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458,314 tools. Updated 2026-08-16 06:47

"QA review sentinel test marker" matching MCP tools:

  • Send a document for e-signature. Accepts PDF as base64, recipients, and field placement. Sandbox keys (sk_test_) send immediately (watermarked test mail). LIVE keys create a DRAFT and return it for human review unless confirm: true — nothing is emailed until confirmed. Field placement: page+x+y (percent, top-left origin) OR an anchor string, not both. After everyone signs, retrieve the sealed PDF with swiftsign_download_signed_pdf.
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  • Measured ground motion (sinking/uplift) for a US address or lat/lon, in mm/year AND in/year, from NASA OPERA Sentinel-1 InSAR. Costs $0.40 from your SibFly credits; out-of-coverage / too-stale / too-low-confidence / fuzzy-geocode return FREE. Use the gate params to avoid paying for data you'd reject. dry_run=true is a FREE coverage+cost preview.
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  • Inbound: record an operator's request to be adopted by this Authority. Called MCP-to-MCP by the operator's ``request_adoption``. Verifies the operator controls ``operator_npub`` (inline Schnorr bound to the adoption sentinel — no relay round-trip), records a durable ``pending`` row, and fires a best-effort owner-notification DM. Does NOT provision — provisioning waits for the owner's ``approve_adoption``.
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  • Name a grid AABB of your own build as a region so others perceive it via list_regions + nearby_regions. Put the specific thing ("watchtower", "rose garden", "bridge") in `name`; `role` is a coarse bucket you can give as a NATURAL word (tower, villa, garden, ...) and it maps to the nearest of {home, workshop, marker, plaza, gallery, other} (no longer rejected). Writes a agent_space_regions row owned by YOU. Grid->world: wx=gx*0.5, wy=2.0+gy*0.5, wz=gz*0.5. Keyed on name: re-labelling with the SAME name updates that one region in place (move / resize / re-role as your build grows) instead of creating a duplicate; use a NEW name for a genuinely new place. role defaults to "marker", visibility to "public". Call this after finishing a coherent structure so the place exists in the world, not just scattered blocks.
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  • Return the markers that make up a lab panel by slug — each resolved to its Mediora marker slug, name and one-line summary — plus the panel's search aliases (CBC/FBC, CMP, LFT, TFT, renal panel, …). Answers "what's included in a CBC / lipid panel / CMP". Follow each marker into mediora__explain_marker for full detail.
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Matching MCP Servers

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    Keyless, local MCP server bringing ISTQB / OWASP / IEEE / ISO / EU AI Act QA standards into your AI client. Standards-grounded retrieval, deterministic QA effort estimation, automated QA document quality review (0-100 rubric), and JUnit/CSV test-results flakiness analysis.
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    Apache 2.0
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    Analyzes Playwright test results to provide structured QA insights, including test metrics, failure analysis, bug report drafts, and release-quality recommendations.
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Matching MCP Connectors

  • Return the per-marker trajectory analyser output for the authenticated patient — HbA1c rising, eGFR falling, ferritin depleting, etc. Each finding includes the marker slug, kind, severity (Info / Notice / Important), trend per year, sample count, date range and a supportive headline + detail. Proxies https://api.mediora.ai/api/trajectory/warnings with the user's token forwarded as Authorization: Bearer.
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  • Analyze any image using AI vision for manual inspection, debugging, visual description, or supplemental critique. Provide exactly one source: generation_result_id for a Shoot Board generation, uploaded_file_id for a Files item, or image_url for a public HTTPS image. Do not use this as the primary QA mechanism when the user asks to QA, quality-check, validate, review, approve/reject, or assess generated results; for QA requests use queue_generation_result_qa first, then read_generation_result_qa.
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  • Add a short, dated changelog entry to a note — structured evidence of a change (e.g. "Bumped reply target 3-5 → 8-10"), separate from and never touching the note body by default. Purely additive: does not overwrite or clobber content. Two ways to use it: (1) create a NEW numbered entry — if `anchor` is given (exact or substring text of an existing line), a small "((n))" marker pointing to the new entry is appended to that line, failing if `anchor` doesn't match any line (call get_note first if unsure of the exact text); without `anchor` the entry is note-wide with no inline marker. (2) append to an EXISTING entry via `appendTo: n` (e.g. 3 for an existing "((3))" marker) — adds another dated comment/update under that same marker without creating a new marker or number; fails if entry `n` doesn't exist yet. `appendTo` and `anchor` are mutually exclusive — `anchor` is ignored when `appendTo` is set. Use `section` (e.g. "update", "note") to label what kind of entry this is when a marker accumulates several over time — the UI groups/filters by section. Use this instead of update_note/append_to_note when recording a decision or change without altering the visible note text. Capped to the most recent 20 entries total across all markers.
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  • Полный pipeline: URL -> вердикт в SQLite + Obsidian vault. ВАЖНО: долгая операция, ~10-20 мин на 300 комментов (haiku 10м + sonnet QA 8м). `qa=False` — пропустить sonnet-эскалацию (быстрее, но без QA-метрик). Возвращает verdict_id + summary (hard_counts + mood + QA-stats). На публичном сервере отключён env-флагом PJQ_PUBLIC_CLASSIFY_DISABLED=1 — синхронный classify не выдерживает параллельной нагрузки и угрожает Claude Max-подписке. Замена на job queue + worker в работе.
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  • Diff a baseline page mapping against a current one and return a CI-style verdict: PASS / FIX / BLOCK, plus per-element drift (ok, renamed, healable, ambiguous, lost, added, rebound). Pure and deterministic — provide two mappings as JSON with "elements" arrays of {role, name, selector, context?}. Use the companion @ia-qa/self-healing package (npm install -g @ia-qa/self-healing) to capture mappings from your app via its local MCP server ia-qa-heal-mcp, or paste the snippet from ia-qa.com/devtools/selector-drift into your browser console.
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  • Report a customer review of a business you manage into Loppee's moderation queue for a claimed policy violation (spam, harassment, off_topic, fake, or other). IMPORTANT: reporting NEVER removes the review — the review stays published, moderation is HUMAN and REACTIVE, and a moderator removes a review only for a recorded policy violation, never for being negative. Do not use this tool to suppress honest criticism; use respond_to_review to answer it publicly. Filing a report never changes the verification class, review authority, the review's weighting, or recommendation order. Requires allowed_actions include report_review. Requires an owner-scoped management key, explicit business scope, and the tool's permission; exposure tier does not grant or remove access. Under v3, a business owner connects an external agent they already use. Loppee provides scoped API and MCP access only; it does not provide or host that customer-connected agent. Access is available at no charge for now. Call get_agent_identity first. Reportability rules: only a NEGATIVE review (rating 3 stars and below) can be reported at all — a 4-5 star review returns review_report_not_negative; only ONE report may be open at a time — while a prior report is being reviewed a new one returns review_report_already_open; and a review accepts at most 3 reports in its LIFETIME — past that the call returns review_report_limit_reached. Returns review_not_found when the review does not belong to this business and invalid_report_reason for an unknown category.
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  • Return the patient's past medical-test rows (id, test type, test date, risk level, summary, status, abnormal-marker count). Bearer token must be a Mediora patient-scope JWT. Paginated; default 10 per page. Proxies https://api.mediora.ai/api/medical-tests with the user's token forwarded as Authorization: Bearer.
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  • The curated buyer-intent collections (e.g. mcp-servers, testing-qa, browser-automation). Use get_collection for the ranked tools inside one.
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  • Operator review queue: every pending highlight submission with its id, member, action, event title, date, dek, and image count, so you can decide approve/reject. Excludes test fixtures + stale pointers. Pair with ic_admin_approve_highlight / ic_admin_reject_highlight. Args: { detail?: 'summary'|'full' (default 'full'; 'summary' trims each row to id/member_name/action/event_title/date) }. Scope: admin:highlights_review.
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  • Generate a ready-to-commit GitHub Actions workflow that gates a build on IA-QA. Two gate types, combinable: "eval_contract" runs a .ia-eval.yaml through ia-qa-com/eval-action@v1 (LLM quality gate, needs a provider API key as a repo secret), and "cli_checks" runs deterministic primitives via npx @ia-qa/cli (secret scan, prompt-injection scan, security headers…) whose exit code fails the build. Deterministic template — no LLM call, no API key, same inputs give the same file. Returns the YAML, the secrets to create, and the remaining steps. Pair with generate_eval_yaml to produce the contract itself.
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  • Permanently remove a single changelog entry (e.g. a test/junk entry added by mistake). Strips the trailing "((n))" marker from content if the removed entry itself carried it, and decrements the lifetime section counter so a removed entry stops inflating it. A marker number is NOT a unique entry id -- appendTo lets many entries share one n as a running thread. If more than one entry shares n, this fails and lists each entry's `at` timestamp; pass the specific one you mean as `at` to remove only that entry, not the whole thread. Call get_note first if unsure which entries share a marker.
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  • Upcoming showtimes for one film (title or slug; fuzzy-matched — ambiguous queries return candidates), grouped by theater with local times, tags (35mm/qa/sold-out), the night each show belongs to, and a ticket link per showtime. Optional date, time window, and venue filters. In "detailed" mode every showtime also carries its confidence level, source tier, reporting sources, and verified_at timestamp.
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  • Mark a student's typed answers against an existing exam paper. Provide the exam_paper_id and per-question answers keyed by question_number. DeepMark creates a submission and grades it through its canonical marker (per mark point / level descriptor) — results stream into the teacher's editor live. Grading is asynchronous: this returns a submission_id; poll submission_get for the per-question grades.
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  • Use this when deploy_app returns, when checking deployment status, or when the app has errors or is not working as expected. Returns deployment status, e2e test status, QA snapshot, and frontend/backend error logs; treat deployed_and_testing status as non-final, always inspect QA/errors, and call get_e2e_qa_run_details if e2e tests fail.
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