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"Guide to Creating Test Cases and Deploying in Jenkins" matching MCP tools:

  • Create a local container snapshot (async). Runs in background — returns immediately with status "creating". Poll list_snapshots() to check when status becomes "completed" or "failed". Available for VPS, dedicated, and cloud plans (any plan with max_snapshots > 0). Local snapshots are stored on the host disk and count against disk quota. Requires: API key with write scope. Args: slug: Site identifier description: Optional description (max 200 chars) Returns: {"id": "uuid", "name": "snap-...", "status": "creating", "storage_type": "local", "message": "Snapshot started. Poll list_snapshots() to check status."} Errors: VALIDATION_ERROR: Max snapshots reached or insufficient disk quota
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  • PERMANENTLY remove a campaign — cannot be undone, not even with undoChange. The campaign and all its ad groups, ads, and keywords will be deleted. Prefer pauseCampaign in most cases. Returns changeId.
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  • Poll the current status of a token deployment by its intentId. Use this after ava_deploy_token times out, or to check progress of an ava_create_token_intent flow. Returns: status ('deploying' | 'deployed' | 'failed'), contractAddress and explorer links when deployed, errorMessage on failure. Poll every 5-10 seconds. Most deployments complete within 60 seconds. Possible errors: insufficient fee sent, gas spike, RPC timeout — check errorMessage field.
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  • Poll the current status of a token deployment by its intentId. Use this after ava_deploy_token times out, or to check progress of an ava_create_token_intent flow. Returns: status ('deploying' | 'deployed' | 'failed'), contractAddress and explorer links when deployed, errorMessage on failure. Poll every 5-10 seconds. Most deployments complete within 60 seconds. Possible errors: insufficient fee sent, gas spike, RPC timeout — check errorMessage field.
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  • Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).
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  • Returns the canonical guide for using TMV from a coding-agent context. Covers the fix-test-retest loop, how to write a good test prompt, how to read the actionTrail / consoleErrors / failedRequests outputs, and common gotchas. Call this first if you're a new agent on a project — it'll save you a debug session. The same content is served at https://testmyvibes.com/docs/coding-agents.
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Matching MCP Servers

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    The Model Context Protocol (MCP) Jenkins integration is an open-source implementation that bridges Jenkins with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Jenkins tools while maintaining data privacy and security.
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  • Transform any blog post or article URL into ready-to-post social media content for Twitter/X threads, LinkedIn posts, Instagram captions, Facebook posts, and email newsletters. Pay-per-event: $0.07 for all 5 platforms, $0.03 for single platform.

  • Alpic Test MCP Server - great server!

  • 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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  • Atomic test set + cases + mocks + mappings ingest. Creates the test set row, every test case, every mock, and the mapping doc in one call. PREFER THE CLI FOR ON-DISK RECORDINGS. When the dev has a recorded test-set on disk (e.g. `./keploy/test-set-0/` produced by `keploy record`), invoke this via Bash instead — it streams bytes from disk to server in one HTTP round-trip: ``` keploy upload test-set \ --app <namespace.deployment> # or --cloud-app-id <uuid> --branch <uuid|name> # optional, find-or-create on name --test-set <path|name> # e.g. keploy/test-set-0 [--name <override>] # rename on the server ``` The CLI path runs in ~3 seconds for a typical recording; calling this MCP tool directly with the same bundle inlined as args takes minutes because Claude has to serialize ~10K+ tokens of YAML/JSON through tool_use. Reserve this MCP tool for cases where the data is already in conversation context (e.g. you just generated test cases programmatically and don't want to round-trip to disk). Each step is its own DB write; partial failure leaves earlier rows in place — callers can replay safely. `branch_id` is REQUIRED — direct writes to main via MCP are blocked. Every row lands on the branch overlay until merge. `test_cases[].mock_names` lists the mocks each case consumes; the server folds these into the mapping doc on upload. Returns { test_set, test_case_ids, mock_ids }.
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  • Prove the just-generated API test actually catches bugs by applying 3 real source-level mutations to the handler, running the test against each, and reverting. The doc-stated "manufactured proof in the first session" moment. OPT-IN, NOT OPT-OUT — this tool TOUCHES THE DEV'S SOURCE FILES (temporarily). Always ASK the dev for explicit consent before walking the playbook: "I'll apply 3 small temporary changes to <handler file> to prove the test catches them, then revert every change. Proceed?" Only run the playbook on "yes". What the playbook does: 1. Identify the handler file(s) the test exercises by reading <app_dir>/keploy/api-tests/<resource>/test.yaml and grepping for the route paths in the dev's code. 2. Pick 3 concrete mutations the test assertion set should catch — e.g. change a response field's type (Name string → Name int), rename a field (email → mail), remove a field. Choose mutations that map to fields the test ACTUALLY asserts on (read the suite's assertions to inform the pick). 3. For each mutation: apply via Edit, restart the dev's app if needed (hot-reload usually handles this), run keploy test-gen run, capture pass/fail, REVERT via Edit before moving to the next mutation. 4. Run a final "git diff -- <handler file>" to verify all reverts succeeded. If non-empty, HALT and ask the dev to run "git checkout <file>" before continuing. 5. Report: "I made 3 small changes, your test caught M/3. Caught: [concrete list]. Missed: [concrete list, with recommendation]." ABSOLUTE RULES: * Revert is non-negotiable. The dev's working tree must be clean at the end. * Never modify test.yaml, config files, or anything outside the handler source(s) for this resource. * Never run more than 3 mutations in one playbook (more is noise, less is unconvincing). * If you can't identify a clear handler file, ASK the dev rather than guessing. When the dev says "expand coverage to the other resources" → call devloop_expand_coverage next.
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  • Returns the complete setup and usage guide for SwapWizard. Call this FIRST before using any other tool. Covers: required configuration (API key, Alchemy RPC URL, private key), how to use poolId correctly, step-by-step operational flows for swap/zap in/zap out/analyze, transaction execution details, and approval rules.
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  • Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).
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  • Create a third-party LEAD-GENERATION page about a business (NOT a site for that business itself). Use this when the goal is to drive qualified search traffic to someone else's business — affiliate pages, review/guide pages, niche directories. The page is branded as an outside guide (e.g. "Best Roofers in San Diego"), refers to the business in the third person, and routes CTAs to the business's existing website. Differences from create_site: - Slug + page brand are SEO-vanity (e.g. "best-roofers-sandiego"), not the candidate's brand name. - Voice is third-party guide/reviewer — never first person. - Primary CTA is "visit their website"; phone/email demoted. - No specific pricing quoted; differentiators emphasized. - Locality is judged by category, not just address (IT/SaaS/agency stays category-wide even when a city is on file). Pass a business candidate object from search_businesses — that business is the one being PROMOTED. Requires authentication via API key (Bearer token). Generate an API key at webzum.com/dashboard/account-settings. The page generation happens in the background. Use get_site_status to check progress. Returns the businessId (a vanity slug) which can be used to access the page at /build/{businessId}.
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  • Validate a token configuration and get a fee estimate without spending gas or deploying anything. Use this before ava_deploy_token or ava_create_token_intent to confirm the config is valid and see the exact ETH cost. Returns: estimated fee in ETH and USD, resolved feature flags, tier (Starter/Basic/Premium), and any validation errors. Does not create an intent or charge any fee.
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  • Get a full application guide by its stable slug (e.g. 'security-application', 'observable-evaluation'). Returns sections, action items, and linked principles. Use this when you already have the guide slug from guides.list or guides.search. Prefer guides.search when the user describes a topic in natural language; prefer guides.list when you need the full inventory.
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  • Get a full application guide by its stable slug (e.g. 'security-application', 'observable-evaluation'). Returns sections, action items, and linked principles. Use this when you already have the guide slug from guides.list or guides.search. Prefer guides.search when the user describes a topic in natural language; prefer guides.list when you need the full inventory.
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  • Ask a question about one or more videos with visual analysis. Most effective on focused time ranges — use start/end to specify the segment to analyze. BEFORE calling this tool, read the reka://docs/guide resource for recommended workflows. In most cases, you should first: - search_videos to find WHEN something happens, then pass those timestamps here as start/end - segment_video to detect and locate specific objects - get_transcript to read what was said For single-video questions, pass video_id with start/end. For cross-video questions, pass videos — a list of video references with start/end each. For follow-up questions, pass conversation_id from the previous response. You can add start/end to drill into a specific moment while keeping the conversation context. Requires qa_only or full pipeline.
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  • Get pre-built graph template schemas for common use cases. ⭐ USE THIS FIRST when creating a new graph project! Templates show the CORRECT graph schema format with: proper node definitions (description, flat_labels, schema with flat field definitions), relationship configurations (from, to, cardinality, data_schema), and hierarchical entity nesting. Available templates: Social Network (users, posts, follows), Knowledge Graph (topics, articles, authors), Product Catalog (products, categories, suppliers). You can use these templates directly with create_graph_project or modify them for your needs. TIP: Study these templates to understand the correct graph schema format before creating custom schemas.
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  • Given a profile of the authorized test target (technology stack, exposed services, authentication type, OS), return a ranked list of ATT&CK techniques and OWASP test cases most relevant to that profile — not a generic dump of all techniques. Ranking factors: platform match, service match, auth type exposure, technique prevalence. Each result includes why it is relevant to this specific profile, the detection opportunity, and the recommended mitigation. Use when starting an authorized engagement to prioritize the testing scope; pair with pentest_guide to get the full methodology for each top-ranked vector.
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  • Wholesale-delete a recording (test set + its cases + mocks + mapping). `branch_id` is REQUIRED — the delete lays a tombstone overlay on the branch (mergeable). Direct deletes from main via MCP are blocked. Returns { deleted: true } on success, 404 when the (app_id, test_set_id) tuple doesn't resolve to a recording.
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  • Returns the complete setup and usage guide for SwapWizard. Call this FIRST before using any other tool. Covers: required configuration (API key, Alchemy RPC URL, private key), how to use poolId correctly, step-by-step operational flows for swap/zap in/zap out/analyze, transaction execution details, and approval rules.
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