"UML diagram generation tools and software like Mermaid or Visio" matching MCP tools:

• verify_content_credentials

  Lumethic Photo Verification

  Verify a single image's authenticity — use this when you only have the image and no RAW camera file. Checks its embedded Content Credentials (C2PA) for capture provenance and AI-generation flags, and runs advisory forensic screens (error-level analysis, double-JPEG artifacts, EXIF timestamp consistency, editing-software traces, screen recapture). Free: it does not consume your verification quota. Provide the image inline as image_base64, or — for large files — call create_verification_upload and pass the returned image_object_key. Returns a verification id; poll get_verification, which on completion includes a structured evidence_report (verdict, per-check findings, coverage). Works without an API key on the keyless anonymous tier (rate-limited; returns an anonymous_user_id to reuse). For the strongest forensic check, use verify_photo with a RAW + JPEG pair instead.

  Connector

• mutate

  Agentic Mermaid

  Apply a list of structured edit ops to an existing Mermaid `source` and return the edited diagram. This is the declarative counterpart to `execute`: plain JSON in, plain JSON out, no sandbox. Prefer it for straightforward edits; reserve `execute` for logic the ops don't express. Returns { ok, family, source, verify:{ ok, warnings } } on success, or { ok:false, family, opIndex, error } — where `error` names the offending field and lists the valid ones — when an op is malformed or cannot apply. Ops apply in order and are all-or-nothing: the first failing op stops the batch (its position is `opIndex`) and the input is left untouched. Each op is { "kind": <op>, …fields }. Call `describe_sdk` for the detected family before authoring unfamiliar ops; it returns compact signatures or exact field types, enum values, defaults, and constraints.

  Connector

• visualise

  recordari

  Generate a Mermaid.js flowchart for human visual inspection only. NOT for orphan detection or programmatic analysis — use audit(mode=orphans) to find isolated memories. Output may be truncated for large domains; never infer graph properties (e.g. orphans) from a truncated result. Pass memory_id (memory ID) to see a single memory and all its direct connections. Pass domain to see the full domain graph (most-connected memories first, capped at limit, default 40 max 100). Returns JSON with mermaid, node_count, edge_count, nodes_shown, nodes_total, edges_shown, edges_total, truncated, memories([{id,label}]) and connections([{from,to,relationship}]). If client supports HTML widgets, prefer passing memories and connections to an interactive renderer rather than outputting raw mermaid. If not, output mermaid inside a ```mermaid code block. If truncated is true, note only most-connected memories are shown and nodes_total/edges_total reveal what was dropped.

  Connector

• how_munimji_helps

  HelloBooks AI Agents

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

  Connector

• create_diagram

  mcp

  Generate a professional data architecture diagram from a description and save it to the user's Datadef account. Use this for any data-shaped diagram: medallion and lakehouse architectures, ETL/ELT pipelines, streaming topologies, data mesh, warehouse and dimensional models, lineage maps, and data platform designs. Write a specific prompt. Name the actual technologies (Snowflake, dbt, Airflow, Kafka, Fivetran), the layers or zones you want, and the tables that matter, the diagram is only as detailed as the description. Zone names you give are treated as a specification, not a suggestion. SCOPE: if the user's request is open-ended ("diagram our platform", "show me something"), ask them how much detail they want before calling this, or say which scope you chose. Default to scope "overview". A dense 40-node diagram is impressive and usually not what someone wanted from a one-line request; they can always ask you to expand it. TIMING: this waits up to ~35 seconds; fast generations come back finished, with a preview image and a markdown line to show the user. Slower ones (1-3 minutes total) return a diagram_id while generation continues: call get_diagram with that id after ~60 seconds (poll every 30s) to get the finished diagram. If you cannot call tools again on your own, never leave the user empty-handed: give them the Open link, say it will be ready in about a minute, and offer to show the diagram inline when they next ask. Never call create_diagram a second time while one is still generating; you would create a duplicate and spend another credit. Do not use image generation for these. This produces a real, editable diagram.

  Connector

• search_diagrams

  Vectree

  Search Vectree's library of ~95,000 interactive concept diagrams by meaning, not keywords. Vectree explains how things work as zoomable, labelled schematics — each diagram breaks a topic into nodes you can read or drill into. Use this when the user wants a diagram, a visual explanation, a systems overview, or a map of how the parts of something fit together. Describe the topic in natural language; the search is semantic, so a full question works better than a bare keyword. Results are ranked by how closely they match and by the quality of the model that generated them. Each result carries a slug — pass it to `get_diagram` for the full content of one diagram. Only public, already-generated diagrams are searched. Nothing is generated on demand, so a topic with no match simply has no diagram yet.

  Connector

• query_capacity_factor_v1

  osint

  Query verified U.S. capacity factor — how hard a fleet actually runs — by joining EIA-860M capacity and EIA-923 generation. Requires `data_month`: one ISO month start, e.g. "2026-01-01". If the user names no month, ask which one (or state the month you chose); if a month is not covered, the error lists the months that are — do not retry blindly. capacity_factor = net generation (MWh) / (operating nameplate capacity (MW) × hours in the month), computed over plant×fuel present in BOTH sources, so scope is auto-aligned. Optional `group_by` of `state` and/or `fuel_group`, and `state`/`fuel_group` filters. Returns the capacity factor per group with its generation and capacity, a `coverage` declaration (what share of in-scope capacity/generation matched), and a citation to BOTH the capacity and the generation source row. Basis is nameplate; storage is excluded; the capacity snapshot is matched to the month. Does not determine per-generator capacity factor, a net-summer/winter basis, or months absent from either source.

  Connector

• sip_ladder_example

  Sipflow

  [cost: free (pure CPU, no network) | read-only] Return a hand-curated SIP scenario as a Mermaid `sequenceDiagram` plus a bullet list of step-by-step explanations with RFC references. Use this when the user asks 'show me what X looks like' and you don't have a real trace handy. Available scenarios: basic-call, auth-challenge, cancel-before-answer, early-media, hold-resume, refer-blind, proxy-with-record-route, shaken-attested-invite, bye-glare, redirect-302. Pair with: `search_sip_docs` for vendor-specific quirks of the scenario; `render_sip_ladder` if the user does have a real trace.

  Connector

• render_mermaid

  Diagrams MCP

  Render a Mermaid diagram definition and return the image with metadata. The definition should be valid Mermaid syntax (e.g. flowchart, sequence, class, ER, state, or Gantt diagram). Returns a list of content blocks: the rendered image plus a JSON text block with metadata including a mermaid.live edit link for opening the diagram in a browser editor. Args: definition: Mermaid diagram definition text. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, return a temporary download URL path (/images/{token}) that expires after 15 minutes; if False, return inline image bytes. Defaults to True (URL) — set ``DIAGRAMS_INLINE_DEFAULT=true`` on the server to flip the default. SVG/PDF and PNGs larger than the inline limit always use a download link.

  Connector

• create_diagram

  Flowgraf

  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.

  Connector

• get_design_guide

  mcp

  Datadef's standard for what makes a diagram readable, size, edge discipline, nested zones, annotations, and the tool order for building or editing. Read this BEFORE your first canvas_* call. It is the same standard Datadef's own generator follows, so a diagram you build by hand comes out indistinguishable from a generated one.

  Connector

• get_solution_detail

  Tuki — Travel Marketplace (Chile y Latinoamérica)

  Get full detail for a Tuki solution: description, who it is for, capabilities, status and contact / CTA. Use after `list_solutions` or when the user asks about a specific Tuki product (WhatsApp Booking OS, boutique ticketing, rental inventory software, event post-sale, tailor-made tourism software).

  Connector

• suggest_classes

  Antevo Trademark

  Which Nice classes a business like this files in. Free, no account, no database. The question people cannot answer for themselves: not "is my name taken" but "taken in WHAT". Nice has 45 classes and the numbering is opaque — software you download is class 9, software you log into is class 42, and selling other people's goods is class 35 whatever the goods are. Filing in one and not the other is the most expensive routine mistake in the process. Run this BEFORE screen_mark when someone describes a business rather than naming a class: the classes it returns are what makes a screen mean anything. Relay the reasoning, not just the numbers, and keep the closing caveat — this reports how similar businesses file, and their counsel decides what they actually file.

  Connector

• repo_status

  mcp

  For a diagram that was created from a connected git repository: which repo and branch or tag it tracks, when it last synced, the commit it reflects, and whether daily sync is on. Use repo_refresh to sync it now. Only the owner of the repository connection can use this.

  Connector

• getApiJob

  Ludo AI Game Assets

  Poll the status of a generation job started by any tool. Every tool returns `{id, status}`; call getApiJob with that id until status is `succeeded` (then read `result`, shaped exactly like the tool's documented output) or `failed` (read `error`). Poll every few seconds, or pass `wait: 30` to long-poll - the call is held up to 30 seconds (max 60) and returns as soon as the job finishes.

  Connector

• category_landscape

  RightAIChoice — verified AI tool data

  Use this when the user asks what a whole category of AI tools looks like — how crowded it is, how healthy or risky it is overall, which tools in it are strongest, or which are in trouble. Examples: "how risky is the AI video generation market", "what does the code assistant category look like". Returns the number of tools we track in that category, how they distribute across survival bands, the category's vendor-link decay rate, and named examples at both the strongest and weakest ends — each with its own score and last-verified date. Categories are our own classification and tools belong to several at once, so category sizes overlap and never sum to the catalog total. Bands classify risk, not quality — the model has no notion of company size. Not for: choosing between named tools (use compare_tools), finding a tool for a job (use recommend_tools), or market-wide mortality statistics (use deadpool_digest).

  Connector

• category_landscape

  RightAIChoice

  Use this when the user asks what a whole category of AI tools looks like — how crowded it is, how healthy or risky it is overall, which tools in it are strongest, or which are in trouble. Examples: "how risky is the AI video generation market", "what does the code assistant category look like". Returns the number of tools we track in that category, how they distribute across survival bands, the category's vendor-link decay rate, and named examples at both the strongest and weakest ends — each with its own score and last-verified date. Categories are our own classification and tools belong to several at once, so category sizes overlap and never sum to the catalog total. Bands classify risk, not quality — the model has no notion of company size. Not for: choosing between named tools (use compare_tools), finding a tool for a job (use recommend_tools), or market-wide mortality statistics (use deadpool_digest).

  Connector

• g2_reviews

  review-intelligence-mcp-server

  Get G2 software reviews. Returns ratings, pros, cons, use cases. Args: product: Software product name (e.g. 'Salesforce') max_results: Max reviews (default 20)

  Connector

• search_occupations

  O*NET Occupational Data

  Search O*NET occupations by keyword. Returns a list of occupations matching the keyword with their SOC codes, titles, and relevance scores. Use the SOC code from results with other O*NET tools to get detailed information. Args: keyword: Search term (e.g. 'software developer', 'nurse', 'electrician'). limit: Maximum number of results to return (default 25).

  Connector

• eia_search_routes

  eia-energy-mcp-server

  Fuzzy text search across route names, descriptions, and category labels. Resolves natural-language queries like "electricity retail sales by state" or "natural gas imports" to matching route paths. Multi-term queries are also matched term by term, so combining a commodity, a metric, and a sector — "electricity price residential", "coal generation industrial sector" — reaches the route carrying that data even when no single entry reads like the whole phrase. STEO series names are indexed so queries like "ethanol net imports" or "crude oil production forecast" also resolve, and so are facet values, so a fuel type or sector term like "wind" or "anthracite coal" resolves to the route that exposes it, with filter_hint carrying the filter to pass on. Results include isLeaf so you know whether to browse further or query directly. Results with score > 0.72 are weak matches — try a more specific query or use eia_browse_routes to explore the taxonomy. The first call after server start waits 24-30s while the index warms, and at most 45s; every later call returns in milliseconds. Check indexComplete before reading anything into a short or empty result set.

  Connector