Create and edit architecture diagrams from your AI agent; get an SVG and a live editable canvas.
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Scored across 3 tools
Each tool maps to a distinct lifecycle stage: create a new diagram, fetch an existing diagram, or modify an existing diagram. There is no overlap in purpose or ambiguous boundary between them. Even though create_diagram and edit_diagram both accept graph semantics, their roles are clearly separated by whether the diagram already exists.
All three tool names follow the exact same verb_noun snake_case pattern: create_diagram, edit_diagram, get_diagram. This is perfectly predictable and consistent with no mixed conventions or vague verbs.
Three tools is a reasonable, focused surface for a diagram creation and editing server. It is slightly minimal, but the three tools cover the core authoring loop well and each earns its place. A fourth tool for deletion or listing would make it feel more complete, but the current count is not problematic.
The core lifecycle of create, read, and edit is fully covered, including rich granular operations through edit_diagram. The obvious gap is the lack of a delete_diagram operation and no way to list diagrams, but these are workable omissions for the server's apparent purpose. Agents can still accomplish the main diagram-authoring workflow without dead ends.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| graph | Yes | ||
| title | Yes | A short title for the diagram. | |
| domain | No | Optional domain hint (default: generic). |
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.
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.
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.
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.
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.
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.
Apply a list of operations to an EXISTING diagram. The ops re-use this tool's op vocabulary; you author them, we validate + apply + re-layout + re-render.
ALWAYS call get_diagram(diagramId) first: it returns the current ids and the version. Pass that version as baseVersion. If the diagram changed since you fetched it, you get a STALE_VERSION error telling you the current version — refetch with get_diagram, recompute your ops, and retry.
The operations (each element of ops):
add_node { op, node:{ id, label, kind, parentId? } }
remove_node { op, id } (also drops edges touching the node)
update_node { op, id, patch:{ label?, kind?, parentId?, metadata? } }
add_edge { op, edge:{ id, source, target, kind, label?, directed? } }
remove_edge { op, id }
update_edge { op, id, patch:{ source?, target?, label?, kind?, directed? } }
add_group { op, group:{ id, label, type, parentId? } }
remove_group{ op, id }
move_to_group { op, nodeId, groupId } (groupId null un-nests the node)
set_layout { op, patch:{ direction?, spacing? } }
insert_between { op, newNode:{ id, label, kind, parentId? }, sourceId, targetId, inKind?, outKind? }
insert_between IS THE KEY OP for "add X between A and B" requests. It splices newNode onto the existing A→B edge: removes that edge, adds the node, and wires A→newNode→B so the connection re-routes through it automatically.
WORKED EXAMPLE — "add a Redis cache between the API and the DB" on the diagram above:
get_diagram(diagramId) → shows nodes n_api, n_db and version 1.
edit_diagram({ diagramId, baseVersion: 1, ops: [ { "op": "insert_between", "sourceId": "n_api", "targetId": "n_db", "newNode": { "id": "n_redis", "label": "Redis", "kind": { "catalog": "saas", "type": "redis" }, "parentId": "g_vpc" }, "inKind": "request", "outKind": "data_flow" } ] }) The API→DB edge is gone and now flows API→Redis→DB. Never send x/y/position — geometry is computed for you.
Node kinds: catalog ∈ {aws, gcp, azure, k8s, saas, generic} with rich per-catalog types (e.g. aws:lambda, gcp:bigquery, azure:cosmos_db, k8s:deployment, saas:kafka), plus generic flowchart kinds (process, decision, terminator, data, document, subprocess).
Returns { url, svg, mermaid, appliedOps, version }.
| Name | Required | Description | Default |
|---|---|---|---|
| ops | Yes | ||
| diagramId | Yes | The diagram to edit (from create_diagram or get_diagram). | |
| baseVersion | Yes | The version you are editing against — get it from get_diagram. Stale → STALE_VERSION. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral burden and succeeds: it discloses validation, apply, re-layout, re-render behavior; stale-version error semantics; automatic geometry computation; and edge-dropping side effects of remove_node. It also explains insert_between's splicing effect on existing edges, making side effects explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but structurally dense: prerequisite call, op list, highlighted op, worked example, geometry warning, kind catalog, and return values. Every section earns its place given the tool's complexity, and the most critical usage caveat (fetch version first) is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Since there is no output schema, the description supplies return shape ({ url, svg, mermaid, appliedOps, version }) and key preconditions. It covers op vocabulary, kinds, side effects, and error handling, leaving no critical gap for an agent to invoke the tool correctly on a mutating endpoint.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67% but only at the top level; the ops array itself has no schema description. The description compensates fully by documenting every op variant, required fields, purpose, and a concrete worked example. It adds meaning well beyond the raw JSON schema, especially for insert_between and baseVersion semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description opens with a specific verb+resource: 'Apply a list of operations to an EXISTING diagram.' It clearly differentiates itself from siblings (create_diagram, get_diagram) by targeting existing diagrams and enumerating the mutation operations. The title and purpose align without tautology.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit 'ALWAYS call get_diagram(diagramId) first' instructions, including how to handle STALE_VERSION errors and retry. It also gives a worked example for the key op and warns against sending x/y/position, giving the agent clear when-and-how-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a diagram's raw IR (nodes, groups, edges with their real ids) and its current version. Call this before edit_diagram so your ops reference ids that actually exist and you pass the correct baseVersion. Returns { diagram, version }.
| Name | Required | Description | Default |
|---|---|---|---|
| diagramId | Yes | The diagram to fetch. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It clearly communicates that this is a fetch operation returning raw IR and a version, and that it is meant to provide accurate ids for subsequent edits. It does not mention auth or error behavior, but for a simple read operation the transparency is strong.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler. It front-loads the core behavior, then adds the crucial workflow context and return shape. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a one-parameter read tool with no output schema. The description covers what is fetched, what is returned, and why it should be called before edit_diagram. Nothing essential for an agent to invoke it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the schema already documents diagramId as 'The diagram to fetch.' The description adds context about what a diagram consists of, but does not add meaningful semantic detail about the parameter beyond the schema, matching the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'Fetch a diagram's raw IR (nodes, groups, edges with their real ids) and its current version.' It clearly states what the tool returns and why it matters, distinguishing it from the create/edit siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit workflow guidance: 'Call this before edit_diagram so your ops reference ids that actually exist and you pass the correct baseVersion.' This clearly states when to use it, though it does not explicitly discuss when not to use it or name alternatives beyond the edit tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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