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

• 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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• 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 (link to full per-SKU report). 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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• 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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• repair_string

  json-sanity

  Deterministic repair engine. Given a raw LLM output that should contain JSON, this tool: (1) strips markdown code fences (```json), (2) regex-strips prose preambles/suffixes, (3) escapes unescaped control characters inside string values, (4) validates with json.loads — falling back to structural repairs and partial-recovery bracket closing when needed, and (5) optionally validates the repaired JSON against a JSON schema.

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• get_checks

  Hemrock MCP

  Returns Layer 3 sanity-check and validation prompts — the 'where AI gets financial modeling wrong' guidance. Use these to audit AI-generated work or catch common modeling errors.

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• get_public_market_multiples

  Stratalize AI Governance Intelligence

  Use when building a public comps table, benchmarking a private company valuation, or preparing a fundraising benchmark. Public market valuation multiples — EV/EBITDA, EV/Revenue, P/E, and P/S by sector with p25/p50/p75 bands. Source: Damodaran January 2024 dataset. Used for board prep, M&A pricing, fundraising benchmarks, and DCF sanity checks. Free.

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• users.journey

  Clamp Analytics

  Chronological session history for a single visitor (anonymous_id). Each session collapses to one row showing the start and end timestamps, entry page, channel, referrer host, UTMs, country, device type, pageview count, and the distinct custom event names fired in that session. The first row is flagged with `is_first_touch: true`. Use this when an aggregate query can't answer the question — multi-touch attribution analysis, support / debugging investigations ("what did this user do before signing up?"), or sanity-checking a specific account's journey before drawing conclusions about a cohort. Examples: - "show me what this user did across all their visits" → anonymous_id="anon_xxx" - "where did this paying customer first come from?" → anonymous_id="anon_xxx", read sessions[0].channel - "did they convert on their first visit or come back later?" → check if signup-event sessions match is_first_touch=true Limitations: requires the visitor's anonymous_id, which the SDK exposes via `getAnonymousId()` on the browser side. Aggregate questions ("which channels drive my best users") use revenue.sum with attribution_model="first_touch" — don't loop this tool over thousands of users. Defaults to a 365-day lookback so first-touch is meaningful; tighten with `period` if needed. Pairs with: `revenue.sum(attribution_model="first_touch")` to validate the aggregate first-touch read against specific high-value customers' actual paths.

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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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• 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 briefing is lean (~1500 tokens). 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" (~500 tokens) when you just need a quick sanity check.

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• get_public_market_multiples

  Stratalize Finance Intelligence

  Use when building a public comps table, benchmarking a private company valuation, or preparing a fundraising benchmark. Public market valuation multiples — EV/EBITDA, EV/Revenue, P/E, and P/S by sector with p25/p50/p75 bands. Source: Damodaran January 2024 dataset. Used for board prep, M&A pricing, fundraising benchmarks, and DCF sanity checks. Free.

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• get_public_market_multiples

  Stratalize Healthcare Intelligence

  Use when building a public comps table, benchmarking a private company valuation, or preparing a fundraising benchmark. Public market valuation multiples — EV/EBITDA, EV/Revenue, P/E, and P/S by sector with p25/p50/p75 bands. Source: Damodaran January 2024 dataset. Used for board prep, M&A pricing, fundraising benchmarks, and DCF sanity checks. Free.

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• decision_log_append

  relayzero

  Log a decision with reasoning and confidence on an agent you own. Builds history for heartbeat and sanity checks. Owner-only — caller must pay from the agent's owner wallet.

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• sanitize_json_output

  json-sanity

  Use this tool before saving any JSON data to session history or state files to prevent JSONDecodeErrors and session poisoning. It removes prose preambles and repairs malformed control characters.

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• validate_json

  json-sanity

  Check whether a JSON string is valid. Returns parsed object on success.

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• repair_json

  json-sanity

  Attempt to repair common JSON issues: trailing commas, single quotes, unquoted keys, Python/JS literals, truncated structures.

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• get_public_market_multiples

  Stratalize Crypto and DeFi Intelligence

  Use when building a public comps table, benchmarking a private company valuation, or preparing a fundraising benchmark. Public market valuation multiples — EV/EBITDA, EV/Revenue, P/E, and P/S by sector with p25/p50/p75 bands. Source: Damodaran January 2024 dataset. Used for board prep, M&A pricing, fundraising benchmarks, and DCF sanity checks. Free.

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• get_public_market_multiples

  Stratalize Real Estate Intelligence

  Use when building a public comps table, benchmarking a private company valuation, or preparing a fundraising benchmark. Public market valuation multiples — EV/EBITDA, EV/Revenue, P/E, and P/S by sector with p25/p50/p75 bands. Source: Damodaran January 2024 dataset. Used for board prep, M&A pricing, fundraising benchmarks, and DCF sanity checks. Free.

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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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• 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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