools or when you need a conversational response from the agent.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 3 tools

Disambiguation3/5

The three tools have distinct primary purposes—greeting, invoking an agent, and interacting with MCP servers—but there is some functional overlap between 'invoke_agent' and 'mcp_client', as both can handle MCP-related tasks. The descriptions clarify their roles, but an agent might be uncertain when to use one over the other for complex MCP operations.

Naming Consistency3/5

The naming is mixed: 'greet' uses a simple verb, 'invoke_agent' follows a verb_noun pattern, and 'mcp_client' uses a noun-based name. While readable, there is no consistent convention across all tools, leading to a lack of predictability in the naming scheme.

Tool Count2/5

With only three tools, the server feels under-scoped for its apparent purpose of MCP interaction and agent invocation. The 'mcp_client' tool is comprehensive, but the limited set may force agents to rely heavily on it for varied tasks, suggesting a mismatch between the tool count and the server's broad functionality.

Completeness2/5

The server lacks clear coverage for common MCP workflows. While 'mcp_client' provides a generic interface, there are no dedicated tools for specific actions like listing servers, managing connections, or handling errors. This forces agents to use a single, multi-purpose tool, which can lead to inefficiencies and potential gaps in handling nuanced scenarios.

Maintenance

ActivityStale
ResponsivenessNo issues

Resources