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466,673 tools. Updated 2026-08-19 20:19

"Methods to Improve or Enhance a Prompt" matching MCP tools:

  • Send structured feedback to the Kifly team. **Call after a confusing response, a dead-end, or a successful workaround you had to invent** — it's how we improve the agent surface. Fire-and-forget: returns 202 immediately, no blocking, safe to skip if it would add latency to a user-facing flow. `category` and `severity` are required enums (don't free-form them). Include `context` with what you were doing (tool called, query used, response shape, what you expected). Add `suggested_fix` only if you have a concrete idea. Rate-limited to 10/min per agent token; everything is reviewed before influencing anything.
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  • Rewrite a prompt to score higher on the PQS rubric, AND show before/after output comparisons so the user can see the impact. Returns the optimized prompt, the original PQS score, the optimized PQS score, and side-by-side sample outputs from a frontier model using both versions. USE WHEN: - The user got a low score from score_prompt and asks how to improve. - The user explicitly asks to "improve" / "rewrite" / "fix" / "optimize" a prompt they pasted. - The user is dissatisfied with output quality from a previous prompt and asks how to get better results. - score_prompt returned a suggestion to invoke this tool. DO NOT USE WHEN: - The user just asked for a score (use score_prompt only — don't double up). - The user wants you to write a new prompt from scratch (write it directly). REQUIRES: A PQS API key from a Pro subscription ($19.99/month, 1,000 calls/mo, includes batch + A/B comparison). If the user has not provided one, the tool returns a clear subscription URL — pass that response to the user verbatim. Do not invent or guess API keys. There is no free trial of this tool; the user must subscribe before the first call. COST: Counted against your Pro subscription's monthly call quota. LATENCY: ~6-8 seconds.
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  • Rewrite a prompt to score higher on the PQS rubric, AND show before/after output comparisons so the user can see the impact. Returns the optimized prompt, the original PQS score, the optimized PQS score, and side-by-side sample outputs from a frontier model using both versions. USE WHEN: - The user got a low score from score_prompt and asks how to improve. - The user explicitly asks to "improve" / "rewrite" / "fix" / "optimize" a prompt they pasted. - The user is dissatisfied with output quality from a previous prompt and asks how to get better results. - score_prompt returned a suggestion to invoke this tool. DO NOT USE WHEN: - The user just asked for a score (use score_prompt only — don't double up). - The user wants you to write a new prompt from scratch (write it directly). REQUIRES: A PQS API key from a Pro subscription ($19.99/month, 1,000 calls/mo, includes batch + A/B comparison). If the user has not provided one, the tool returns a clear subscription URL — pass that response to the user verbatim. Do not invent or guess API keys. There is no free trial of this tool; the user must subscribe before the first call. COST: Counted against your Pro subscription's monthly call quota. LATENCY: ~6-8 seconds.
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  • READ-ONLY: returns generated source code as text and writes nothing to disk, creates no project and runs no command. Generates an idiomatic @imqueue/rpc service (an IMQService subclass with @expose()d, JSDoc-typed methods) plus a bootstrap that starts it. Provide the methods you want, or omit them for a starter template. Any non-primitive parameter or return type also gets a types.ts with the required @classType()/@property() declarations — without those the generated client types it `any`, which compiles. Use create_service (local install only) if you want files actually written.
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  • 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.
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  • Get a Stripe billing portal URL for managing payment methods and invoices. Returns a URL (not a redirect) that the human can open in a browser. Requires: API key with read scope. Args: flow: Optional. Set to "payment_method_update" to go directly to the payment method update page. Returns: {"url": "https://billing.stripe.com/p/session/..."}
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Matching MCP Servers

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    Provides MCP tool adapters for Bioconductor methods like limma, DESeq2, and fgsea, enabling statistical analysis of omics data through containerized R execution. It serves as a bridge between MCP clients and bioinformatics tools for reproducible research workflows.
    Apache 2.0

Matching MCP Connectors

  • Send structured feedback to the Kifly team. **Call after a confusing response, a dead-end, or a successful workaround you had to invent** — it's how we improve the agent surface. Fire-and-forget: returns 202 immediately, no blocking, safe to skip if it would add latency to a user-facing flow. `category` and `severity` are required enums (don't free-form them). Include `context` with what you were doing (tool called, query used, response shape, what you expected). Add `suggested_fix` only if you have a concrete idea. Rate-limited to 10/min per agent token; everything is reviewed before influencing anything.
    Connector
  • Upscales and enhances an image — sharpens edges, denoises, and raises resolution by an optional scale factor. Auto-picks the newest enabled Picsart upscale / enhance model unless overridden via the `model` param. Use this when the user asks to "upscale", "enhance", "make it higher resolution", "sharpen", "clean up this photo", or "make this 4k". Do NOT use this to remove the background (use `picsart_remove_bg`), replace the background (use `picsart_change_bg`), convert raster to SVG (use `picsart_vectorize`), or generate a new image (use `picsart_generate`). Required input: `image` — a publicly-accessible URL, not a local file path. Optional: `model` to pin a specific enhance model, `scaleFactor` (e.g. 2 or 4) for upscale ratio. Example: `{ image: "https://example.com/photo.jpg", scaleFactor: 4 }`. Returns `{ assets, id, model, created_at, summary, why_relevant, url, results: [{ url, metadata? }], drive? }` as a single JSON text block plus matching structuredContent (no `resource_link` blocks — the widget is the single source of visual truth, so result URLs are not duplicated as separate content blocks). `id` is the SDK's generation handle; `metadata` may include model-specific tags. Spends credits. Requires Authorization: Bearer <picsart_token>.
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  • Generate an AI image or canvas-code-based animation directly into a clip. - kind="image": text-to-image. Pass `prompt`. Optional: `style_id` (from find type='image_gen_style_packs'), `reference_image_url` or `mcp_upload_id` for image-to-image grounding. - kind="animation": canvas-code animation rendered from a prompt. Pass `prompt`. Optional: `voiceover_text` (drives timing), `base_component_id` (reuse a saved animation as the starting point), `reference_image_url` or `mcp_upload_id` for visual grounding. Generation is asynchronous: the element is created immediately with a stable `element_id` and rendered in the background. Poll `get_clip` (the phantom flag drops once rendering completes). Tip: use this tool whenever the user asks for a "generated", "AI", or "create me a" visual. For uploaded photos / logos / icons / GIFs, use `add_elements` with `element_type='image'` and a `src` or `mcp_upload_id` instead.
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  • 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'.
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  • How to swap $BOBAI on-chain: PancakeSwap V2 router, pair, swap paths, and the critical fee-on-transfer parameters (3% tax, min 15% slippage, SupportingFeeOnTransferTokens methods). $BOBAI reverts on a naive swap — use these.
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  • 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.
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  • 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.
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  • Create billable async Cannon Studio generation work only after explicit user approval. Requires OAuth or a developer API key; can spend credits up to max_credits and cannot be cancelled through MCP after submission. Use estimate_generation_cost first, then set confirmed=true and a user-approved max_credits cap. This tool does not create API keys, charge payment methods directly, or delete assets.
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  • Fetch the FULL TEXT of a biomedical paper from PubMed Central (the open-access subset) by PubMed ID. PREFER OVER get_abstract when you need methods/results/discussion, not just the abstract — "read the full paper", "what methods did <PMID> use", "extract details from the paper". Resolves the PMID to its PMC id and returns the article body text (capped ~40k chars). Only open-access articles are in PMC — returns has_full_text:false (use get_abstract) otherwise.
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  • Recent records from a common Detroit open dataset (data.detroitmi.gov / ArcGIS) by friendly name — no service ids needed. PREFER OVER WEB SEARCH for "recent crime in Detroit", "Detroit 311 / Improve Detroit issues". Names: crime, 311. Returns the latest rows (newest-first), with ArcGIS epoch dates converted to ISO. Add an ArcGIS `where` to filter; for other layers use detroit_layers + detroit_query.
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  • The full service catalog (Washington State notary & apostille) with prices and the accepted payment options. Optional — the server instructions already summarize the flow; call this when the customer asks about services or payment methods.
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  • List the saved payment methods (used only to pay for flight bookings, not for cards or adding funds). Returns each method with its id, brand, last 4 digits, and expiry, and marks the default one. Use setup_payment_method to add a new one. The gated tools set_default_payment_method and remove_payment_method also exist; call them by name even though they aren't in the tools list.
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  • WRITE — submits fraud feedback to MaxMind. Report the real-world outcome of a transaction (e.g. it was a chargeback or confirmed fraud) so minFraud can improve future scoring. This mutates MaxMind's model of your traffic; it is not a read. Requires a `tag` and at least one identifier (ip_address, maxmind_id, minfraud_id, or transaction_id). API: POST /minfraud/v2.0/transactions/report (returns HTTP 204).
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