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flowgraf

by abhishek-genailytics
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TypeScript
Hybrid

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).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full transparency burden. It does well by disclosing that the agent supplies only semantics, that layout is deterministic and icon selection is automatic, that coordinates/pinning must not be sent, and that the result is an editable anonymous canvas. It stops short of detailing persistence, idempotency, or failure behavior, but the key behavioral traits are clearly surfaced.

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 long, but the tool is genuinely complex and the length is justified by the catalog taxonomy, layout constraints, and worked example. It is well structured with clear sections and front-loads the core purpose and outputs. A small amount of repetition with schema enums exists, but it aids usability rather than bloating the definition.

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?

For a complex tool with no output schema and no annotations, this description is remarkably complete. It explains the return payload, tells the agent to give the URL to the user, clarifies that the diagram is claimable by signing in, and describes the follow-up flow via get_diagram and edit_diagram. An agent has everything needed to invoke and explain the result.

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 schema has moderate coverage, but the description adds substantial meaning: the full catalog vocabulary (aws, gcp, azure, k8s, saas, generic), example kinds per catalog, edge kind semantics, group types, and the rule that x/y/position/pinned are forbidden. The worked example demonstrates exactly how nodes, groups, edges, and parentIds fit together, going well 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 opens with a specific verb and resource: "Create a NEW architecture diagram" from a graph the agent authors, and it states the concrete outputs (URL, SVG, Mermaid). It explicitly distinguishes itself from siblings by saying "NEW" and by pointing to edit_diagram/get_diagram for later changes.

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 usage context is explicit: use this tool when authoring a new diagram from semantic graph data. It also gives a clear when-not: "To change the diagram afterwards, use get_diagram then edit_diagram." This routes the agent to the correct sibling without ambiguity.

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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