Skip to main content
Glama
inbarajaldrin

MCP Tools Orchestrator

by inbarajaldrin
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
MCP_CLIENT_IPC_URLYesRequired. Set automatically by mcp-client. The URL of the client's IPC server.
MCP_CLIENT_OUTPUT_DIRNoOptional. Shared outputs directory (set by client).

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

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
execute_composed_codeA

Execute Python code that orchestrates tools from multiple MCP servers.

Used to automate a heuristic search or sequential execution using loops and error handling.

Example: ```python from unified_api import *

items = ["item_a", "item_b", "item_c"]
results = []

for item in items:
    server__prepare(target=item, mode="sim")
    result = server__execute_action(name=item, value=123, mode="sim")

    if result.get("result") != "success":
        server__restore_state()
        continue

    server__finalize(item=item)
    results.append({"item": item, "status": "success"})

print(f"Processed {len(results)}/{len(items)} successfully")
```

Returns: output: stdout/stderr from execution (partial results if aborted/timed out) returncode: 0=success, 1=error, -1=timeout status: "success" | "failed" | "aborted" | "timeout" session_id: present when persistent=True and status="success" session_vars: list of saved variable names, present with session_id reason: present when status="aborted" — user cancelled operation. Do not retry. tool: present when status="aborted" — which tool was cancelled

list_available_toolsB

List all available tools from connected MCP servers with their signatures.

Returns dict with keys: servers (dict mapping server name to tool list), total_servers (int), total_tools (int).

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

A3.7/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one discovers available MCP tools and signatures, while the other executes composed Python that orchestrates those tools. There is no overlap in resource or action, so an agent can easily select the right tool.

Naming Consistency5/5

Both names use consistent snake_case and follow a clear verb_noun pattern: execute_composed_code and list_available_tools. The convention is predictable and readable.

Tool Count3/5

Only two tools are provided for an orchestration server, which is thin even if the pair is focused. Each tool earns its place, but the surface lacks supporting operations like session management or execution status checks.

Completeness4/5

Discovery and execution are the core operations needed for code-based orchestration, and both are present. Minor gaps remain around session lifecycle management (e.g., listing, resuming, or deleting saved sessions) and direct cancellation controls.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources