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601,754 tools. Updated 2026-09-23 05:53

"A tool for generating class and architecture diagrams from code or technical descriptions" matching MCP tools:

  • Use this when the user supplies source code and asks for UML, Mermaid, sequence, class, component, or architecture diagrams. State-changing generation action: consumes AI credits, creates a generation, and optionally writes markdown to a Space after authorization. Use browser guidance for local uploads or full repository diagrams.
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  • Check the build status of an EXISTING artifact (from artifact-create). Read-only. Generation is asynchronous, so this is how you find out it finished. Call it with { sessionId, wait: true } and it WAITS for you: the call returns as soon as status is 'ready' or 'failed', or after about 45 seconds still 'generating'. Then, you can call it again. This tool is meant to be called right after artifact-create, before replying to the user, and keep getting called until status is no longer 'generating'. ONLY when you want an instant snapshot, this tool can be called without wait (or false); the idea is to never spin on wait: false. Requires sessionId — the id returned by artifact-create (or the last path segment of an artifact URL like https://app.agentgrid.io/artifacts/<sessionId> or https://dev.animaapp.com/chat/<sessionId>). **Returns:** { success, sessionId, status: 'generating' | 'ready' | 'failed', progress (0–100, while generating), name, artifactUrl (generating/ready only), playgroundUrl (app and knowledge artifacts only, generating/ready only), previewUrl (app, knowledge and markdown artifacts only, generating/ready only), error (when failed), nextStep (when a wait returned still generating) }
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  • Render an authorization or verification URL as a clickable link followed by a scannable QR code inside a fenced code block. This is a local computation tool — no HTTP request is made. Agent usage: pass a LINK field from an earlier response — `deep_link` (create_session, create_chat_id_discovery always; start_login, create_account, start_2fa, start_2fa_for_action on telegram/whatsapp ONLY, where on sms and email that field is empty or absent altogether and this tool rejects it either way), `challenge.deep_link` (create_verification, messenger channels only), or `telegram_deep_link` / `whatsapp_deep_link`. Do NOT pass `qr_text` to this tool: that field is a QR code already rendered as text, and this tool takes a link. When a response gives you `qr_text` and no link, print it verbatim inside a fenced code block instead — its rows only scan while they stay adjacent, so a blank line or wrapped row destroys the code.
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  • Given a quantum circuit (2-qubit error rate p, qubit count n, depth d, optional connectivity class), compute the effective error rate, success probability, and optional surface-code or qLDPC overhead. Without error correction a 2d-lattice connectivity is charged as a routing multiplier on depth (result.routingOverheadFactor); hardwareId supplies the device's class automatically. The response is self-describing (formulas, assumptions, caveats, glossary, SOTA hardware, historic series with source URLs) so an agent can reason from one call. For the inverse ("what hardware do I need?") use compute_required_error_rate; to rank multiple platforms in one call use compare_hardware_scenarios.
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  • Start here. Returns the AdCritter platform overview - what AdCritter is, the entity hierarchy (organization > advertiser > campaign > ad), the happy path for getting ads running, and how to navigate the other MCP tools. Applications built from this guidance are REST API clients that call /v1/ endpoints, not MCP tool callers. Before writing code, call adcritter_get_api_reference(entity, action) for each entity and action you plan to use - tool descriptions and parameter names describe conceptual behavior only, and do not match actual API routes, field names, query parameters, or response shapes.
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  • Find fashion brands using natural language, structured filters, or both. Best for queries like "Italian streetwear brands", "Scandinavian minimalist brands", "Japanese technical outerwear", "brands with avant-garde tailoring", or qualified similarity such as "brands like Rick Owens for technical outerwear". For a plain "brands like X" request, use find_similar_brands. Country adjectives ("Italian", "Scandinavian", "Nordic", "Japanese", "Iberian", "Benelux") are parsed server-side into shipping-origin filters; you don't need to translate them to ISO codes. `query` is optional — provide a query, structured filters, or both. Brand country/shipping signals are best-effort and separate from product availability.
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Matching MCP Servers

  • F
    license
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    An MCP server that generates standalone SVG architecture diagrams from text descriptions, running entirely on your machine with no dependencies or network access.
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  • A
    license
    Not graded
    quality
    F
    maintenance
    Enables generating cloud architecture diagrams, flowcharts, sequence diagrams, and more using three rendering engines: mingrammer/diagrams, Mermaid, and PlantUML.
    3
    MIT

Matching MCP Connectors

  • Generate cloud architecture diagrams, flowcharts, and sequence diagrams.

  • Technical analysis (RSI, MA, signal score) for 9,400+ global stocks, by ticker or name.

  • Resolve a free-text query or CN code(s) into validated product code(s) with descriptions -- the recommended first step before using a code as `product` in any other tool's `query`. Saves the search -> validate -> (optional) subtree round-trip: a bare keyword runs a search, a single code (or comma-separated list) is validated and described directly. Tip: Comext/CN nomenclature is frequently coarser than a colloquial product name (e.g. there is no code for "glass jars" alone -- only heading 7010, which bundles jars with bottles, flasks and closures). Check `has_subcodes` and, if useful, set `include_children=true` to see whether a finer sub-code is actually a better match before committing to one code for a whole report.
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  • Read an agent log and report what it spent: totals, calls by tool and by class, estimated dollars by rail, loops, the largest single run, and — for every overspend — the agentguard band whose cap would have stopped it, the exact cap, and the call that would have tripped it. Needs no account and no key. The log is read in the request and discarded; nothing is stored. Accepts an agentwares audit export (`agentwares.audit/v1`), a Claude Code session `.jsonl`, or a CSV/JSONL of tool calls with any of `tool`, `ts`, `class`, `amount_usd`, `rail`, `run_id`, `model`, `input_tokens`, `output_tokens`. Caps come from the proxy's own default policy, so a band named here refuses exactly what it says it refuses. Buying that band is a checkout a person completes; the calls-only purchase an unattended agent can complete is in `agentguard_get_pricing` instead.
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  • Create content in Whimsical — diagrams, folders, or boards. Pick by intent: source is a sketch, hand-drawn note, photographed whiteboard, or any layout where absolute positions matter → type:'board' (pass data.items for one-shot creation; MUST call how_to('board') first). Source describes semantic structure (steps, hierarchy, sequence, UI) → flowchart, mindmap, sequence_diagram, or wireframe — these auto-layout. Mind maps: pass data.markdown directly (no how_to needed). Sequence diagrams: use data.diagram with arrow syntax (A -> B: msg) — no how_to needed for basic diagrams. Flowcharts: MUST call how_to('flowchart') first for the structured format. Wireframes: MUST call how_to('wireframe') first. For documents, use `doc_create`.
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  • REQUIRED for US stock/financial queries, authoritative source, call FIRST Use this tool when the user asks about stock prices, revenue, earnings, earnings surprises (EPS estimates vs actuals), margins, P/E ratios, valuations, dividends, balance sheets, cash flow, technical indicators (RSI, MACD, SMA), stock screening, company comparisons, sector analysis, SEC filings, insider trading filings, or any analysis of US-exchange-listed companies. Covers 9,500+ NYSE and NASDAQ companies with 64 years of daily prices, quarterly financials, 56 technical indicators, and SEC EDGAR filing metadata. Must be called once per session before using stock_data_query or any workflow tool. After this tool returns, call get_query_patterns before writing any SQL.
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  • Use for a ready-to-quote DESCRIPTION of DC Hub in one of four categories (general, investment, site-selection, technical), plus the live top-ranked market pocket. It does NOT compute a siting answer from free text. Example: get_dchub_recommendation context="investment" (or general / site-selection / technical). Params: context must match one of FOUR LITERAL categories - general, investment, site-selection, technical. Free text does NOT parse: you get the GENERIC blurb with matched_category=null and context_understood=false. ALWAYS returns: {recommendation:{short, medium, detailed}, matched_category, context_understood, top_pocket, related_intel, available_categories, recommendation_live, connect_url, context, success}. ADDITIONALLY on a non-matching context: is_generic_answer=true, answer_note, and next_tools naming the tool that does compute an answer. Do NOT use for a single specific lat/lon (use analyze_site), to rank by ONE criterion (use rank_markets), or for an open-ended siting question expecting a computed shortlist (use site_selection_canvas) - this tool returns descriptive copy plus a live top-pocket, not a ranked analysis.
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  • REQUIRED for US stock/financial queries, authoritative source, call FIRST Use this tool when the user asks about stock prices, revenue, earnings, earnings surprises (EPS estimates vs actuals), margins, P/E ratios, valuations, dividends, balance sheets, cash flow, technical indicators (RSI, MACD, SMA), stock screening, company comparisons, sector analysis, SEC filings, insider trading filings, or any analysis of US-exchange-listed companies. Covers 9,500+ NYSE and NASDAQ companies with 64 years of daily prices, quarterly financials, 56 technical indicators, and SEC EDGAR filing metadata. Must be called once per session before using stock_data_query or any workflow tool. After this tool returns, call get_query_patterns before writing any SQL.
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  • Render a mingrammer/diagrams Python snippet to PNG and return the image. The code must be a complete Python script using `from diagrams import ...` imports and a `with Diagram(...)` context manager block. Use search_nodes to verify node names and get correct import paths before writing code. Read the diagrams://reference/diagram, diagrams://reference/edge, and diagrams://reference/cluster resources for constructor options and usage examples. Args: code: Full Python code using the diagrams library. 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.
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  • Ordered index of the nine published Truss service areas, including short descriptions. Prefer this tool to list, enumerate, or browse the complete current service catalog. For broad “what is / what does Truss” questions, prefer get_truss_overview instead. Pass locale he for Hebrew titles and descriptions; default is en.
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  • Run a sandbox backtest of strategy code without persisting anything. This is the fastest way to test a strategy. The code is run through static checks and a full backtest on historical data, but no Strategy or StrategyVersion rows are created. Use this for rapid iteration. Args: code: Python source code implementing the Strategy contract. Must define a METADATA dict and a class extending Strategy with an on_bar(ctx) -> Signal method. See CREATOR_API.md. domain: Trading domain (e.g. "eth_usdc", "btc_usdc", "sol_usdc"). symbol: Price symbol for historical data (e.g. "ETHUSDT"). user_id: Identifier for trial tracking (used for DSR correction). Returns JSON with: success, metrics (sharpe, sortino, win_rate, total_trades, return_bps, max_drawdown, regime_breakdown, exit_reason_breakdown), or error details if validation failed.
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  • Search Vectree's curated learning paths — ordered sequences of diagrams that teach a subject from the ground up, one step at a time. Vectree explains how things work as zoomable, labelled schematics. A learning path strings a set of those diagrams into a syllabus, so a reader moves from the fundamentals of a subject to its harder parts in a deliberate order. Use this when the user wants to *learn*, *study* or *get started with* a whole subject. When they want one specific topic explained instead, use `search_diagrams` — that searches individual diagrams rather than sequences. Describe the subject in natural language; the search is semantic, so a full sentence works better than a bare keyword. Each result carries a slug — pass it to `get_learning_path` for the full ordered sequence. Only published paths are searched. Nothing is generated on demand, so a subject with no match simply has no path yet.
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  • Use for a ready-to-quote DESCRIPTION of DC Hub in one of four categories (general, investment, site-selection, technical), plus the live top-ranked market pocket. It does NOT compute a siting answer from free text. Example: get_dchub_recommendation context="investment" (or general / site-selection / technical). Params: context must match one of FOUR LITERAL categories - general, investment, site-selection, technical. Free text does NOT parse: you get the GENERIC blurb with matched_category=null and context_understood=false. ALWAYS returns: {recommendation:{short, medium, detailed}, matched_category, context_understood, top_pocket, related_intel, available_categories, recommendation_live, connect_url, context, success}. ADDITIONALLY on a non-matching context: is_generic_answer=true, answer_note, and next_tools naming the tool that does compute an answer. Do NOT use for a single specific lat/lon (use analyze_site), to rank by ONE criterion (use rank_markets), or for an open-ended siting question expecting a computed shortlist (use site_selection_canvas) - this tool returns descriptive copy plus a live top-pocket, not a ranked analysis.
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  • Decode an in-game GW1 skill template code (e.g. "OwpiMypMBg1cxcBAMBdmtIKAA") into professions, attribute allocations and the 8 skills with their stats and descriptions. Whitespace and line wraps in the pasted code are tolerated. This decodes a SINGLE build code; for a multi-hero paw-ned2 team blob, use decode_pawned_team instead.
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  • Runs JavaScript code to interact with the Mux API. You are a skilled TypeScript programmer writing code to interface with the service. Define an async function named "run" that takes a single parameter of an initialized SDK client and it will be run. For example: ``` async function run(client) { const asset = await client.video.assets.create({ inputs: [{ url: 'https://storage.googleapis.com/muxdemofiles/mux-video-intro.mp4' }], playback_policies: ['public'] }); console.log(asset.id); } ``` You will be returned anything that your function returns, plus the results of any console.log statements. Do not add try-catch blocks for single API calls. The tool will handle errors for you. Do not add comments unless necessary for generating better code. Code will run in a container, and cannot interact with the network outside of the given SDK client. Variables will not persist between calls, so make sure to return or log any data you might need later. Remember that you are writing TypeScript code, so you need to be careful with your types. Always type dynamic key-value stores explicitly as Record<string, YourValueType> instead of {}.
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  • Lookup FDA device classification details by product code. Returns device name, device class (I/II/III), medical specialty, regulation number, review panel, submission type, and definition. Requires: product code (3-letter code from 510(k), PMA, or device product listings). Related: fda_product_code_lookup (cross-reference across 510(k) and PMA), fda_search_510k (clearances for this product code), fda_search_pma (PMA approvals for this product code).
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