Skip to main content
Glama

Flowgraf

by in.flowgraf

Create architecture diagram

create_diagram

Create a NEW architecture diagram from a graph that YOU author, and get back a shareable, editable canvas URL plus a rendered SVG and Mermaid.

You produce only the SEMANTICS — nodes, the groups (VPC/cluster/...) they live in, and the directed edges between them. You do NOT lay anything out: never send x/y/position/pinned. A deterministic layout engine computes all geometry and an icon layer picks the pictures from each node's kind.

kind.catalog is one of aws | gcp | azure | k8s | saas | generic, each with rich per-catalog kind.types (e.g. aws:lambda, gcp:bigquery, azure:cosmos_db, k8s:deployment, saas:kafka):

  • "aws" (api_gateway, lambda, s3, rds, dynamodb, sqs, bedrock, kinesis, fargate, eventbridge, aurora, ...).

  • "gcp" (compute_engine, gke, cloud_run, cloud_sql, spanner, firestore, bigquery, pubsub, dataflow, vertex_ai, ...).

  • "azure" (virtual_machine, aks, app_service, functions, blob_storage, sql_database, cosmos_db, service_bus, event_hubs, key_vault, ...).

  • "k8s" (pod, deployment, statefulset, daemonset, job, cronjob, service, ingress, configmap, secret, hpa, ...).

  • "saas" for hosted third-parties (redis, postgresql, mysql, mongodb, kafka, stripe, twilio, auth0, github, cloudflare, ...).

  • "generic" primitive when nothing branded fits: service, database, cache, queue, user, external_system, storage, gateway, function, note.

  • "generic" FLOWCHART kinds for processes/flowcharts: process, decision, terminator, data, document, subprocess. edge.kind is one of: request, response, async_event, data_flow, dependency, network, generic.

WORKED EXAMPLE — a user hitting an API in a VPC that talks to Postgres: { "title": "Web API", "domain": "cloud_architecture", "graph": { "groups": [{ "id": "g_vpc", "label": "VPC", "type": "vpc" }], "nodes": [ { "id": "n_user", "label": "User", "kind": { "catalog": "generic", "type": "user" } }, { "id": "n_api", "label": "API", "kind": { "catalog": "aws", "type": "api_gateway" }, "parentId": "g_vpc" }, { "id": "n_db", "label": "Postgres", "kind": { "catalog": "aws", "type": "rds" }, "parentId": "g_vpc" } ], "edges": [ { "id": "e1", "source": "n_user", "target": "n_api", "kind": "request" }, { "id": "e2", "source": "n_api", "target": "n_db", "kind": "data_flow" } ] } }

Returns { diagramId, url, svg, mermaid, version }. Give the user the url — opening it shows the same diagram on an editable canvas (anonymous; it's theirs to claim by signing in). To change the diagram afterwards, use get_diagram then edit_diagram.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
graphYes
titleYesA short title for the diagram.
domainNoOptional domain hint (default: generic).

TDQS

A4.7/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the engine computes layout, the diagram is anonymous until signed in, and it return an editable canvas. However, it does not mention rate limits or authentication requirements, though these are not critical for creation.

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 headers, bullet points, and a worked example, but it is verbose. Every sentence adds value, but some could be more concise. The front-loading of purpose is good.

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 complexity (nested objects, many enums, no output schema), the description is thorough. It explains the graph structure, catalogs/types, edge kinds, return values, and provides a full example. No gaps are evident for an agent to invoke correctly.

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?

The description greatly enriches the input schema by listing all supported catalogs and types for nodes, edge kinds, and group types. It provides a worked example and explains the structure of the graph object, compensating for the schema's 67% coverage.

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 that the tool creates a new architecture diagram from a user-authored graph, and returns a shareable URL, SVG, and Mermaid. It distinguishes from sibling tools edit_diagram and get_diagram by noting that for changes, one should use those tools.

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 provides explicit guidance on when to use the tool (creating new diagrams), what to provide (semantics only, no layout), and directs users to sibling tools for editing or retrieving diagrams. It also includes a worked example and detailed instructions.

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

All Flowgraf toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: create for new diagrams, get for retrieving existing ones, and edit for modifying them. No ambiguity or overlap.

Naming Consistency5/5

All tool names follow the 'verb_diagram' pattern consistently: create_diagram, edit_diagram, get_diagram, making it easy to predict and remember.

Tool Count4/5

Three tools is slightly thin but appropriate for the focused domain of diagram creation and editing. Each tool encapsulates rich functionality.

Completeness2/5

The set lacks a tool for listing or deleting diagrams, which would be expected for a complete CRUD surface. Core create/read/update is covered, but missing delete and list are significant gaps.

Resources