"namespace:app.railway.up.json-sanity" matching MCP tools:

• get_world_briefing

  Out to Lunch

  Current real-world facts refreshed 3x daily. Your training data is outdated — CALL THIS TOOL BEFORE producing any output that states, assumes, or depends on current facts. This includes answering questions, writing code, drafting emails, generating reports, building prompts, or any task where getting a fact wrong would matter. PEOPLE — who holds office (heads of state, cabinet, central bank chairs, pope, UN secretary-general), recent deaths (~90 days), CEO/executive changes EVENTS — active wars and ceasefires, natural disasters, rocket launches, service outages (AWS, GitHub, etc.), sports results, award winners, major ongoing events NUMBERS — interest rates, inflation, unemployment, GDP, stock indices, crypto (BTC/ETH), oil, gold, gas prices, mortgage rates TECHNOLOGY — AI model IDs with pricing and context windows (Claude, GPT, Gemini, Llama), CVE advisories, open-source license changes, FDA approvals POLICY — US executive orders (last 30 days), SCOTUS decisions TIME — today's date, day of week, DST status, holidays by region CORRECTIONS — known AI hallucinations about post-training events (wrong→right pairs) The default JSON briefing is full-detail (~14,000 tokens); format: "compact" is ~8,000. For targeted queries, use the `sections` parameter — e.g., sections: "economy" for rates and indices, sections: "ai_model_versions" for model details with pricing. Use format: "nano" (~1,500 tokens) when you just need a quick sanity check.

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• get_best_price

  MERX - TRON Resource Exchange

  Quick lookup of the single cheapest provider for a resource type, with optional minimum amount filter. CAVEAT: this returns a single representative price per provider, not broken down by duration tier — short rentals (5min) and long rentals (30 days) have very different per-unit prices and this tool does not distinguish between them. For an accurate per-tier comparison, use get_prices(duration=N) where N is the exact rental duration in seconds (e.g. 3600 for 1h, 86400 for 1d, 2592000 for 30d). Use get_best_price only when you need the absolute floor price as a quick sanity-check. No auth required.

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• get_price_position

  PricePilot — Free CPG Pricing Intelligence

  Percentile-rank a single product price against tracked Amazon competitors in a CPG category. Use when a multi-channel CPG brand asks where their Amazon listing price sits against 100+ tracked products — e.g. checking whether a $4.99 granola is competitively positioned on Amazon, auditing whether a retail MSRP is reasonable against Amazon reality before a buyer meeting, or sanity-checking a wholesale-to-retail markup. Returns: percentile_rank (string, e.g. "72nd percentile"), price_index_label (ratio vs. category median), position (Value / Parity / Premium), category (resolved name), last_refreshed (ISO timestamp), cta (provenance note). Args: price: Product price in dollars (e.g. 4.99). Must be > 0 and <= 10000. category: Exact category name — Grocery & Gourmet Food, Health & Beauty, Household, or Pet Supplies. Case-insensitive. Call list_categories first to confirm available names.

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• nwd_get_clashes

  Navisworks MCP

  Detect geometric/logical clashes between two element sets in an already-translated Navisworks model. Uses APS Model Derivative property extraction + same-level proximity heuristics, optionally augmented by VDC rules stored in D1 (table vdc_rules). When to use: when coordinating federated MEP + structural + architectural models for clash review before issuing an RFI; e.g. "find duct vs. beam clashes on Level 3 before the Wed coordination meeting" or "sanity-check the latest MEP revision against structure before releasing for fabrication." Pair with nwd_export_report to produce a deliverable. When NOT to use: do not call on a model whose translation is still "inprogress" — call nwd_export_report first and confirm translation_status == "success"; not a substitute for Navisworks Manage Clash Detective for final sign-off (this is a coordination-stage screen, not a regulatory clash report). APS scopes required: viewables:read data:read (read-only — does not create anything in APS). Rate limits: APS default ~50 req/min per endpoint; Model Derivative metadata/properties endpoints are the bottleneck. Properties response may return 202 "isProcessing" on first call — the worker retries once after 3s. For very large models (>50k elements) the worker caps analysis at 50x50 element cross-compare and 100 reported clashes; re-run with tighter category_a/category_b filters for exhaustive coverage. Errors: 401 APS token expired (transient, retry); 403 missing viewables:read/data:read scope (report, do not retry); 404 URN not found or not translated (prompt user to re-run nwd_upload); 409 not applicable; 422 model translated but property index unavailable — typically means source NW version unsupported or translation partially failed (supported: Navisworks 2015+); 429 rate limit (backoff); 5xx APS upstream (retry once). If properties.data.collection is empty the tool returns clash_count: 0 with a note rather than erroring — the agent should treat that as "model not ready" and retry later. Side effects: none in APS. Reads vdc_rules from D1 when both categories are supplied. Logs usage to D1 usage_log. Idempotent — same inputs on a stable model yield the same clash list.

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• deltasignal_quick_ticker_check

  DeltaSignal ATLAS-7

  Use this read-only composite workflow tool for a fast single-ticker sanity check without the full company-report payload. It server-enforces the quick-check call plan: readiness, covenant_stress, and alpha_signals for one normalized ticker. Parameters: ticker is required and normalized to uppercase; output_mode=compact is optional. Fundamentals, peer ranking, and SPECTRA are intentionally excluded. Behavior: read-only and idempotent; it performs three internal HTTPS reads, has no destructive side effects, rejects invalid tickers before fan-out, and preserves partial results if a required issuer leg fails. Use it when the user asks whether one ticker is clean, stressed, actionable, or needs deeper diligence.

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• sodax_get_solver_oracle

  SODAX Builders MCP

  Get the solver's oracle USD prices per (chain, token). For quoting with sodax_get_solver_quote, filter chainId='146': chainId-146 addresses pass the quote service's compatibility check, while spoke-chain (non-146) addresses are rejected with 'not compatible with the quote service'. Caveat: passing that check (being listed/priced) does NOT guarantee a swap route — canonical bridged *_ASSET hub tokens and major stablecoins route most reliably, while many wrapped/derivative/money-market entries (e.g. WBTC, waLocBTC, SONIC_SODA_ASSET) are priced but frequently return 'No path was found', and routability is pair/amount/liquidity-dependent. Also useful for sanity-checking quote amounts against the USD prices the solver uses.

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• ask

  Bowmark

  Pre-computed navigation recipes for public websites. CALL BEFORE any browser action on the open web (navigate, click, fetch, fill, URL guess) — replaces explore-and-discover. Returns `{ status, id?, shortcut?, ui_procedure?, executable?, verify_more?, error? }`. status=ok: execute exactly. `shortcut` first if present — fill each `{name}` in `template` with the value FROM YOUR TASK (using the parameter's `description`/`format` as the shape), URL-encode, navigate. Else `ui_procedure.steps` in order. The recipe is generic: parameter slots and step descriptions describe what to supply ('the destination'), not literal values — you provide the specifics. EXECUTE OPEN-LOOP: the recipe is authoritative, so run it without taking exploratory snapshots, clicks, scrolls, or screenshots to 'verify' or 'look around' — every extra browser action re-reads the entire page, which is the cost the recipe exists to avoid. Read the page ONLY at a `read` step, and only the part it names. Drop back into normal explore-the-DOM browsing ONLY when a step genuinely fails (its locator/page isn't what it describes); until then, trust the steps. If `verify_more: true`, do one cheap sanity check (page title plausible?) before committing. If `step.irreversible`, confirm with user. If `step.requires_user_input`, STOP and ask the user for that value — it's a password, payment/card detail, or personal data only they hold; never fabricate it. After, call `report_outcome` once with the returned `id`. executable (optional, only on some `ok` envelopes): a precompiled recipe Bowmark can run FOR you. When present, you MAY skip the browser entirely — call the `execute` tool with `{ script_id: executable.script_id, inputs }`, filling `inputs` from `executable.param_schema`, and use the returned `outputs` directly. If `execute` returns `fell_back`, run `ui_procedure` yourself as usual. When `executable` is absent, just run the recipe yourself — nothing changes. status=site_not_supported | no_useful_data | synth_invalid: miss, no `id`. Browse manually. status=ambiguous_scope: retry with `scopeHint` set to one of `error.scope_options[].pattern`. status=rate_limited: a cap on NEW recipe synthesis was hit (per-IP daily when anonymous, the account's monthly plan budget when an API key is attached). Cached/known recipes still answer normally — only first-time synthesis is capped. Do NOT retry-spam; back off (it resets at `error.retry_after` seconds) and browse manually until then. A free API key (bowmark.ai) lifts the anonymous per-IP cap to a plan budget. Skip for: localhost / 127.0.0.1 / *.local / RFC1918 (10., 192.168., 172.16-31.); open-ended search with no destination. On 503 `embedder_unavailable`/`synth_unavailable`, retry once after Retry-After.

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• run_backtest

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  A SMALL, BOUNDED, in-Worker sanity-check backtest — NOT a full-universe backtesting engine. Answers a quick question like 'does this factor actually work on these 5 names over the last year' inline, mid-conversation, without leaving MCP. Composes two existing tools (`get_pit_universe` + `get_pit_valuation_ratios`) across up to 10 tickers x 12 rebalance dates (120 cells): for each rebalance date, checks which requested tickers were in the survivorship-free PIT universe on that date (dropping — never erroring on — a ticker not yet listed or already delisted), then pulls each surviving ticker's point-in-time valuation multiples and computes the forward return to the NEXT rebalance date from the raw (unadjusted) close. Returns a flat {rebalance_date, ticker, factor_values, forward_return_pct} grid plus a small factor<->forward-return correlation per requested factor — a quick cross-sectional signal check, NOT a transaction-cost-aware portfolio simulation or a statistically validated backtest result. If the requested grid exceeds 120 cells, this tool does NOT silently truncate — it returns a `stream_fallback` response (signed Parquet download URLs, same shape as `get_compute_ready_stream`) and tells you to use those URLs. For a REAL full-universe, multi-date, survivorship-free backtest, use the Python SDK's AlphaEngine (`pip install valuein-sdk`) looped over `as_of` dates client-side — this tool is explicitly the small complement to that, not a replacement for it. Available on every plan; coverage follows your plan tier same as the two tools it composes.

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• tm_render_image

  Twinmotion MCP

  Render a still preview image of the model at a specified resolution by pulling the APS Model Derivative thumbnail (capped at 800x800 by the APS endpoint). Also resolves the camera_preset against model metadata to identify which 3D view it maps to, and applies any stored environment config from tm_set_environment for reference. When to use: when you need a quick visual sanity-check of an imported model (e.g. 'show me what Tower A looks like'), to preview a specific named view before committing to a full UE/Twinmotion render, or to embed a low-res preview in a chat/report. Pair with tm_list_scenes first to discover valid view names/GUIDs. When NOT to use: not for production-quality renders (APS thumbnails are low-res and raster-only; for cinematic output use Unreal Engine Movie Render Queue after FBX/USD export), not for arbitrary custom camera angles (only named views from the source file are resolvable — there is no runtime camera placement API here), not for 2D sheet exports (use tm_list_scenes to find 2D roles and fetch directly). APS scopes required: viewables:read data:read. Hits Model Derivative thumbnail + metadata endpoints only. Rate limits: APS default ~50 req/min per app per endpoint. Thumbnail endpoint is usually fast (<2s) once the model has translated; if called while status='inprogress' it returns no thumbnail. Do not loop-poll this tool — poll the manifest via tm_set_environment or tm_list_scenes instead. Errors: 401/403 = token/scope; 404 = URN not found or thumbnail not yet generated (model still translating — retry after manifest reports success); 409 = n/a; 422 = n/a; 429 = back off 30s; 5xx = APS upstream. Side effects: NONE (read-only on APS). Reads KV env_config_<urn>. Writes a row to usage_log. Idempotent.

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• asterwise_get_kp_ruling_planets

  Asterwise

  Computes KP ruling planets for the instantaneous chart at lat/lon with no birth data and returns day lord, Moon/Ascendant lord chains, and a deduplicated ruling_planets list. SECTION: WHAT THIS TOOL COVERS Current-moment KP snapshot: target_utc, day_lord, Moon and Ascendant tuples with sign/nakshatra/sub lords, ruling_planets[] unique names. Not natal positions (asterwise_get_kp_chart) and not house significators (asterwise_get_kp_significators). Coordinate sanity is upstream — not locally validated floats beyond whatever FastMCP passes. SECTION: WORKFLOW BEFORE: None — this tool is standalone. AFTER: asterwise_get_kp_chart — if natal confirmation is needed afterwards. SECTION: INPUT CONTRACT lat and lon only; no date parameter — "now" is implicit on the server clock. SECTION: OUTPUT CONTRACT data.ayanamsa (string — 'kp') data.target_utc (string — ISO UTC) data.day_lord (string — planet name) data.moon: longitude (float) rashi (string) sign_lord (string) nakshatra_lord (string) sub_lord (string) data.ascendant — same fields as data.moon data.ruling_planets[] (string array — unique names, deduplicated) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — lat/lon not range-checked locally. INVALID_PARAMS (upstream): — None — coordinate errors surface as MCP INTERNAL_ERROR at the tool layer. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Represents instantaneous sky — differs from natal stored charts. SECTION: DO NOT CONFUSE WITH asterwise_get_kp_chart — needs BirthData and returns full natal KP cusps. asterwise_get_prashna_chart — horary keyword workflow, not ruling-planet snapshot.

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• asterwise_get_numerology_compatibility

  Asterwise

  Compares two people on Pythagorean Life Path numbers derived from their names and birth dates and returns a score, tier label, narrative, strengths, challenges, and advice. SECTION: WHAT THIS TOOL COVERS Pairwise numerology only — no charts, rashis, or kootas. Outputs discrete compatibility_score 1..10 with textual bands. It does not run asterwise_get_compatibility (Jyotish matchmaking) or regional porutham tools. SECTION: WORKFLOW BEFORE: RECOMMENDED — asterwise_get_numerology_profile per person — sanity-check Life Paths before comparing. AFTER: None. SECTION: INPUT CONTRACT Four strings (two names, two dates) are passed through without local guards. SECTION: OUTPUT CONTRACT data.life_path_1 (int) data.life_path_2 (int) data.compatibility_score (int — 1 through 10) data.compatibility_level (string — 'Excellent', 'Good', 'Average', or 'Challenging') data.interpretation (string) data.strengths[] (string array) data.challenges[] (string array) data.advice (string) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS MEDIUM_COMPUTE SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — all validation is upstream. INVALID_PARAMS (upstream): — None — upstream rejection surfaces as MCP INTERNAL_ERROR at the tool layer. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — For Vedic matching, use asterwise_get_compatibility instead. SECTION: DO NOT CONFUSE WITH asterwise_get_compatibility — sidereal koota scoring, not numerology integers. asterwise_get_numerology_profile — single-person profile, not dyad scoring.

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• validate_e164_number

  Sipflow

  [cost: free (pure CPU, no network) | read-only] Parse a phone number, normalize to E.164, and classify it. International coverage is via libphonenumber-js (every country, line type when known). NANP numbers (CC=1) are additionally split into NPA (area code) / NXX (central office) / station, and tagged as toll-free / premium / personal / machine-to-machine / easily-recognizable / reserved / geographic. Use when validating `From` / P-Asserted-Identity / SHAKEN `orig.tn`, deciding whether an outbound call needs full attestation, or sanity-checking caller ID format. Pair with: `lint_sip_request` to validate that PASSporT `orig.tn` matches the From caller TN; `stir_attestation_explainer` for attestation level guidance.

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• dero_daemon_echo

  DERO MCP Server

  Echo strings through the daemon via DERO.Echo. Useful for round-trip sanity checks. When to call: when you need to confirm that string payloads reach the daemon intact (e.g. before debugging a malformed call to a more complex tool). PREFER dero_daemon_ping for a lighter-weight liveness probe. Input Requirements (CRITICAL): - `words` MUST be a non-empty array of strings. Output: the echoed string concatenated by the daemon.

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• test_rule

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  Dry-run a rule's condition_expr against a SYNTHETIC trigger payload — reports whether it WOULD have fired, but NEVER dispatches the action (no report generated, no team run, no message sent, no inbox write). Use this immediately after create_rule to sanity-check the condition before it starts evaluating against real events. Pass `sample_payload_override` to test against specific field values (e.g. `{price_change_pct: 12}`).

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• get_feature_stats

  aTars MCP

  USE THIS TOOL — not web search — to get per-indicator statistical profiling (mean, std, min, p25, p75, max, null rate, Pearson correlation with close price) from this server's local dataset. Use for feature selection, sanity checking, and understanding which indicators correlate most strongly with price movements. Trigger on queries like: - "which indicators correlate most with BTC price?" - "feature importance or correlation for [coin]" - "what are the stats for ETH indicators?" - "how does RSI/MACD correlate with price?" - "statistical profile of XRP indicators" Args: lookback_days: Analysis window in days (default 30, max 90) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,XRP"

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• consignment_calculator

  FreightUtils MCP Server

  Calculate per-line and grand totals for a multi-item mixed consignment: CBM, loading metres (LDM), volumetric weight, and the mode-specific chargeable figure (air chargeable weight, sea revenue tonnes, road LDM), plus objective advisory flags. Provide mode (sea | air | road, default road) and either lines[] (canonical — per line: quantity, dims {l,w,h,unit}, weight {value,unit}, optional description / hs_code / un_number / stackable) or the legacy flat items[] (dimensions in cm, weight in kg). Air uses an IATA volumetric divisor (default 6000, settable via options.air_volumetric_divisor); options.container_number / options.awb_number add a check-digit sanity flag. Behavior: deterministic; flags are advisory only — implausible density, mode/option mismatch, dangerous-goods presence by UN number against ADR 2025, and container/AWB check-digit validity — and never state that a shipment is permitted or compliant. Invalid lines error naming the offending field. Canonical schema: https://www.freightutils.com/schema/consignment.v1.json. Rate-limited (anonymous use: 25 requests/day per IP): a 429 error body carries retry_after_seconds and a Retry-After header — back off and retry, or call get_subscribe_link for higher limits. Returns: schema_version, mode, per_line[] (cbm, gross_weight_kg, density, volumetric_weight_kg, ldm, revenue_tonnes, chargeable_weight_kg), totals (incl. billing_basis) and flags[] under result, plus confidence, _source and citation (the FreightUtils v1 response envelope). Limitations: best-effort deterministic calculation and reference data — not regulatory, customs or dangerous-goods compliance advice; classification, documentation and carrier acceptance remain your responsibility. Related: cbm_calculator / chargeable_weight_calculator / ldm_calculator (single-figure versions), shipment_summary (adds vehicle/container suggestion and duty estimates), adr_lookup (what a flagged UN number is).

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• stix-feed-summary

  Jeevesus — DugganUSA Threat Intelligence MCP

  Live shape report on the DugganUSA STIX 2.1 threat feed for a chosen lookback window (1-7 days). Returns total indicator count, top malware families, top source feeds, type breakdown (ip/domain/url/hash/cidr), and top countries. Use this BEFORE pulling the full STIX bundle to gauge feed depth and freshness, plan SIEM ingestion budget, or sanity-check that a campaign you read about is actually in our corpus. Does NOT return the full bundle — for that, fetch `https://analytics.dugganusa.com/api/v1/stix-feed` with the same Bearer key. The bundle is STIX 2.1 / TAXII 2.1 with Splunk ES, OPNsense, Suricata, and Unbound DNS sinkhole plugins. Authentication required (Bearer token). Anonymous callers get a clear 401 with the registration URL. Example: `{"days": 7}` returns the last week's feed shape — useful for capacity planning and spot-checking recent ingest tags.

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• compare_analytical_vs_simulated

  QueueSim

  Run the same M/M/c configuration through BOTH the closed-form Erlang-C formula AND the discrete-event simulator, returning a side-by-side comparison with deltas. Use this when the user is validating QueueSim's engine against textbook values, learning queueing theory by watching simulation converge on the formula, or auditing a result that 'feels off' — agreement within ~5%% is the canonical sanity check for an M/M/c run. Pure-Exponential M/M/c only; the closed-form Erlang-C is undefined for other service distributions. Large deltas usually mean the simulation run was too short for steady-state — raise simulationDays. ANTI-FABRICATION: both sides come from real computation — closed-form is deterministic, simulation is stochastic but engine-backed. Quote both verbatim. Do not synthesize an 'average of the two' or recompute the formula from training-data recall.

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• analyze_weather

  epwforge-mcp

  Compute design conditions, HDD/CDD, monthly stats, and peak heating/cooling days for one or more EPW files. Accepts a `url` (existing EPW), `urls` (compare 2+), or `config` (synthesize on the fly with morphing/UHI/events/smoke). Config mode runs the full generation pipeline server-side but returns only stats — never the EPW content. **Token-saver: pass `compact: true` to get a ~10-field headline response (~100 tokens) instead of the full ~800-token payload.** Use compact for sanity-checking, dashboards, or when chaining many calls; full when you need monthly arrays / peak days / full ASHRAE. Optional `include_full_ashrae` adds ASHRAE 0.4/1/2% cooling + 99.6/99% heating design conditions with mean coincident dewpoint. Optional `include_improbability` (config mode) adds a stress-test score. Optional `include_idf` adds ready-to-paste EnergyPlus SizingPeriod:DesignDay objects. Optional `units` ('imperial' default | 'metric'). **Presentation: when calling with `urls` (multiple files), the response includes a `comparisons` array — render it as a markdown table to the user. Lead with the headline delta, not the raw data.** No auth required; no credits charged.

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• scaffold_local

  mcp

  OPTIONAL preflight: returns a podman-compose.yml + .env so the user can run the app (and a throwaway local Postgres) on THEIR machine before deploying to redu — to see it run / sanity-check the container. Requires local podman/podman-compose. It's a suggestion, not a gate — skip it and go straight to deploy_app any time. Honest caveat: the local Postgres is NOT the managed Postgres, so a green local run does not prove the prod DB wiring.

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