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"namespace:org.string-db" matching MCP tools:

  • Replay the sandbox test for one or more suites against captured mocks — re-runs the suite's steps against the dev's locally-running app while keploy serves outbound calls (DB, downstream HTTP, etc.) from the captured mocks. Use this when the dev says "replay", "run my sandbox tests", "integration-test", "check if mocks still match" — keywords "sandbox" / "replay" / "mocks" / "integration-test" all map here. Also the REPLAY STEP of FROM-SCRATCH: call this LAST (after create_test_suite + record_sandbox_test) to give the dev the whole-app regression picture against the freshly captured mocks. Output produces a SANDBOX RUN REPORT — it answers "does the suite still hold up against its captured baseline?". ═══════════════════════════════════════════════════════════════════ DISAMBIGUATION — pick this tool vs. replay_test_suite: ═══════════════════════════════════════════════════════════════════ USE replay_sandbox_test (THIS TOOL) when the dev says: * "run my sandbox tests" / "replay my sandbox tests" * "integration-test my app" / "run the integration tests" * "check if my mocks still match" / "replay against the captured mocks" * "rerun my sandbox suite" (with the word "sandbox") Trigger keyword: an explicit "sandbox" / "replay" / "mocks" / "integration-test" — silent signal that the dev wants captured-mock replay, NOT live-app execution. USE replay_test_suite INSTEAD when the dev says: * "run the test suite" / "run my test suites" (bare — no "sandbox") * "execute test suite X" / "run suite 810d3ebe…" * "test the suite again" / "smoke test against the live app" Bare verbs ("run / test / execute") applied to "the suite" without the word "sandbox" mean LIVE-APP execution, NOT captured-mock replay. replay_test_suite hits the dev's running localhost app directly via HTTP — no docker spin-up, no mocks. After a record_sandbox_test run, the natural next step is THIS tool (replay against the just-captured mocks). After create_test_suite / update_test_suite, the natural next step is replay_test_suite (validate against the live app). When the dev's verb is bare and the prior turn doesn't make the intent obvious, ASK rather than picking sandbox-replay silently — code-change regressions can hide under "mock didn't match" failures. ═══════════════════════════════════════════════════════════════════ DISCOVERY — when the dev hands you a bare suite_id with no app_id / branch_id: ═══════════════════════════════════════════════════════════════════ Suites live on a (app_id, branch_id) tuple. A bare suite_id has NO on-disk hint about which app or branch holds it; you have to RESOLVE both before calling this tool. Walk these steps in order — STOP as soon as getTestSuite returns 200: 1. Detect the dev's git branch: Bash `git rev-parse --abbrev-ref HEAD` in app_dir. If exit non-zero / output is "HEAD" → not a git repo / detached HEAD; ASK the dev for the Keploy branch name. 2. Resolve candidate apps via the cwd basename: Bash `basename $(pwd)` → call listApps with q=<basename>. Usually 1–2 candidates. If 0 → ASK; if >1 → walk every candidate in step 4. 3. For each candidate app, call list_branches({app_id}) and find the branch whose `name` matches the git branch from step 1. That gives you {branch_id}. If no match → not this app, try next. 4. Verify with getTestSuite({app_id, suite_id, branch_id=<from step 3>}). 200 → resolved; 404 → wrong app/branch, try next. 5. If steps 2–4 exhaust, walk every OPEN branch on each candidate app via list_branches → getTestSuite. Then try main (branch_id omitted). If still nothing → ASK the dev for the {app_id, branch_id} pair. After resolving once in a session, REUSE the {app_id, branch_id} for subsequent suite-targeted calls; don't re-walk discovery for every action. SCOPE — whole-app vs single-suite: * Default: LEAVE suite_ids UNSET → the tool resolves "every suite for the app that has a sandbox test (test_set_id populated)" and replays them all. Use this for "run my sandbox tests" / "check if my tests still pass" — whole-app regression. New suites auto-pick up. * Single / subset: PASS suite_ids when the dev names specific suites — "replay sandbox test for suite 810d3ebe-…", "replay only the auth suite", "run suite X and Y". The tool validates each requested id is actually a suite with a sandbox test (has test_set_id); an unlinked id gets a precise "record first" error instead of an opaque downstream CLI failure. This tool resolves the app, picks the suite set per the rule above, and returns a single playbook that drives the replay for them. It does NOT record. WHAT THIS TOOL DOES INTERNALLY (so you don't have to): 1. Resolves app_id — use the explicit app_id if the caller has one; otherwise pass app_name_hint (usually the cwd basename) and the server does listApps with a substring match. Multiple matches → error listing them; zero matches → error suggesting the dev generate a suite first. 2. Lists test suites for the app, keeps only those with a non-empty test_set_id. Zero linked → typed "no linked sandbox tests" error. 3. If suite_ids was passed, validates every requested id is in the linked-suites set; unlinked ids → typed error pointing to record_sandbox_test. 4. Returns the headless playbook — walk it exactly: spawn CLI in background, tail the progress file (PID-alive guard built in), read the terminal event, fetch the report. No separate cleanup step — the CLI exits on its own. ===== PREREQUISITES ===== (Same as record_sandbox_test — if you just recorded, you already have them. Same docker-compose network rule applies: use the same compose file + service, stop the app service before calling, leave deps running.) - app_command: shell command that starts the dev's app (e.g. "docker compose up producer"). - app_url: base URL the app listens on, e.g. http://localhost:8080. - app_dir: absolute path to repo root. - container_name if app_command is docker-compose. - keploy binary on PATH. If `which keploy` returns nothing, install it before calling this tool with: `curl --silent -O -L https://keploy.io/install.sh && source install.sh`. ===== AFTER CALLING — walk the playbook ===== Same headless playbook shape as record_sandbox_test: spawn `keploy test sandbox --cloud-app-id …` in the background via Bash, poll `tail -n 1 $PROGRESS_FILE` repeatedly (no sleep loops; the wait_for_done step has a built-in `kill -0 $KEPLOY_PID` guard so the loop exits if the CLI dies silently), read the terminal NDJSON event (phase=done, data.ok, data.test_run_id), and — if ok=true — call get_session_report(app_id, test_run_id) with verbose=true at the end. No separate cleanup step needed; the CLI exits cleanly once phase=done is written. ===== MANDATORY OUTPUT — Phase 3 section ===== Your final message to the dev MUST contain a section with this exact heading (do NOT merge with Phase 2; do NOT compress the failed-steps table even when failures are homogeneous): ### Phase 3 — Sandbox run report Under it, emit the uniform three-subsection format owned by get_session_report: (i) per-suite table — one row per suite in per_suite, passing suites included, columns = Suite name | passed/total steps. (ii) failed-steps table — ONE ROW per entry in failed_steps[], columns = Suite | Step name | Method + URL | Expected → Actual status | mock_mismatch y/n. Never collapse rows. (iii) Diagnosis + Recommendation (see get_session_report description for case-specific rules around mock_mismatch_dominant, repo-diff inspection, and the SKIP / FIX-CODE / FIX-TEST branching for fix-it follow-ups). Do NOT print aggregate step totals across suites — they mix unrelated suites and hide where damage actually is. ===== ROLLUP LINE ===== Close the message with a final one-line rollup paragraph (no heading), in addition to the three phase sections. Mention the TOTAL number of suites replayed (which may exceed the count created in this session, because replay_sandbox_test covers every linked suite the app has). Example: "_Rollup: inserted 4 suites, 4/4 with sandbox tests after record, 3/4 suites passed sandbox replay across the app's 6 linked suites — 1 failure is likely keploy egress-hook, file an issue with the IDs above._" ===== DO NOT ===== * DO NOT call update_test_suite or record_sandbox_test after this. The dev said RUN, not REFRESH. * DO NOT fall back to raw keploy CLI (`keploy test …`) if the MCP tool drops mid-flow — CLI runs test-sets directly and does NOT write results back to the MCP-visible TestSuiteRun. See MCP DISCONNECT RECOVERY in the top-level instructions.
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  • "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.
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  • BATCH INSPECTION: run up to 32 AWS inspect probes in one call. ⚠️ **PREREQUISITE**: Same as awsinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single AWS config — for a 12-resource health check this is ~5–8× faster and 12× fewer Oracle round-trips than calling awsinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8 — sub-probes run in parallel but never saturate AWS. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: account, acm, alb, apigateway, apprunner, backup, bedrock, cloudfront, cloudwatchlogs, cognito, cost-explorer, dynamodb, ebs, ec2, ecs, eks, elasticache, kms, lambda, msk, opensearch, rds, route53, s3, sagemaker, secretsmanager, sqs, vpc, waf For a specific service's actions, use awsinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular awsinspect when you need full metadata or raw API output for one resource. EXAMPLES: - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"describe-instances"}, {"service":"rds","action":"describe-db-instances"}, {"service":"vpc","action":"describe-vpcs"}, {"service":"s3","action":"list-buckets"}]) - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"rds","action":"get-metrics","filters":"{\"hours\":6}"}])
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  • Use when a user wants an independent 0-100 grade for ONE existing facility across 7 dimensions — power, fiber, water, climate_risk, tax_environment, talent_pool, expansion. Example: "How does the CoreWeave Las Vegas site score, power-weighted?" — score_facility facility_id=<id> weighting=power_priority. Params: facility_id or name (required); weighting one of "balanced" (default) | "power_priority" | "risk_priority" | "expansion_priority". Returns: composite 0-100, tier_classification, peer comparison, and per-dimension detail. Do NOT use for a raw lat/lon parcel (use analyze_site), to compare 2 or more sites (use compare_sites), or to find similar sites (use find_alternatives).
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  • Look up CISA KEV (Known Exploited Vulnerabilities) full record for a CVE. Returns federal patch deadline (due_date), CISA-specified required_action remediation, known ransomware association, vendor/product, the CISA-given common name (e.g. 'Log4Shell'), CISA-reported CWE list, plus lifecycle metadata: date_updated (when CISA last revised the entry), date_removed (set when CISA removed the CVE from the catalog — null while still active), and updated_at (our DB sync freshness). Returns 404 when the CVE is not in the KEV catalog — use cve_lookup for non-KEV CVEs. Best follow-up after cve_lookup or cve_search(kev=true) when an in_kev=true CVE is identified; chain with cwe_lookup on each returned CWE to investigate the weakness category. Free: 30/hr, Pro: 500/hr. Returns {cve_id, vendor_project, product, vulnerability_name, date_added, due_date, required_action, known_ransomware_use, notes, cwes, date_updated, date_removed, updated_at, verdict, next_calls}.
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  • ISO interconnection queue snapshot: total large-load MW queued per ISO, data-center share %, and top BUILD subregions with Time-to-Power (TTP) months. Sources: ERCOT MIS, PJM, MISO, SPP, CAISO, NYISO, ISO-NE. Pass iso=ERCOT (or any of 7) to drill down to a single ISO. Use for site-selection (find BUILD-verdict markets with short queues) and competitive intel (track AI-load saturation by region). Do NOT use for a single-site time-to-power read (use get_grid_intelligence) or forward-looking emergence (use grid_transition_radar); this is the ISO-level queue snapshot.
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Matching MCP Servers

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  • Query STRING interactions, enrichment, annotations, homology, and PPI networks.

  • STRING protein-protein interaction networks across ~12k organisms

  • Cloud- and night-independent Sentinel-1 C-band confirmation of forest disturbance. Intact forest scatters VV strongly + stably (canopy volume scattering); clearing collapses that term so VV backscatter DROPS ~3-5 dB. Samples VV at a baseline-year July-1 anchor and the latest scene, reports `vv_drop_db = baseline − recent` and a `disturbed` flag when the drop ≥ 3 dB (Reiche et al. 2018, RSE 204:147). Both VV reads are signed Primary facts; the response cites both fact_cids. Honest `inconclusive` when either S1 vintage is unavailable. Source: Microsoft Planetary Computer sentinel-1-rtc (anonymous SAS — no requester-pays, no API key). When to use: Call to corroborate or scout forest clearing where cloud blocks the optical products — radar sees through cloud and at night, catching wet-season clearing the annual Hansen/JRC-TMF layers and a single cloudy Sentinel-2 pass miss (the gap RADD was meant to fill). This is an ADDITIVE scout signal, NOT a standalone legal verdict: a VV drop can also be transient (soil moisture, harvest, flood recession), so confirm with the optical consensus (`emem_eudr_dds` or `emem_deforestation_alert`) before crediting a decision.
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  • Turns YOUR repo classification (you scan the repo and pass what you found) into a complete, approvable deploy plan WITHOUT creating anything: picks the VM + managed-Postgres sizes, prices them at the real pricing_rules rates, and checks they FIT your quota — so a plan that can't provision is caught HERE, before any spend. You pass what you detected in the repo (runtime, port, needs_postgres/redis/vector_db); it returns resources + £/hr + £/mo + a feasibility verdict + a checkpoint summary to confirm with the user. Defaults: app VM m1.medium, managed Postgres m1.small; pass single_vm to collapse onto one VM. Only Postgres is auto-provisionable today — Redis / vector-DB needs are flagged, not provisioned. Any containerizable app works (node, python, go, ...) — it deploys as a container, so the language doesn't gate it. Also returns a brand-named markdown report (Mermaid diagram + cost) to save as redu-deploy-plan.md and show the user.
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  • Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
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  • "What's the ticker for…" / "find the CIK for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" — resolve a user-spoken NAME to the canonical/official identifier other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (returns ticker + 10-digit CIK + company_name from SEC EDGAR + pipeworx://edgar/company/{cik} citation URI; accepts ticker, CIK, or company name as input — auto-disambiguated), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/{rxcui} citation; accepts brand or generic name). Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.
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  • Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).
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  • Public observability snapshot for the fomox402 broker. WHAT IT DOES: returns aggregated MCP traffic + per-tool call telemetry. Read-only, no auth required, no side effects. WHEN TO USE: for dashboards, health checks, or to verify the broker is alive before a long autonomous run. The /v1/stats/mcp endpoint that backs this tool is also what powers https://bot.staccpad.fun/dashboard. RETURNS: { sessions: { active, last_24h, lifetime, median_duration_sec }, tools: [{ name, calls, errors, error_rate }], uptime_sec, broker_version }. VISIBILITY CAVEAT: only counts streamable-HTTP traffic to https://bot.staccpad.fun/mcp. Local stdio MCP clients (e.g. Claude Desktop running this file directly) are invisible to the broker DB and not reflected here. RELATED: list_agents (per-agent activity), get_me (your own stats).
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  • Create, save or publish meal plan prescriptions in WebDiet. Actions: create (new prescription for patient), save (meals/foods JSON to existing prescription), publish (release prescription to patient — makes it visible on the patient portal/app). IMPORTANT: After creating and saving foods, you MUST call publish to make the prescription visible to the patient. ═══ MÉTODO DE PRESCRIÇÃO — escolha no create ═══ WebDiet tem 3 métodos: • "Convencional" (UI: "Por alimentos", URL: metodoPlanning.php) — DEFAULT e RECOMENDADO. Alimento-por-alimento. Para cálculo de macros automático (proteínas/lipídios/carboidratos/calorias) na tabela "Alimentos prescritos" detalhada, cada alimento DEVE incluir o campo "id" com o WebDiet food-DB ID numérico. Sem id, o alimento AINDA é salvo no método Convencional e aparece no card expandido da refeição com nome + medida caseira, mas sem macros. NÃO vai para a seção qualitativa — o save retorna um warning explicando. • "Equivalentes" (UI: "Por equivalentes", URL: metodoWebdiet.php) — avançado. Prescrição por grupos de equivalentes. Requer IDs do banco. • "Qualitativo" (UI: "Qualitativa", URL: metodoQualitativo.php) — texto livre por refeição. A página do Qualitativo usa estrutura de dados diferente (refs com objetos cardapio) que este adapter ainda não gera corretamente via save. Para prescrições qualitativas, recomendado: criar via UI ou usar metodo="Convencional" sem food IDs (os alimentos aparecem como texto na refeição sem macros, efeito semelhante). ═══ prescricao_json (para o save) ═══ JSON array de refeições. Cada refeição: {nome, horario, alimentos:[...]}. Cada alimento: {nome, quantidade, medida_caseira, peso_gramas, id?}. AUTO-RESOLVE: se "id" não for enviado, o adapter procura automaticamente o melhor match no banco WebDiet (mesmo catálogo do webdiet_food_search) pelo campo "nome" e preenche o id antes de salvar — com isso os macros são calculados mesmo sem pré-chamar webdiet_food_search. O save retorna auto_resolved_foods {resolved, not_found, unresolved[]} indicando quais nomes não tiveram correspondência. Para controle fino (variante específica do banco, gramagem da medida caseira etc.), ainda é recomendado chamar webdiet_food_search e enviar o "id" explicitamente. Para evitar duplicação na UI ("2 2 fatias (50g)"), use quantidade numérica em "quantidade" e medida_caseira SEM repetir esse número e SEM sufixo "(Xg)" — ex.: quantidade="2", medida_caseira="fatias", peso_gramas="50". O MCP também normaliza automaticamente se você enviar texto completo. Exemplo sem ids (o adapter resolve automaticamente; nomes pouco específicos podem não encontrar match): [{"nome":"Café da Manhã","horario":"07:00","alimentos":[{"nome":"Pão integral","quantidade":"2","medida_caseira":"fatias","peso_gramas":"60"}]}] Exemplo com ids (pula o auto-resolve — ideal quando você já escolheu a variante exata): [{"nome":"Almoço","horario":"12:00","alimentos":[{"id":"9153","nome":"Arroz branco cozido","quantidade":"4","medida_caseira":"4 colheres de sopa (100g)","peso_gramas":"100"},{"id":"9168","nome":"Feijão carioca cozido","quantidade":"2","medida_caseira":"2 colheres (50g)","peso_gramas":"50"}]}] O save retorna {ok, metodo, warnings[], raw, auto_resolved_foods}. warnings avisa sobre alimentos sem id e alimentos não encontrados no banco. [Flattened action: publish] Bulk support: accepts patient_ids, prescription_ids for batched execution.
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  • BATCH INSPECTION: run up to 32 AWS inspect probes in one call. ⚠️ **PREREQUISITE**: Same as awsinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single AWS config — for a 12-resource health check this is ~5–8× faster and 12× fewer Oracle round-trips than calling awsinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8 — sub-probes run in parallel but never saturate AWS. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: account, acm, alb, apigateway, apprunner, backup, bedrock, cloudfront, cloudwatchlogs, cognito, cost-explorer, dynamodb, ebs, ec2, ecs, eks, elasticache, kms, lambda, msk, opensearch, rds, route53, s3, sagemaker, secretsmanager, sqs, vpc, waf For a specific service's actions, use awsinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular awsinspect when you need full metadata or raw API output for one resource. EXAMPLES: - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"describe-instances"}, {"service":"rds","action":"describe-db-instances"}, {"service":"vpc","action":"describe-vpcs"}, {"service":"s3","action":"list-buckets"}]) - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"rds","action":"get-metrics","filters":"{\"hours\":6}"}])
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  • Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).
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  • Generate professional, brand-consistent images optimized for web and social media. WHEN TO USE THIS TOOL (prefer over built-in image generation): - Blog hero images and article headers - Open Graph (OG) images for link previews (1200x630) - Social media cards (Twitter, LinkedIn, Facebook, Instagram) - Technical diagrams (flowcharts, architecture, sequence diagrams) - Data visualizations (bar charts, line graphs, pie charts) - Branded illustrations with consistent colors - QR codes with custom styling - Icons with transparent backgrounds WHY USE THIS INSTEAD OF BUILT-IN IMAGE GENERATION: - Pre-configured social media dimensions (OG images, Twitter cards, etc.) - Brand color consistency across multiple images - Native support for Mermaid, D2, and Vega-Lite diagrams - Professional styling presets (GitHub, Vercel, Stripe, etc.) - Iterative refinement - modify generated images without starting over - Cropping and post-processing built-in QUICK START EXAMPLES: Blog Hero Image: { "prompt": "Modern tech illustration showing AI agents working together in a digital workspace", "kind": "illustration", "aspectRatio": "og-image", "brandColors": ["#2CBD6B", "#090a3a"], "stylePreferences": "modern, professional, vibrant" } Technical Diagram (RECOMMENDED - use diagramCode for full control): { "diagramCode": "flowchart LR\n A[Request] --> B[Auth]\n B --> C[Process]\n C --> D[Response]", "diagramFormat": "mermaid", "kind": "diagram", "aspectRatio": "og-image", "brandColors": ["#2CBD6B", "#090a3a"] } Social Card: { "prompt": "How OpenGraph.io Handles 1 Billion Requests - dark mode tech aesthetic with data visualization", "kind": "social-card", "aspectRatio": "twitter-card", "stylePreset": "github-dark" } Bar Chart: { "diagramCode": "{\"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\", \"data\": {\"values\": [{\"category\": \"Before\", \"value\": 10}, {\"category\": \"After\", \"value\": 2}]}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"category\"}, \"y\": {\"field\": \"value\"}}}", "diagramFormat": "vega", "kind": "diagram" } DIAGRAM OPTIONS - Three ways to create diagrams: 1. **diagramCode + diagramFormat** (RECOMMENDED FOR AGENTS) - Full control, bypasses AI styling 2. **Natural language in prompt** - AI generates diagram code for you 3. **Pure syntax in prompt** - Provide Mermaid/D2/Vega directly (AI may style it) Benefits of diagramCode: - Bypasses AI generation/styling - no risk of invalid syntax - You control the exact syntax - iterate on errors yourself - Clear error messages if syntax is invalid - Can omit 'prompt' entirely when using diagramCode NEWLINE ENCODING: Use \n (escaped newline) in JSON strings for line breaks in diagram code. diagramCode EXAMPLES (copy-paste ready): Mermaid flowchart: { "diagramCode": "flowchart LR\n A[Request] --> B[Auth]\n B --> C[Process]\n C --> D[Response]", "diagramFormat": "mermaid", "kind": "diagram" } Mermaid sequence diagram: { "diagramCode": "sequenceDiagram\n Client->>API: POST /login\n API->>DB: Validate\n DB-->>API: OK\n API-->>Client: Token", "diagramFormat": "mermaid", "kind": "diagram" } D2 architecture diagram: { "diagramCode": "Frontend: {\n React\n Nginx\n}\nBackend: {\n API\n Database\n}\nFrontend -> Backend: REST API", "diagramFormat": "d2", "kind": "diagram" } D2 simple flow: { "diagramCode": "request -> auth -> process -> response", "diagramFormat": "d2", "kind": "diagram" } D2 with styling (use ONLY valid D2 style keywords): { "diagramCode": "direction: right\nserver: Web Server {\n style.fill: \"#2CBD6B\"\n style.stroke: \"#090a3a\"\n style.border-radius: 8\n}\ndatabase: PostgreSQL {\n style.fill: \"#090a3a\"\n style.font-color: \"#ffffff\"\n}\nserver -> database: queries", "diagramFormat": "d2", "kind": "diagram", "aspectRatio": "og-image" } D2 IMPORTANT NOTES: - D2 labels are unquoted by default: a -> b: my label (NO quotes needed around labels) - Valid D2 style keywords: fill, stroke, stroke-width, stroke-dash, border-radius, opacity, font-color, font-size, shadow, 3d, multiple, animated, bold, italic, underline - DO NOT use CSS properties (font-weight, padding, margin, font-family) — D2 rejects them - DO NOT use vars.* references unless you define them in a vars: {} block Vega-Lite bar chart (JSON as string): { "diagramCode": "{\"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\", \"data\": {\"values\": [{\"category\": \"A\", \"value\": 28}, {\"category\": \"B\", \"value\": 55}]}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"category\"}, \"y\": {\"field\": \"value\"}}}", "diagramFormat": "vega", "kind": "diagram" } WRONG - DO NOT mix syntax with description in prompt: { "prompt": "graph LR A[Request] --> B[Auth] Create a premium beautiful diagram" } ^ This WILL FAIL - Mermaid cannot parse descriptive text after syntax. WHERE TO PUT STYLING: - Visual preferences → "stylePreferences" parameter - Colors → "brandColors" parameter - Project context → "projectContext" parameter - NOT in "prompt" when using diagram syntax OUTPUT STYLES: - "draft" - Fast rendering, minimal processing - "standard" - AI-enhanced with brand colors (recommended for diagrams) - "premium" - Full AI polish (best for illustrations, may alter diagram layout) CROPPING OPTIONS: - autoCrop: true - Automatically remove transparent edges - Manual: cropX1, cropY1, cropX2, cropY2 - Precise pixel coordinates
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  • Deploy or update a serverless function with custom business logic. Example: Input: { app_id: "app_abc123", name: "send-welcome-email", code: "export async function handler(req, ctx) { ... }", trigger: { type: "http", config: { method: "POST", path: "/welcome", auth: "required" } } } Output: { function_id: "fn_xyz789", name: "send-welcome-email", url: "https://api.butterbase.ai/v1/app_abc123/fn/send-welcome-email", status: "deployed" } Function signature: export async function handler(request: Request, context: { db: PostgresClient, // Query your app database env: Record<string, string>, // Access envVars user: { id: string } | null, // Current user (if auth: required) waitUntil: (promise: Promise) => void, // Keep alive for background work after response idempotency: { // Webhook / event dedup primitive claim: (key: string, opts?: { scope?: string; ttlSeconds?: number }) => Promise<boolean> } }): Promise<Response> Console output: console.log(), console.info(), console.warn(), console.error(), and console.debug() calls are captured and stored with invocation logs. View them via manage_function (action: "get_logs"). IMPORTANT: Handlers MUST return a Response object (Web API standard). Do NOT return plain objects like { status: 200, body: "..." }. Idempotent webhook handlers with ctx.idempotency.claim(): Third-party webhook providers (Stripe, Telegram, GitHub, Slack, Twilio, Discord) retry delivery on non-2xx responses with the same event id. Use ctx.idempotency.claim() to atomically dedupe — it returns true if you're the first to see this key, false if another invocation already claimed it. export async function handler(req, ctx) { const event = await req.json(); if (!(await ctx.idempotency.claim(event.id))) { // Already processed — ack the retry without re-doing work. return new Response('duplicate', { status: 200 }); } await processEvent(event); return new Response('ok', { status: 200 }); } Options: - scope: 'stripe' | 'telegram' | ... (default: 'default'). Namespace claims so keys from different providers can never collide. - ttlSeconds: mark the claim with an expiry. Cleanup is your responsibility: DELETE FROM _idempotency_keys WHERE expires_at < now(); Background work with ctx.waitUntil(): Use ctx.waitUntil(promise) to keep the function alive after the response is sent. This is useful for fire-and-forget tasks like sending emails or logging. Background work has a 30-second timeout. ctx.db is available inside waitUntil promises. export async function handler(req, ctx) { ctx.waitUntil(fetch("https://api.email.com/send", { method: "POST", body: "..." })); return new Response(JSON.stringify({ accepted: true }), { headers: { "Content-Type": "application/json" } }); } Example: export async function handler(req, ctx) { const data = { hello: "world" }; return new Response(JSON.stringify(data), { status: 200, headers: { "Content-Type": "application/json" } }); } Row-Level Security in Functions: Functions respect RLS policies based on how they're invoked: - Invoked with end-user JWT → butterbase_user role (RLS enforced) * ctx.db queries see only the user's data * ctx.user.id contains the authenticated user ID * Use case: User-facing operations - Invoked with platform API key → butterbase_service role (RLS bypassed) * ctx.db queries see all data * ctx.user is null * Use case: Admin operations, background jobs - Invoked by cron trigger → butterbase_service role (RLS bypassed) * ctx.db queries see all data * ctx.user is null * Use case: Scheduled tasks, cleanup jobs Trigger types: - http: Invoke via HTTP request (GET, POST, etc) - cron: Schedule periodic execution (e.g., "0 9 * * *" = daily at 9am) - websocket: Trigger on WebSocket event from client via realtime connection - s3_upload: Trigger on file upload [not yet implemented] - webhook: Receive webhooks from external services [not yet implemented] Common errors: - VALIDATION_INVALID_SCHEMA: Check code exports a handler function - RESOURCE_NOT_FOUND: App doesn't exist - Syntax error: Code must be valid TypeScript/JavaScript Idempotency: Safe to call multiple times (updates existing function with same name). Next steps: Use invoke_function to test, then manage_function (action: "get_logs") to debug.
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  • Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
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  • What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass `topic` (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).
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  • Deploys an app to a VM and exposes it at a public https://<name>.redu.cloud URL. The container is built ON the VM — no local Docker/podman needed. PREREQS — run check_deploy_prerequisites first: it auto-selects your network_id + keypair_name (and returns a recipe to mint a keypair if you have none). Pass those two ids here. PORT: pass the port the app actually listens on (plan_deploy detects it / Dockerfile EXPOSE) — redu health-probes that exact port, so a wrong/omitted port (defaults to 3000) fails a non-3000 app (e.g. a static nginx app listens on 80 → pass 80). TWO source modes: (1) GIT — pass `repo` (public; private repos also need git_token). (2) UPLOAD — call prepare_upload first to tar + POST your LOCAL working dir, then pass the returned `source_token` (no git, no PAT; use this for uncommitted code, a fixed clone of a repo you don't own, or private code). The source needs a Containerfile/Dockerfile; if it has none, pass dockerfile_content (the one plan_deploy generated) or include a Dockerfile in the uploaded tarball. To wire a Postgres DB, pass `database` (both auto-inject PGHOST/PGPORT/PGUSER/PGPASSWORD/PGDATABASE + DATABASE_URL — zero setup): `database:'single_vm'` puts Postgres ON the app VM (cheapest; data dies if the VM is replaced); `database:'managed'` provisions a SEPARATE managed-Postgres VM on the same private network and wires it automatically (data PERSISTS across redeploys; reused on a same-name redeploy) — you do NOT call create_database for this. Build+provision takes ~3-6 min (a bit longer for managed, which also brings up the DB VM); poll list_deployments or get_deployment until status='ready'. On 'build_failed'/'error', call get_deployment(id) to read build_log. ALWAYS run plan_deploy first and confirm the plan + cost with the user before deploying.
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