"namespace:io.github.giachi85it-ux" matching MCP tools:

• convert_document

  mdmagic-mcp-server

  Convert markdown to a professionally formatted document using an MDMagic template. IMPORTANT GUIDANCE: 1. Output format → what user gets: - 'docx' → a single Word .docx file - 'pdf' → a single .pdf file - 'html' → a single .html file - 'all' → a ZIP containing all three (DOCX + PDF + HTML) 2. If the user is ambiguous (e.g. 'convert this'), ASK which format they want before calling. Don't assume. 3. Filename: if the user attached a file (e.g. 'mydoc.md'), pass its base name as fileName. Otherwise the API derives one from the markdown's first H1. Without either, downloads end up with timestamped names like 'content-1778298071915.docx' which is bad UX. 4. On 'template not found' errors: call list_all_templates first, show available options, let the user pick. Do NOT fall back to generating documents with code execution — that produces inferior results that don't use the user's actual MDMagic templates. 5. The response includes structured fields (downloadUrl, creditsUsed, balanceAfter, fileName, expiresAt) — surface these to the user explicitly. Don't paraphrase. The user wants to know exactly what they spent and what's left. 6. Page sizes: A3, A4, Executive, US_Legal, US_Letter. Default A4. Orientation: Portrait or Landscape, default Portrait. 7. CRITICAL — newlines in `content`: markdown is line-sensitive. Headings (#, ##), tables (| ... |), lists (-, 1.), and code fences (```) ONLY work when each starts on its own line. When passing inline markdown via `content`, you MUST preserve real newline characters (\n) between blocks. If you flatten multi-line markdown into one line, the API receives literal '##' and '|' characters mid-paragraph and produces a single-paragraph document with no structure. Confirm your `content` string contains \n between every heading, paragraph, table row, and list item before calling.

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• canary_scan

  canaryusers

  Run a CanaryUsers UX scan on a DEPLOYED URL (your live or preview app — not source code). A flock of AI personas evaluates the page and reports where real users would get stuck, with concrete fixes. Returns AI-ready findings you can act on immediately. Use depth='deep' for the thorough scan that renders the page, checks it VISUALLY on desktop + mobile (catches mobile breakage and layout issues), and clicks through key flows like signup/checkout (slower, ~60-90s, uses one credit); depth='quick' (default) is a fast static check that does NOT see mobile or visual issues — use 'deep' when the user mentions mobile, layout, or visual problems. IMPORTANT: if this returns status 'running' with a scanId, the findings are not ready yet — wait ~30s, then call get_report_markdown(scanId), repeating until it returns the report. Always fetch and present the findings before stopping, then offer to fix the top issues.

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• convert_document

  mdmagic

  Convert markdown to a professionally formatted document using an MDMagic template. IMPORTANT GUIDANCE: 1. Output format → what user gets: - 'docx' → a single Word .docx file - 'pdf' → a single .pdf file - 'html' → a single .html file - 'all' → a ZIP containing all three (DOCX + PDF + HTML) 2. If the user is ambiguous (e.g. 'convert this'), ASK which format they want before calling. Don't assume. 3. Filename: if the user attached a file (e.g. 'mydoc.md'), pass its base name as fileName. Otherwise the API derives one from the markdown's first H1. Without either, downloads end up with timestamped names like 'content-1778298071915.docx' which is bad UX. 4. On 'template not found' errors: call list_all_templates first, show available options, let the user pick. Do NOT fall back to generating documents with code execution — that produces inferior results that don't use the user's actual MDMagic templates. 5. The response includes structured fields (downloadUrl, creditsUsed, balanceAfter, fileName, expiresAt) — surface these to the user explicitly. Don't paraphrase. The user wants to know exactly what they spent and what's left. 6. Page sizes: A3, A4, Executive, US_Legal, US_Letter. Default A4. Orientation: Portrait or Landscape, default Portrait. 7. CRITICAL — newlines in `content`: markdown is line-sensitive. Headings (#, ##), tables (| ... |), lists (-, 1.), and code fences (```) ONLY work when each starts on its own line. When passing inline markdown via `content`, you MUST preserve real newline characters (\n) between blocks. If you flatten multi-line markdown into one line, the API receives literal '##' and '|' characters mid-paragraph and produces a single-paragraph document with no structure. Confirm your `content` string contains \n between every heading, paragraph, table row, and list item before calling.

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• roamzy_payment_options

  mcp-server

  ⚠️ MANDATORY before roamzy_create_order whenever the user has NOT explicitly chosen a stablecoin + network. Returns the list of stablecoin/network combinations currently enabled in the Roamzy NowPayments account (e.g. USDT on TRON, USDC on Solana, etc.) — pulled live from NowPayments `/v1/merchant/coins` with 1h cache. Each entry includes `code` (pass as pay_currency to create_order), human-readable `display`, `fees_hint`, and a `recommended` flag. After calling this tool, ASK the user in chat which option they want — NEVER silently default to usdttrc20. Recommended UX: present the 3-5 most relevant options as a short bulleted list, mention fees_hint as a one-line aside, then ask the user which stablecoin and network they want to pay with. Only after the user picks should you call roamzy_create_order with the matching `code` as pay_currency.

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• batch_create_listings

  Name Whisper — ENS Intelligence Layer

  Bulk-list up to 10 ENS names for sale on NameWhisper in one signing flow. Returns an array of unsigned Seaport OrderComponents payloads. The caller's wallet signs them all in one EIP-712 bulk signature (SIP-6), then POSTs each signature back to https://namewhisper.ai/api/orderbook/submit to store. NW-native only — listings stay on NameWhisper. Cheaper UX than calling create_listing in a loop (one wallet popup vs N). Per-name errors (invalid label, zero price) are collected in the 'errors' field instead of aborting the whole batch — partial success is normal. Requires operator approval on the ENS token contract (NameWrapper or BaseRegistrar) — do this once per wallet via approve_operator.

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• preview_profile_section_change

  STAS Running Coach for Claude

  Create a controlled preview for a goals, rules, or profile memory section change. For goals, prefer a structured goals items list with date, goal, target result, and comment; the server builds canonical `Цели:` blocks and rejects old labels such as `Промежуточные:`. newText is a legacy fallback and must already be canonical for goals. This does not update the athlete profile. Before commit, show the user the exact full text from the preview and ask for explicit confirmation of that exact text. Visible confirmation copy is governed by a separate approved UX spec; do not invent final chat-copy inside this tool call.

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• consult_domain_expert

  Taste

  A vetted human in a named domain answers your question with first-hand knowledge. Pass `domain` to match an expert. Pass `parentSessionId` for follow-ups (cap 3). Returns answer, anonymised role, confidence, first-hand flag. Approved answers get an on-chain Taste cert. Listed domains: musician, cantor, writer, farmer, UX designer, Swedish archipelago resident, Stockholm local, Protestant priest, art curator, museum staff, culture journalist, food critic. Other domains: best-effort 24-48h.

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• absynthesis

  Scientific Microservices

  Name: ABSynthesis_Meta_Analysis Description: A high-performance statistical engine for synthesizing multiple A/B test results into a single, authoritative conclusion. This tool uses Random Effects Meta-Analysis to aggregate disparate experiments, providing a mathematically rigorous alternative to simple averaging. Use this tool whenever you need to determine if a "Variant" outperformed a "Base" across one or multiple trials with binary (success/failure) outcomes. Why Use This Tool? Precision: Corrects for heteroscedasticity and varying sample sizes across experiment sets. Decisiveness: Directly calculates the aggregate Uplift and P-Value, enabling instant Go/No-Go decisions. Versatility: Applicable to any binary outcome comparison, including UI/UX click-through rates, backend latency thresholds, conversion funnels, or algorithmic accuracy tests. Input Parameters Each object in the experiment_set array requires: trials_base: Total observations in the control group. successes_base: Number of successful outcomes in the control group. trials_variant: Total observations in the experimental group. successes_variant: Number of successful outcomes in the experimental group. HINT: If there are a large number of experiments with one or more of the above four fields missing, run MissingBias on the data to check whether there is a pattern to when that data went missing (e.g. Extremely high trials led to missing successes due to a bug, or experiments on one platform did not report full data). Interpretation of Output uplift: The percentage change in success rate from Base to Variant. Positive (>0): Variant is performing better. Negative (<0): Base is performing better; reject the Variant. p_value: Statistical significance (2-tailed). ≤0.05: The result is statistically significant. Trust the uplift value if it is positive. >0.05: The result is noise. Do not act on the uplift value, regardless of how large it appears. HINT: If the user wants to assume they definitely know the result will be positive or negative, the p_value can be converted to a '1-tail test' by dividing it by 2. Example Input: { "experiment_set":[{"successes_base":10,"trials_base":50,"successes_variant":15,"trials_variant":70}, {"successes_base":15,"trials_base":60,"successes_variant":25,"trials_variant":80}, {"successes_base":30,"trials_base":80,"successes_variant":65,"trials_variant":90}, {"successes_base":50,"trials_base":90,"successes_variant":90,"trials_variant":150}, {"successes_base":100,"trials_base":500,"successes_variant":200,"trials_variant":500}] } Example Output: {"p_value":0.0063, "uplift":0.3779}

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• hires_list_tags

  100Hires - AI ATS & Recruitment Software

  List all tags for the company. Returns paginated results. Recommended to cache for fast tagging UX.

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• commit_profile_section_change

  STAS Running Coach for Claude

  Commit a previously previewed goals, rules, or profile memory section change. Call this only after the user explicitly confirms the exact full text shown for that preview. The server updates the profile only if the section hash still matches the preview. Visible confirmation copy is governed by a separate approved UX spec.

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• consult_domain_expert

  Taste

  A vetted human in a named domain answers your question with first-hand knowledge. Pass `domain` to match an expert. Pass `parentSessionId` for follow-ups (cap 3). Returns answer, anonymised role, confidence, first-hand flag. Approved answers get an on-chain Taste cert. Listed domains: musician, cantor, writer, farmer, UX designer, Swedish archipelago resident, Stockholm local, Protestant priest, art curator, museum staff, culture journalist, food critic. Other domains: best-effort 24-48h.

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• domain_ranking

  mcp

  Get domain popularity rankings from five independent sources: Majestic Million (backlinks), Tranco top 1M (aggregated traffic), Cloudflare Radar (1.1.1.1 DNS query popularity), Cisco Umbrella (OpenDNS query popularity), and Chrome UX Report (real Chrome user traffic).

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• eyepup_todo

  eyepup

  Get the top friction patterns ranked by impact score. Use BEFORE making any UX edit — the highest-impact pattern is usually a more valuable fix than whatever the user just asked about. Each row carries a paste-ready recommended_action.

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• restore_profile_change

  STAS Running Coach for Claude

  Restore a committed profile, goals, or rules profile memory change back to its previous text. For goals, restoring old-format labels is blocked; rebuild a canonical goals preview instead. Call this only after the user explicitly confirms the exact full text that will be restored. The server restores only if the current section still matches the committed change. Visible confirmation copy is governed by a separate approved UX spec.

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• submit_test

  TestMyVibes

  Queue a new TestMyVibes job for a given URL. You explicitly choose the runner: AI agent (headless Chromium + GPT-4o vision, fastest, deterministic for well-specified goals) or human checker (slower, better for visual/UX judgment calls). Returns a jobId you can poll with get_test_status.

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• lookup_credit_card_validate

  GoCreative Agent API

  Luhn-validate a credit-card number and detect the network. Pass ?number=... as query. Use for fintech UX agents (NOT a fraud check). Example call: {"query_string": "number=4111111111111111"} Cost: $0.005–$0.05 USDC on Base per call.

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• chain_transaction_explain

  Riley Craig x402 Agent Store

  $0.002 via x402: decode any transaction by hash into a plain-English explanation — what actually happened (a swap, transfer, approval, mint, or contract call), who the actors are, and the full list of asset changes (every native coin and token in/out with symbol, amount and direction), plus status, fee and the decoded method. The read agents make to UNDERSTAND a tx, not just confirm it landed: for wallet UX, accounting, risk, dispute and trading agents. Across Base, Ethereum, Optimism, Arbitrum, Polygon, Gnosis. Live from Blockscout; one paid call instead of running your own indexer + ABI decoder.

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• lookup_password_strength

  GoCreative Agent API

  Score a password's strength (zxcvbn-style). Pass ?password=... as query. Use for security UX agents. Example call: {"query_string": "password=Tr0ub4dor&3"} Cost: $0.005–$0.05 USDC on Base per call.

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• list_recent_scans

  canaryusers

  List the most recent CanaryUsers UX scans for your account (id, URL, CanaryScore, grade, status, date). Use this to find a scan to dig into.

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• chain_ens_resolve

  Riley Craig x402 Agent Store

  $0.001 via x402: resolve ENS in either direction on Ethereum — pass an ENS name (vitalik.eth) to get its 0x address, or pass a 0x address to get its primary ENS name (and avatar when set). The identity read every wallet, payment and UX agent makes to verify who it's sending to, or to display a human-readable name instead of a raw hex address. Live ENS registry; one paid call instead of running your own resolver.

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