Flowgraf
Server Details
Create and edit architecture diagrams from your AI agent; get an SVG and a live editable canvas.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.7/5 across 3 of 3 tools scored.
Each tool has a unique, clearly defined purpose: create_diagram makes new diagrams, edit_diagram modifies existing ones, and get_diagram fetches diagram data. There is no overlap or ambiguity.
All tools follow a consistent verb_noun pattern in snake_case: create_diagram, edit_diagram, get_diagram. No deviations, making it easy for an agent to predict tool names.
Three tools is on the minimal side but sufficient for the core workflow of creating, editing, and fetching diagrams. It feels slightly thin but is still reasonable given the focused domain.
The tool set covers creation, retrieval, and modification but lacks delete or listing capabilities. An agent cannot clean up or manage multiple diagrams, which is a notable gap for a diagram management server.
Available Tools
3 toolscreate_diagramCreate architecture diagramAInspect
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). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavioral traits: deterministic layout, no manual positions, return values (diagramId, url, svg, mermaid, version), and anonymous canvas claimable later. No contradictions.
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 well-structured with clear sections, a worked example, and no redundant sentences. Each sentence adds value, front-loading the key purpose and result.
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?
Given the tool's complexity (3 params, nested objects, no output schema), the description is complete: it explains all input fields (title, graph, domain), provides examples, and describes output. No missing context for correct use.
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 covers 67% of parameters with descriptions (title and graph sub-properties), but the 'graph' parameter itself lacks a schema description. The description extensively compensates with a detailed explanation of graph structure, catalog/types, edge kinds, and a worked example, adding substantial meaning beyond the 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 clearly states it creates a NEW architecture diagram from a user-authored graph and returns a shareable URL, SVG, and Mermaid. It distinguishes from siblings (edit_diagram, get_diagram) by focusing on creation.
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 includes a worked example illustrating correct input structure. It implicitly guides usage by stating what the user provides (semantics only) and what the tool handles (layout). It mentions alternatives for later changes (get_diagram then edit_diagram). Lacks explicit when-not-to-use but is otherwise clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
edit_diagramEdit architecture diagramAInspect
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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, yet the description discloses all critical behavioral traits: validation, apply, re-layout, re-render, STALE_VERSION error, side effects of remove_node/dropping edges, and insert_between splicing logic.
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?
Well-structured with bullet points and worked example, front-loaded with purpose and prerequisite. Slightly lengthy but every sentence adds value. Could trim some redundancy but still excellent.
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?
Despite no output schema, the description fully specifies the return object { url, svg, mermaid, appliedOps, version }. Covers error handling and all operation effects. Complete for a complex tool.
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 coverage is 67% (diagramId, baseVersion have descriptions). The description massively supplements by detailing each operation's structure and semantics, including a worked example. It adds meaning far beyond the 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 clearly states the tool applies a list of operations to an existing diagram, distinguishing it from create_diagram and get_diagram. It uses specific verb 'Apply' and resource 'EXISTING diagram', making purpose unambiguous.
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?
Explicitly instructs to call get_diagram first and explains version handling. Includes a worked example for insert_between. Lacks explicit 'when not to use' or alternatives, but the prerequisite step is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_diagramGet architecture diagramAInspect
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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description must disclose behavior. It does by specifying the return shape { diagram, version } and implying a read-only fetch. However, it does not explicitly confirm idempotency or mention error conditions.
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?
Two sentences, front-loaded with purpose and details, zero redundant words. Every sentence serves a distinct role.
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 single-parameter fetch tool with no output schema, the description covers purpose, prerequisite usage, and return content. Missing only minor details like errors or pagination, which are not needed here.
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?
One parameter (diagramId) with 100% schema coverage; description adds no extra meaning beyond the schema's 'The diagram to fetch.' Baseline 3 applies.
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 uses specific verb 'fetch' and identifies precise resource: 'raw IR (nodes, groups, edges with their real ids) and its current `version`'. Differentiates from siblings by stating this is a prerequisite for edit_diagram.
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?
Explicitly instructs 'Call this before edit_diagram so your ops reference ids that actually exist and you pass the correct baseVersion', providing clear when-to-use and reasoning.
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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