Skip to main content
Glama

umlforge_ai_agent

Read-only

Design AI agent pipelines and multi-agent orchestration systems. Visualize agent sequences, tool calls, memory strategies, and failure behaviors.

Instructions

Design an AI agent pipeline or multi-agent orchestration system.

USE THIS WHEN:

  • You are building a system where LLMs call tools or hand off to other agents

  • You want to visualise a multi-agent workflow (planner → researcher → writer)

  • You need to document tool access, memory strategy, and failure behaviour

NOT FOR:

  • General system architecture → use umlforge_stakeholder_arch

  • Standard synchronous API flows → use umlforge_api_sequence

  • Event-driven pipelines without LLM agents → use umlforge_event_driven

Produces:

  • Agent pipeline sequence: agents as participants with model names, tool calls as self-calls, human-in-the-loop gates, retry/fallback logic

  • Agent component map: agents, tool deps, memory, external integrations

  • Agent responsibility matrix: model, role, tools, inputs, outputs, failure behaviour

  • Risk & observability note: hallucination hotspots, validation gates, logging points

  • Tool overload flags (⚠️) for agents with more than 5 tools

  • (report_mode=True) Agent Pipeline Analysis Notes: pipeline risks, coverage gaps, reliability quick wins

Args: pipeline_purpose: What the agent system does (e.g. "Research pipeline that queries the web and drafts a report"). agents: Agents and their roles (e.g. "Planner [claude-opus-4], Researcher [claude-sonnet-4]"). tools_available: Tools agents can call (e.g. "web_search, execute_code") (optional). orchestration_approach: Coordination strategy — sequential, DAG, hierarchical, parallel fan-out (optional). memory_strategy: Memory approach — shared context, vector memory, Redis, none (optional). report_mode: True → also produce Agent Pipeline Analysis Notes. Pro/Team/Enterprise only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentsYes
report_modeNo
memory_strategyNo
tools_availableNo
pipeline_purposeYes
orchestration_approachNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate read-only and open-world hints. The description adds context about produced artifacts (diagrams, reports), licensing restrictions for report_mode, and warning flags for tool overload. It does not contradict annotations and provides valuable behavioral insights beyond the structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (USE THIS WHEN, NOT FOR, Produces, Args) but is somewhat verbose, especially the 'Produces' list. It could be slightly more concise without losing clarity. Still, the organization aids readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (multi-agent orchestration) and the presence of an output schema, the description covers all essential aspects: purpose, usage, produced artifacts, parameters, and constraints (licensing). It is thorough enough for an agent to effectively invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the description compensates with a detailed 'Args' section explaining each parameter's purpose with examples (e.g., 'pipeline_purpose: What the agent system does (e.g. "Research pipeline...")'). This adds significant meaning beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Design an AI agent pipeline or multi-agent orchestration system.' It provides specific use cases and distinguishes from siblings with explicit 'NOT FOR' sections, making the tool's role unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes dedicated 'USE THIS WHEN' and 'NOT FOR' sections that list specific scenarios and alternative tools, such as 'use umlforge_stakeholder_arch' for general architecture. This provides excellent guidance for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dave1362/umlforge-connector'

If you have feedback or need assistance with the MCP directory API, please join our Discord server