"Creating Playwright Tests by Exploring a Website and Generating Test Cases" matching MCP tools:

• create_tests

  3TG Test Generation

  Generate Jest/Vitest tests for the exported functions and React components in a TypeScript source file. Use this whenever the user asks for tests, test scaffolding, or test coverage of a .ts or .tsx file. Returns the generated test (and any companion .3tg.md / __mocks__) file contents, with paths already translated to the user's `.3tg/` mirror convention. Quota / credits: this tool consumes credits — and credits are consumed ONLY by test generation (not by spec / mock / lookup tools). The accounting is exactly **1 credit per generated test case** (i.e. per `test(...)` / `it(...)` block 3TG emits inside the returned `.test.ts` / `.test.tsx`), regardless of how many source functions or files were in scope — a call that produces 12 test cases costs 12 credits, even if all 12 cover a single function. Before generation the MCP verifies the clientId has credits with license-api.coding-creed.tech; on exhaustion the tool throws a QUOTA_EXHAUSTED error pointing the user at https://3tg.dev. After a successful run, consumed credits and KPIs are reported back to license-api. Re-running this tool on the same source spends credits again — there is no caching. When the previous call returned `enrichment.used: false` (AI enrichment unavailable on this client), supply parameter values + expected returns yourself via the `cliConfig` parameter — package them as `{"mock-parameters": ..., "function-returns": ...}` (same shape AI enrichment would produce) and pass them on a retry call. **Do NOT autonomously write `.3tg/config.3tg.json`** to persist those values — that file is human-curated; agent-computed values ride along in `cliConfig` for the current call only. (Explicit user requests to edit the file are fine — handle those normally.) See the cliConfig parameter description below for the full pattern. CRITICAL POST-CALL ACTION — write returned files to disk: The MCP server does NOT touch the user's filesystem. It returns the generated file CONTENTS in the response's `files` array. After this tool returns, you MUST iterate over `files` and write each entry's `content` verbatim to its `path` using your native file-write capability (e.g. Write / edit_file / create_file — whatever your client exposes). Create parent directories as needed. Returned paths are project-root-relative and already translated to the `.3tg/` mirror convention where applicable (e.g. specs land under `.3tg/<source-path>.3tg.md`; tests / mocks travel through unchanged). Write each path verbatim. Do NOT claim "Generated test file: <path>" unless you have actually written the file. The user will assume the MCP wrote it and waste time looking for a non-existent file. If you can't write for some reason (permission denied, no write capability in this client), return the contents inline in your message so the user can copy-paste them. Never report success silently when the write didn't happen.

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• validate_spec

  3TG Test Generation

  Lint a `.3tg.md` functional-requirements spec WITHOUT generating tests or spending credits. Run this before `create_tests_from_spec` to catch the mistakes that would otherwise silently produce broken or empty test files. WHY THIS EXISTS: 3TG's spec parser is deliberately lenient — it never errors on a malformed `.3tg.md`, it just silently ignores tables it can't parse and emits whatever column names it sees. So a spec can look fine yet compile to nothing useful. This tool runs the same parse 3TG would, then cross-checks the result against the source's real exports (via 3TG's own analysis) and reports problems. WHAT IT CATCHES: - ERROR: the spec parsed to an empty config (no valid table — usually a wrong return-column header; it must be the literal `=>`, or a row/header column-count mismatch). - ERROR: a table targets a function the source does not export (the generated test would import a non-existent symbol). - WARNING: a parameter column matches no parameter of any exported function (likely a typo such as `input_a` for `a`). - INFO: exported functions the spec doesn't cover yet. WHAT IT CANNOT CHECK: whether the `=>` expected-return values are arithmetically correct — 3TG itself doesn't verify that. Treat a `valid: true` result as "structurally sound and ready to compile", not "the expected values are right". This tool is FREE — no clientId, no quota, no test cases consumed. Surface the `summary` and any `diagnostics` back to the user; if there are errors, help them fix the spec, then re-validate.

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• check_test

  Zephex

  Run tests and return structured health — fix_first, broken_areas, failure_clusters, coverage_by_area, blind_spots, deploy probes. Not for finding which files to edit (find_code). Workflow: task=detect (0 credits — framework, command, test file count, missing-test gaps) → task=run once (1 credit hosted on success) → task=failures|missing|why|status|fix_prompt on session_id (0 credits, cached session, no re-run). task=missing scans git diff for source files without tests (0 credits, no suite run). meta.credits and meta.charges_usage show billing. Read summary and fix_first first; detail_level=brief on PASS. Call after every substantive edit, when user asks if tests pass or if tests are missing, or before push. Pass diff_base: main for failures_in_diff. area/file_filter scopes re-runs after a full run. Local stdio: absolute project path (runs on your machine). Hosted: public GitHub URL or inline_files — not local disk paths. NOT for symbols (find_code), packages (check_package), stack brief (get_project_context), URL audit (audit_headers). Example: check_test({ task: "detect", path: "/abs/my-app" }) then check_test({ task: "missing", path: "/abs/my-app" }) then check_test({ task: "run", path: "/abs/my-app" }). Does not modify source.

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• get_account_summary

  mcp-server

  Return a count of cases per lifecycle stage for the creditor's account. Useful for a quick portfolio overview without listing all cases. Stages: PendingContractSigning, PendingVerificationInternal, PendingVerification, NeedsAdditionalDetails, Leads, LeadsQuoteGiven, Active, Paused, Closed, Merged. Note: these counts include the creditor's own test cases; list_cases exposes the `isTestCase` flag that marks them.

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• list_job_tests

  CircleCI MCP Server

  List a CircleCI job's test results, given the job's UUID (a job id from list_jobs or get_job). By default only failing tests are returned — the ones that explain a failure; set all=true to include passing and skipped tests. Narrow further with filter ("result=...", "name=...", "classname=..."). A job's full test set can be very large, so the number returned is capped by limit (defaults to 100); when more tests matched than were returned, truncated is true — narrow the filter or raise limit for the rest.

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• test_hypothesis

  Ground Truth - Fresh Evidence for AI Agents

  Run a small verification plan made of concrete live checks and summarize whether a hypothesis is supported. Use this when one conclusion depends on multiple simple checks such as endpoint reachability, npm search counts, or whether a page contains an exact substring. This is a coordination tool, not an open-ended research agent: every test must be explicitly defined in advance, and tests run in order with no branching or early exit. The final verdict is mechanical: all tests passing => SUPPORTED, zero passing => REFUTED, otherwise PARTIALLY SUPPORTED. Use verify_claim when you already have evidence URLs, estimate_market for category sizing, and compare_competitors when you already know exact package names.

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• varrd_ai

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Talk to VARRD AI (~$0.25/turn). Describe any trading idea in plain language and the system handles everything — loading decades of market data, charting your pattern, running statistical tests, backtesting with stops, and generating exact trade setups. MULTI-TURN: First call creates a session. Keep calling with the same session_id, following context.next_actions each time. 1. Your idea -> VARRD charts pattern 2. 'test it' -> statistical test (event study or backtest) 3. 'show me the trade setup' -> exact entry/stop/target prices HYPOTHESIS INTEGRITY (critical): VARRD tests ONE hypothesis at a time — one formula, one setup. Never combine multiple setups into one formula or ask to 'test all' — each idea must be tested as a separate hypothesis for the statistics to be valid. Say 'start a new hypothesis' between ideas to reset cleanly. - ALLOWED: Test the SAME setup across multiple markets ('test this on ES, NQ, and CL') — same formula, different data. - NOT ALLOWED: Test multiple DIFFERENT formulas/setups at once — each is a separate hypothesis requiring its own chart-test-result cycle. If ELROND council returns 4 setups, test each one separately: chart setup 1 -> test -> results -> 'start new hypothesis' -> chart setup 2 -> etc. KEY CAPABILITIES you can ask for: - 'Use the ELROND council on [market]' -> 8 expert investigators - 'Optimize the stop loss and take profit' -> SL/TP grid search - 'Test this on ES, NQ, and CL' -> multi-market testing - 'Simulate trading this with 1.5 ATR stop' -> backtest with stops EDGE VERDICTS in context.edge_verdict after testing: - STRONG EDGE: Significant vs zero AND vs market baseline - MARGINAL: Significant vs zero only (beats nothing, but real signal) - PINNED: Significant vs market only (flat returns but different from market) - NO EDGE: Neither significant test passed TERMINAL STATES: Stop when context.has_edge is true (edge found) or false (no edge — valid result). Always read context.next_actions.

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• get_template_schemas

  RationalBloks

  Get pre-built template schemas for common use cases. ⭐ USE THIS FIRST when creating a new project! Templates show the CORRECT schema format with: proper FLAT structure (no 'fields' nesting), every field has a 'type' property, foreign key relationships configured correctly, best practices for field naming and types. Available templates: E-commerce (products, orders, customers), Team collaboration (projects, tasks, users), General purpose templates. You can use these templates directly with create_project or modify them for your needs. TIP: Study these templates to understand the correct schema format before creating custom schemas.

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• pictify_batch_render

  mcp

  Start a batch render job to generate multiple images from a single template — from inline variable sets, or from a hosted CSV where every row becomes a render. Each variable set produces a separate image. Supports up to 100 items per batch (plan-dependent). Common use cases: generating personalized social cards for all team members, product images for an entire catalog, event badges for all attendees, certificate images for course graduates, or marketing assets with localized content. WORKFLOW: 1) Use pictify_get_template_variables to discover variables, 2) Call this tool with an array of variable sets, 3) Use pictify_get_batch_results to poll for completion and get result URLs. The job runs asynchronously — this tool returns immediately with a batchId (HTTP 202). For generating a single multi-page PDF instead, use pictify_render_multi_page_pdf.

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• search_cases

  Folketinget Dk

  Convenience search over Sag (cases/bills): finds cases whose Danish title (titel) contains a substring. Sorted by most recently updated. Use this to look up legislation/matters by keyword; for full control use query_entity.

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• submit_test_batch

  TestMyVibes

  Queue up to 20 AI tests at once and run them in parallel instead of one-after-another. Each test in the batch costs 1.15× its base credits (the parallel premium). Returns the shared batchId and a per-test breakdown so you can poll each jobId individually. Use this when you have an independent set of tests to run (e.g. signup + login + dashboard + settings + delete across one customer site) and want them done in minutes rather than queued through a serial worker. AI runner only — human-runner batching ships separately.

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• mediora__get_patient_history

  mediora-mcp

  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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• delete_website

  squirrelscan

  Delete a website from the organization (soft delete: past audits, reports, and issues are preserved, and published report links keep working). Frees a slot under the plan's website limit. Re-adding the same domain later registers a fresh website with a new website_id. Call once without confirm to see what will happen; call again with confirm: true to delete. To remove many sites at once, use delete_websites.

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• dc_dmv_offices

  District of Columbia DMV

  Find a Washington DC DMV location — the District of Columbia Department of Motor Vehicles service centers where residents renew a license, register a vehicle or get a REAL ID, plus the Half Street inspection station, the two self-service emissions-inspection kiosks, the Deanwood road-test and CDL center, and Adjudication Services for ticket hearings. Returns street address, coordinates and the DMV website. Answers "DC DMV near Georgetown", "where is the DC vehicle inspection station", "DC DMV road test location", and "how many DMV service centers does Washington DC have". Covers all 9 published DMV locations.

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• test_skill_trigger

  agent4.io

  Reality-check whether your prompts actually trigger tool calls (dry-run) — run this after writing/changing a skill instead of counting corpses in production. Replays your messages N times against the **production** system-prompt assembly, tool schemas and this tenant's actual model routing, capturing only the model's tool-call decision: **tool side effects are NOT executed**, no session is stored. Tokens count toward the tenant quota (messages≤5, samples≤5, at most 25 calls per invocation — pick test messages carefully). Two modes for the skill's two battlefields: - loaded=false (default): first turn, skill not loaded — tests whether the trigger in description works; - loaded=true: simulates post-load_skill — tests the quality of instructions (incl. few-shot examples). Returns per-message hit counts plus claimed_without_call (the model said "noted" WITHOUT calling the tool — the worst failure, fix first). Cover edge cases in your test messages: numbers with spaces, buried in long questions, corrections, email-only. The loop: create_skill → check warnings (static lint) → test_skill_trigger (dynamic reality check) → adjust description / add examples → re-test until the hit rate holds.

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• testrail_list_tests_for_run

  Robot Actions — Remote Device Control

  List EVERY test inside a TestRail run, walking all pages server-side so the caller never handles offsets. Returns each test with its T-id (test instance, e.g. T1234), C-id (underlying case_id — feed this to testrail_get_case), title, and status_id. Filter by statusIds (TestRail status: 1=passed, 2=blocked, 3=untested, 4=retest, 5=failed) to e.g. list only failed tests when triaging. Capped at 20 pages (5000 tests); check capReached before concluding a test is not in the run.

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• get_app_status

  AppDeploy

  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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• delete_site

  Valet

  Permanently delete a website and stop serving it. Identify it by name, which requires connecting a Valet account, or by the site_token returned when it was published anonymously — whoever published a site can always take it down. This cannot be undone.

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• autonomous_varrd_ai

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Point VARRD's autonomous AI in a direction and let it discover edges for you. Give it a topic and it draws from one of the most comprehensive market structure knowledge graphs ever built — containing ideologies and theories, not statistics — so it generates genuinely novel hypotheses rather than overfitting to what already worked. BEST FOR: Exploring a space broadly. Give it 'momentum on grains' and it might test wheat seasonal patterns, corn spread reversals, or soybean crush ratio momentum. It propagates from your seed idea into related concepts you might not think of. Returns a complete result — edge or no edge, stats, trade setup. Each call tests ONE hypothesis through the full pipeline (~$0.25/idea). Call again for another idea. Use 'varrd_ai' instead when YOU have a specific idea to test and want full control over each step.

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• check_sponsor_license

  Sponsor Finder

  Check whether a company holds a UK or Netherlands work-visa sponsorship licence. The register lists official registered legal entity names, not brand, product, or trading names. If the user provides a brand name, product name, or website, first determine the company's registered legal name (via web search, the company's own website, or the relevant companies register) and pass that. Tolerates minor typos but not brand-vs-legal-name mismatches. Returns licence routes, ratings, locations and register dates. If results are ambiguous or none are found, refine the legal name and try again. For exploring or filtering many companies, use search_sponsors instead.

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