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Create architecture diagram

create_diagram

Create architecture diagrams by defining nodes, groups, and edges. Returns a shareable canvas URL and rendered SVG.

Instructions

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).
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that it is a create operation, returns a deterministic layout, and the canvas is anonymous. It does not mention auth or rate limits, but the behavioral traits are well-covered for a creation tool.

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 lengthy but well-organized with clear sections, bullet lists, and an example. Every part adds value; however, it could be slightly more terse without losing clarity.

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

Completeness4/5

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

Given no annotations and no output schema, the description covers input thoroughly and output sufficiently, including return fields and what to do with the URL. It references sibling tools but does not elaborate on their usage. Overall, it is complete for the tool's complexity.

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 adds extensive value beyond the input schema, including a full worked example, lists of valid kind.catalog and type values, edge kinds, and group types. It compensates for any gaps in schema descriptions, making the parameters fully understandable.

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 it creates a new architecture diagram from a user-authored graph and returns a shareable URL, SVG, and Mermaid. It distinguishes itself from sibling tools (edit_diagram, get_diagram) by focusing on creation, though not explicitly naming them.

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

Usage Guidelines4/5

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

The description provides detailed guidance on what to include (semantics, not layout) and what not to send (x/y/position/pinned). It includes a worked example. However, it does not explicitly state when to use this tool over alternatives or when not to use it, though sibling context is available.

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

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