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Glama
unionai-oss

Union MCP

by unionai-oss
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

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
run_taskC

Run a task with natural language.

- Based on the prompt and inputs dictionary, determine the task to run
- Format the inputs dictionary so that it matches the task function signature
- Invoke the task

Args:
    project: Project to run the task in.
    domain: Domain to run the task in.
    name: Name of the task to run.
    inputs: A dictionary of inputs to the task.

Returns:
    A dictionary of outputs from the task.
run_workflowB

Run a workflow with natural language.

- Based on the prompt and inputs dictionary, determine the workflow to run
- Format the inputs dictionary so that it matches the workflow function signature
- Invoke the workflow

Args:
    project: Project to run the workflow in.
    domain: Domain to run the workflow in.
    name: Name of the task to run.
    inputs: A dictionary of inputs to the workflow.

Returns:
    A dictionary of outputs from the workflow.
get_taskD

Get a union task.

get_executionD

Get personalized union execution.

list_tasksC

List all tasks in a project and domain.

list_workflowsC

List all workflows in a project and domain.

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

C2.8/5.0

Scored across 6 tools

Disambiguation4/5

Most tools have distinct purposes: get_execution and get_task retrieve specific items, list_tasks and list_workflows list collections, and run_task and run_workflow execute operations. However, get_task and list_tasks could potentially be confused if an agent isn't clear on whether it needs a single task or multiple tasks, as both relate to tasks in the same domain.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., get_execution, list_tasks, run_workflow). The naming is uniform across all six tools, with clear and predictable conventions that make it easy for agents to understand the action and target resource.

Tool Count5/5

With 6 tools, the count is well-scoped for a server focused on union execution, tasks, and workflows. It covers key operations like retrieval, listing, and execution without being overly sparse or bloated, fitting typical MCP server ranges of 3-15 tools.

Completeness4/5

The tool set provides good coverage for core operations in a union management domain, including getting items, listing collections, and running tasks/workflows. A minor gap exists in the lack of update or delete operations for tasks or workflows, which might limit full lifecycle management, but agents can still perform essential workflows with the available tools.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources