"JSON Document Templates for Engineering Prompt Generation" matching MCP tools:

• get_prompt_libraryA

  Birthstar Vedic Astrology MCPA

  Fetch curated high-value prompt templates and multi-tool question workflows. Call this tool whenever you want to suggest high-value questions to the user, or when the user asks "what can you do?", "what should I ask?", or wants guided astrology workflows (e.g. Sade Sati analysis, timeline forecast, dasha transitions, chart strength, school comparisons, timing windows). Args: category: Optional category filter. One of 'all', 'Core Reading', 'Timing & Transits', 'Career & Wealth', 'Strengths & Accuracy', 'Relationships', 'Daily & Remedies'. Names are matched case-insensitively; an unrecognised one is an error listing the valid names, never a silent empty result. include_full_templates: Set to True to retrieve the full expanded prompt text. Defaults to False for compact workflow titles and tool chains. Returns a structured catalog of prompt templates with their titles, descriptions, required arguments, and which underlying tools they chain.

  ConnectorNo auth

• restyleA

  Nano Studio ProB

  Edits an existing image with AI. COSTS CREDITS (generate_* pricing, like generate_image). Always creates a NEW generation; the original is untouched. Modes: plain-text edit (just instructions); one-click product placement (action bring + reference_id — places the reference's product into the scene at true scale, relit); recolor/remove/add items (use get_detections for item names); merge two images (merge_bias 0=reference dominates, 100=base dominates); art-style transfer (action style + style_intensity 0-100). reference_id can be an asset OR a generation you own. @slug in the prompt or entity_ids attach a saved entity's sheet (list_entities).

  ConnectorAPI key

• create_documentA

  ProductNowA

  Create a NEW ProductNow document and generate its content with AI. Use this to persist knowledge the user wants remembered or shared across sessions and teammates — decisions, specs, RFCs, plans, meeting outcomes. Search with search first so you don't duplicate an existing document; to change an existing document use edit_document instead. File the document in an existing folder. Search results include each document's folderId; pass the folderId of the closest related document. If search does not show a fit, fetch_folder the root and walk folders until you have a folderId. Leave folderId off only when the user explicitly asked for the warehouse root. If no folder fits, call create_folder under the closest existing folder and pass that new folderId. Content is generated from `prompt` (the full outline: numbered sections, their titles, and what each should say) and `context` (verbatim source material — notes, code, or a file the user named; read the file first and pass its contents). Generation paraphrases: if exact wording must survive, put it in `context` and say so in `prompt`. Pass `templateId` only when you already have one. Returns the documentId and URL; generation continues after the call returns.

  ConnectorOAuth

• idealaunch_generate_hero_imageA

  Idea LaunchA

  Start generating hero image variants for an idea's ad. Runs in the background. Args: - ideaId (string) - prompt (string, optional): defaults to the idea's saved image prompt Returns: { job: { id, status, prompt, variantCount, results }, note }. Poll idealaunch_get_image_job until status is 'succeeded', then choose one with idealaunch_apply_hero_image. Consumes one of the idea's AI generation turns. Costs no Ad Run credit.

  ConnectorOAuth

• video_to_audioA

  Media Generator — Images, Videos & AudiosA

  Input: A muted video URL along with a textual prompt describing the desired audio. Output: We will return the video URL with the applied audio. Functionality: This tool now takes a muted video and a text prompt as input. It generates an audio track based on the provided prompt and applies this audio to the video, resulting in a video with integrated sound. Steps: 1. We will get the user_id from the request context. 2. We will validate the user's generation tokens. 3. We will call the Audio Application API with the muted video URL and the provided prompt. 4. The API will generate the audio from the prompt and merge it with the muted video, returning a JSON response with the updated video URL. 5. We will return the updated video URL to the user. INSTRUCTION FOR CLIENT MODEL: - Extract the required input parameters 'video_url' (type: string, URL) and 'prompt' (type: string, describing the desired audio) from the user's prompt. - Ignore any extraneous information in the user's input. - Pass the extracted values to this tool as 'video_url' and 'prompt'. - Example: For user input "Add dramatic orchestral music to this video https://example.com/video.mp4", extract 'video_url' as 'https://example.com/video.mp4' and 'prompt' as 'dramatic orchestral music'.

  ConnectorOAuth

• run_research_snapshotA

  TrakkrB

  Trigger a topic snapshot research run. Generates 50 focused prompts on the supplied topic and runs them through the full Trakkr research pipeline (Gemini prompt generation + GPT-4o ranking + competitor normalization). Consumes one of the brand's monthly snapshot credits (5/mo per active brand) — call get_research_credits first to confirm availability. The brand must be active (tracking on). Only use when the user explicitly asks to run new research on a topic. Full prompt research runs are intentionally NOT exposed through the MCP — those run daily on a schedule. The snapshot runs asynchronously (3-5 min). The response returns immediately with a placeholder_id; poll get_research_runs to retrieve the completed payload, or use get_latest_research with report_type='topic_snapshot'. Args: brand_id: The brand to run the snapshot for (required). topic: The topic to focus the snapshot on (2-200 chars, required). topic_context: Optional extra context to refine prompt generation (up to 500 chars).

  ConnectorOAuth

• axint.schema.compileA

  AxintA

  Compile a minimal JSON schema directly to Swift, bypassing the TypeScript DSL entirely. Supports intents, views, components, widgets, and full apps via the 'type' parameter. Uses ~20 input tokens vs hundreds for TypeScript — ideal for LLM agents optimizing token budgets. Use: use for token-light JSON-to-Swift generation; use compile for full TypeScript DSL control and scaffold for TS starters. Inputs: schema kind selects intent, view, widget, or app output; options add companion metadata. Effects: read-only Swift generation; writes no files and uses no network.

  ConnectorNo auth

• generate_cover_imageA

  Misar.Blog MCP ServerA

  Generate an image from a text prompt with AI, upload it to the Misar.Blog CDN, and return its public URL for use as cover_image_url when publishing. Use it when no artwork exists yet; use upload_image for a file the user already has. Each call generates a NEW image and costs generation credits against the account's plan — it is not idempotent, so re-running to 'try again' bills again. Generation takes noticeably longer than other tools. Requires an API key. The resulting URL is public and cannot be deleted through this server. Results vary between runs for the same prompt.

  ConnectorNo auth

• get_strategy_schemaA

  Backtest360A

  JSON Schema for the strategy document (condition_tree + indicators). Fetch this before composing a strategy by hand; the validate_strategy tool checks against the same rules.

  ConnectorNo auth

• get_extractA

  webbersites-x402-data-apiA

  Document extraction: fetch a PDF, DOCX, or CSV by URL and get clean Markdown plus structured JSON — PDF text by page with metadata (honestly flags scanned PDFs that would need OCR), DOCX converted to real Markdown, CSV parsed to typed columns + JSON rows + a Markdown table. For agents that need document contents, not bytes. ($0.02 per call, paid via x402)

  ConnectorNo auth

• list_character_posesA

  spriteshipA

  All poses on a character: { id, name, prompt, status pending|done|error, url, direction, pendingSince, errorCode, creditsRefunded }. This is the poll target after generate_pose — pose generation has no job id. It is also the source of ready pose ids for generate_character_animation poseFirstFrameIds / poseLastFrameIds. A pose still pending long after pendingSince is stalled, not working.

  ConnectorNo auth

• generate_cover_imageA

  Misar.Blog MCP ServerA

  Generate an image from a text prompt with AI, upload it to the Misar.Blog CDN, and return its public URL for use as cover_image_url when publishing. Use it when no artwork exists yet; use upload_image for a file the user already has. Each call generates a NEW image and costs generation credits against the account's plan — it is not idempotent, so re-running to 'try again' bills again. Generation takes noticeably longer than other tools. Requires an API key. The resulting URL is public and cannot be deleted through this server. Results vary between runs for the same prompt.

  ConnectorNo auth

• generate_videoA

  CCAPIA

  Generate a video from a text prompt, optionally driven by reference images (image-to-video, first/last frame). This is asynchronous: it returns a task_id you poll with get_task(platform='video'). Generation usually takes 30-180 seconds. Pass wait_seconds to have the server poll for you. Video generation is the most expensive capability here — confirm the prompt with the user before spending on retries.

  ConnectorNo auth

• generate_textA

  HermosoA

  Text generation against the writing-model catalog (Claude, Gemini, GPT, Llama, DeepSeek…) — ad copy, hooks, scripts, rewrites, brainstorms. Prompt-only, no ad assembly (for a finished on-brand creative use plan_ad -> render_ad). BY DEFAULT the model answers as a marketing copywriter (a short house system prompt is applied, which is what you want for ad copy); pass raw:true for a plain, unstyled answer from the model itself with NO system prompt at all. model = a writing-model id from hermoso_capabilities (omit for the default Claude orchestrator). Paid (a credit or two by length).

  ConnectorOAuth

• list_templatesA

  makespdfA

  List templates visible to the caller — both curated library templates (no owner) and the caller's own saved templates. Metadata only (no DSL bodies). Paginated. Use `owner: 'mine'` to restrict to your own; `owner: 'library'` for the curated catalog only; default `'all'` returns both. Optional `category`, `tags` (comma-separated, ANDed), and `q` (free-text over name + description) filters apply across both. Mirrors GET /api/v1/templates.

  ConnectorNo auth

• generate_promptA

  PrompeteerA

  Generate an AI prompt for a selected platform using external AI processors and automatically save it to your private PromptDrive. Consumes generation quota and may queue private tile or quality enrichment. Optional Memory grounding uses only your authorized private sources. It does not execute the generated prompt or browse supplied URLs. Calling hosts should pass their selected platformId and exact modelId when available; omission remains supported.

  ConnectorOAuth

• generate_promptA

  PrompeteerA

  Generate an AI prompt for a selected platform using external AI processors and automatically save it to your private PromptDrive. Consumes generation quota and may queue private tile or quality enrichment. Optional Memory grounding uses only your authorized private sources. It does not execute the generated prompt or browse supplied URLs. Calling hosts should pass their selected platformId and exact modelId when available; omission remains supported.

  ConnectorOAuth

• execute_actionA

  maginary-mcpA

  Run a follow-up action on a completed generation's image. After ``generate`` → ``wait_for_generation``, the response's ``processing_result.available_actions`` lists what's possible per slot. Call this tool with one of those action types. Args: generation_uuid: UUID of the parent generation (from ``generate``). action_type: One of the values from ``available_actions`` — e.g. ``"upscale_2x"``, ``"upscale_1_5x"``, ``"vary_strong"``, ``"vary_subtle"``, ``"pan_left"``, ``"pan_right"``, ``"pan_up"``, ``"pan_down"``, ``"zoom_out_2x"``, ``"zoom_out_1_5x"``, ``"img2vid_basic"``, ``"reroll"``. parent_image_index: The slot index of the image to act on (0, 1, 2, or 3 for a 4-image grid). Required for per-slot actions; omit for ``"reroll"`` (global action). prompt: Optional replacement prompt. For ``vary_*`` you can steer the variation with a new prompt; for ``img2vid_basic`` you can describe the desired motion. callback_url: Optional webhook URL (same as ``generate``). Returns: The newly created child generation record (same shape as ``generate``'s return — poll it with ``wait_for_generation``). On failure, same ``isError`` contract as ``generate``: ``"auth"``, ``"payment_required"`` (with x402 challenge), or ``"failed"``.

  ConnectorNo auth

• list_templatesA

  Povver — Strength TrainingA

  List all workout templates (names + IDs only, no exercise details). Use when the user asks "what templates do I have?" or you need template IDs for creating/updating a routine. For full exercise prescriptions, use get_template with a specific ID.

  ConnectorOAuth

• compendium-document-getA

  Foundry MCPA

  Get a compendium document as a readable summary: actors come back as a statblock (HP, AC, abilities, attacks), other types as core fields plus system data. For the complete raw JSON use compendium-document-get-raw, or uuid-resolve when you already hold the document UUID (what the filter tools return). Use compendium-browse to find document IDs.

  ConnectorAPI key

• asset_generate_3dA

  SceneplaneA

  Start a text/image → 3D generation of ONE freestanding object per task — build ground planes and assemblies from scene operations, and reuse a generated model by duplicating it with scene ops. provider=hyper3d (Rodin) takes prompt only; provider=hunyuan3d and provider=tripo3d take prompt OR imageUrl. Each provider needs its integrations_set token. Poll asset_generate_status, then import with asset_library_import source=<provider> slug=<taskUuid>.

  ConnectorOAuth