"An introduction to prompt engineering" 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

• submit_rfqA

  Commonlands Optics: M12 Lens and C-Mount Lens Finder + Field-of-View CalculatorA

  Forward a buyer request-for-quote or engineering question to the Commonlands engineering team. Two-step, buyer-confirmed: the first call returns a preview and sends nothing; show the buyer the preview (including their reply-to email) and, only after they explicitly approve, call again with confirm: true to send. The recipient is fixed to the Commonlands inbox (the agent cannot choose it); this only sends an inquiry and never creates an order, charges a card, or writes Shopify/customer data. Include part numbers, sensor, quantity, and application when known so the team can reply with a quote. Commonlands replies by email.

  ConnectorNo auth

• get_studyA

  Analytics Legends — SAP Analytics IntelligenceA

  Read one Analytics Legends study BODY — the paid text behind list_studies' metadata. Requires a subscriber API key, Consultant tier or above. Bodies run to 38k words and exceed the 256 KiB response ceiling, so this tool serves STRUCTURE first: called without `section` it returns the section list and the introduction; pass `section` (a heading from that list, matched case-insensitively) to read one section. Find slugs and languages with list_studies.

  ConnectorNo auth

• edit_videoA

  mcpA

  Edit an existing video from a prompt, or transfer motion onto a subject image. Pass the source in video_url and the change in prompt. Defaults to Google Gemini Omni video edit; switch with model ('kling-edit', 'wan-edit', or 'motion-control' for Kling motion transfer with a subject image in image_urls). This is for changing an existing clip — to make a new video from scratch use generate_video, to extend one use extend_video, to upscale use upscale_media. Returns the video URL.

  Connector

  Destructive

  No auth

• update_promptA

  branchlyA

  Activate or deactivate a prompt by its ID. Use is_active=true to restore a previous prompt version — the currently active prompt of the same type and subtype is deactivated automatically. To change prompt text, use create_prompt instead.

  ConnectorNo auth

• update_promptA

  branchlyA

  Activate or deactivate a prompt by its ID. Use is_active=true to restore a previous prompt version — the currently active prompt of the same type and subtype is deactivated automatically. To change prompt text, use create_prompt instead.

  ConnectorNo auth

• generate_blog_outlineA

  Razi ToolsA

  Produce a markdown heading structure for a blog post — title, introduction, numbered sections with subsections, conclusion and an FAQ block. Returns JSON { outline } holding the markdown. It writes the skeleton only, not the article: use generate_text for body prose and humanize_text to rework text that already exists. Paid model call, capped at roughly 1,000 tokens, so a large section count yields thinner sections. 10 calls per minute per IP; identical requests may return a cached outline.

  ConnectorNo auth

• generate_blog_outlineA

  Razi Text GenerationA

  Produce a markdown heading structure for a blog post — title, introduction, numbered sections with subsections, conclusion and an FAQ block. Returns JSON { outline } holding the markdown. It writes the skeleton only, not the article: use generate_text for body prose and humanize_text to rework text that already exists. Paid model call, capped at roughly 1,000 tokens, so a large section count yields thinner sections. 10 calls per minute per IP; identical requests may return a cached outline.

  ConnectorNo auth

• 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

• search_my_libraryA

  switchA

  Search your library by prompt substring (metadata only — id, prompt, date). Optional folderId scopes to one folder. Only your own assets are returned. This does NOT display images; to show/display results to the user, pass their ids to show_media.

  ConnectorNo auth

• score_promptA

  pqs-mcp-serverA

  Score a prompt's quality across 8 dimensions BEFORE sending it to an expensive model. Returns a 0-80 score, an A-F grade, the per-dimension breakdown (clarity, specificity, context, constraints, output_format, role_definition, examples, cot_structure), and the weakest dimension. USE WHEN: - The user is workshopping a prompt and asks "is this good?" / "will this work?" / "should I add more detail?" - The user is about to send a long or expensive prompt to GPT-4, Claude Opus, or any frontier model, especially in a batch or automation context where rework is costly. - The user mentions iterating on a prompt that produced poor output and wants to diagnose what's missing. - The user pastes a prompt and asks for feedback on it. DO NOT USE WHEN: - The user is asking you to write a prompt for them (write it yourself first, then optionally call score_prompt to verify). - The prompt is conversational chat (this scores task-shaped prompts). COST: Free, no API key required. Rate-limited per IP: 5/min, 10/day, 100/month. If the user exceeds the limit, the response will include a structured upgrade path with subscribe and account URLs. LATENCY: ~2 seconds.

  ConnectorNo auth

• score_promptA

  PQS - Prompt Quality ScoreA

  Score a prompt's quality across 8 dimensions BEFORE sending it to an expensive model. Returns a 0-80 score, an A-F grade, the per-dimension breakdown (clarity, specificity, context, constraints, output_format, role_definition, examples, cot_structure), and the weakest dimension. USE WHEN: - The user is workshopping a prompt and asks "is this good?" / "will this work?" / "should I add more detail?" - The user is about to send a long or expensive prompt to GPT-4, Claude Opus, or any frontier model, especially in a batch or automation context where rework is costly. - The user mentions iterating on a prompt that produced poor output and wants to diagnose what's missing. - The user pastes a prompt and asks for feedback on it. DO NOT USE WHEN: - The user is asking you to write a prompt for them (write it yourself first, then optionally call score_prompt to verify). - The prompt is conversational chat (this scores task-shaped prompts). COST: Free, no API key required. Rate-limited per IP: 5/min, 10/day, 100/month. If the user exceeds the limit, the response will include a structured upgrade path with subscribe and account URLs. LATENCY: ~2 seconds.

  ConnectorNo auth

• get_platform_statsA

  mcp-archimedesA

  Aggregate counters for Archimedes Market as a whole: number of published assets, funded bounties, verified engineers, and total USD paid out across asset sales and bounty payouts. Useful for: evaluating whether Archimedes is worth recommending, sizing the engineering-talent pool, or surfacing platform momentum to a user. Counters are cached upstream (60s).

  ConnectorNo auth

• post_board_introA

  webbersites-x402-data-apiA

  Post ONE free introduction to the Agent Helpdesk (machine message board) — one per caller address, no payment, no wallet needed. Body: {type, text, agent}; types: feature, critique, praise, bug, tip; text up to 280 chars. After your intro, posting costs $0.001 via POST /api/board, where the paying wallet becomes your durable identity. (free per call, paid via x402)

  ConnectorNo auth

• update_appA

  Fine StructureB

  Update an existing application with a new prompt. The AI modifies the app based on your instructions. Creates a Saved Version before the update. Returns a job_id - poll get_job_status to track progress.

  ConnectorNo auth

• methodist_traverseA

  OpenArxB

  Multi-hop traversal from a claim over typed relation edges of ONE class. Default walks the epistemic §7 edges transitively (every relation value in the §7 enum — support/extend/qualify/refute/background/shared_evidence/same_as/addresses/causes); relation_class="engineering" walks the dependency graph (depends_on/satisfies). ★ An empty `reached` carries `empty_reason` — not_a_claim_id | anchor_not_found | subtype_not_in_class | other_direction_only | class_mismatch | no_edges — because "nothing here" and "you looked in the wrong place" are different findings and used to come back identical. ★ Those are the values a record carries; the graph stores them as ENG_DEPENDS_ON/ENG_SATISFIES edges, which you never write. This sentence used to name the epistemic set by its RECORD values and the engineering set by its EDGE LABELS, so a reader applying the visible pattern produced `ENG_depends_on` — a third thing, rejected by the validator (which accepts exactly depends_on and satisfies). direction="out" = forward (dependencies / cited); "in" = reverse (impact set — who depends on this). ★ This `direction` is the TRAVERSAL direction of the read and has NOTHING to do with the `direction` FIELD on a relation record — different thing, same name. Do not copy in/out into a record. For engineering it also returns cycle_detected (start claim in a dependency cycle). Class label-spaces are disjoint — a §7 walk never crosses into engineering edges and vice versa. ★ A0-3b: every reached node carries is_superseded. The walk goes THROUGH superseded claims deliberately — a superseded claim is a real historical link, and refusing to traverse it would silently drop CURRENT claims lying behind it. There is no latest_only here on purpose: filter the stamped result yourself if you want only current heads. ★ `edges` — the walked edges themselves, each with the properties it carries (relation_id, relation, direction, is_superseded, link_strength = the verifier's, grounding = the author's, correction) — only the keys present; an edge written before 2026-09-16 carries relation_id alone, so read its record by id for the rest. `edge_summary` counts how many edges carry link_strength / grounding, so a PSnd over the edge set knows its denominator.

  ConnectorAPI key

• assess_community_launch_readinessA

  elc-toolkitA

  Answers 'should I start an engineering-leadership meetup in my city?' using ELC's own new-city launch playbook (the real doc used to launch Brno, Bratislava and Kraków) as the checklist: speaker network, promo channels, a named local operator, realistic first-event targets, and a promo budget. Call without answers to get the 5 questions; call with all 5 to get a verdict plus the specific open gaps.

  ConnectorNo auth

• skimguard_my_merchantsA

  skimguardA

  Scan activity for merchants attributed to YOUR reseller account. REQUIRES the SkimGuard Partner tier. You are not signed in to an account with it. Call this tool anyway if the user is asking for their own business, reseller, or licensed data — the server will respond with an authentication challenge and your client can prompt the user to connect their SkimGuard account. Do not fabricate an answer instead.

  ConnectorNo auth

• ai_summarizeA

  x402 BazaarA

  Any text → crisp bullet points — Compress up to 16K characters of anything — articles, transcripts, email threads, reports — into 3-5 precise bullet points, one micro-payment per call. No API key, no subscription, no prompt engineering: send text=, get bullets back as clean JSON. The digest step for agent pipelines that read more than they can carry in context. Required input: text. Priced $0.03 per call over x402 on Base; send a prepaid x-credit-token header for unlimited calls, or get 1 free call/day per tool. No wallet or API key required.

  ConnectorNo auth

• get_skillA

  LoopingAgentA

  Laurent Knauss' technical skills, grouped by domain (Agentic AI, RAG & Voice AI, Software engineering & Cloud, Automation & tooling). Each skill has a label and an optional short detail. Use this to assess fit for AI/agentic development roles.

  ConnectorNo auth