147,734 tools. Last updated 2026-05-27 15:15
"Arc" matching MCP tools:
- Calculates the tropical lunar phase for any date using Swiss Ephemeris. Returns the phase name, phase angle, illumination percentage, Moon age in days, and the next major phase transition. SECTION: WHAT THIS TOOL COVERS Eight-phase tropical lunar cycle: New Moon (0°), Waxing Crescent, First Quarter (90°), Waxing Gibbous, Full Moon (180°), Waning Gibbous, Last Quarter (270°), Waning Crescent. Phase angle is Sun–Moon elongation in degrees (0–360). Illumination percentage is derived from the phase angle using the cosine formula. Moon age is days since last New Moon. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_get_western_moon_calendar — get the full month's phase data. SECTION: INPUT CONTRACT date (optional string YYYY-MM-DD) — Date to compute phase for. Defaults to today. Example: '2026-05-01' SECTION: OUTPUT CONTRACT data.date (string — YYYY-MM-DD) data.phase_name (string — one of the eight canonical phase names) data.phase_angle (float — 0–360°, Sun–Moon elongation) data.illumination_pct (float — 0–100) data.moon_age_days (float — days since New Moon) data.moon_longitude (float — tropical ecliptic longitude) data.sun_longitude (float — tropical ecliptic longitude) data.is_waxing (bool — true from New to Full Moon) data.next_phase_name (string — next major phase) data.next_phase_date (string — approximate YYYY-MM-DD of next major phase) SECTION: RESPONSE FORMAT response_format=json — structured phase data. response_format=markdown — human-readable moon report. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local): None — date validated upstream. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_western_moon_calendar — full monthly day-by-day phase table. asterwise_get_panchanga — Vedic tithi system (lunar day based on 12° arc increments).Connector
- Get the complete profile of a single Chinese apparel supplier by ID. PREREQUISITE: You MUST first call search_suppliers or recommend_suppliers to obtain a valid supplier_id. Do not guess IDs. USE WHEN user asks: - "tell me more about [supplier]" / "show full details for sup_XXX" - "what certifications does this factory hold" - "what's their monthly capacity / worker count / equipment list" - "can [supplier] export to US / EU / Japan / Korea" - "give me the full profile / dossier / fact sheet for [supplier]" - "how verified is this supplier's data" (returns coverage_pct + 8 dimensions) - "what's their ownership type — own factory or broker" - "show payment terms / lead time / sample turnaround for sup_XXX" - "这家供应商具体情况 / 详细资料 / 工厂档案" - "[供应商] 的合规 / 认证 / 出口资质" Returns 60+ fields including: monthly capacity (lab-verified), equipment list, certifications (BSCI/OEKO-TEX/GRS/SA8000), ownership type (own factory vs subcontractor vs broker), market access (US/EU/JP/KR), chemical compliance (ZDHC/MRSL), traceability depth, and verified_dimensions breakdown showing exactly which of the 8 dimensions (basic_info, geo_location, production, compliance, market_access, export, financial, contact) have data. WORKFLOW: search_suppliers → pick supplier_id → get_supplier_detail → optionally get_supplier_fabrics (fabric catalog) OR check_compliance (market export readiness) OR find_alternatives (backup pool) OR compare_suppliers (side-by-side evaluation). RETURNS: { data: { supplier_id, company_name_cn/en, type, province, city, product_types, worker_count, certifications, compliance_status, quality_score, verified_dimensions: { verified_dims: "5/8", coverage_pct, dimensions: {...} } } } EXAMPLES: • User: "Show me the full profile for sup_001" → get_supplier_detail({ supplier_id: "sup_001" }) • User: "What certifications does Texhong hold and can they export to EU?" → get_supplier_detail({ supplier_id: "sup_texhong_042" }) — then inspect certifications + eu_market_ready; follow with check_compliance for formal verification • User: "我要看 sup_123 的完整档案" → get_supplier_detail({ supplier_id: "sup_123" }) ERRORS & SELF-CORRECTION: • "Supplier not found" → the supplier_id is invalid or outside free-tier access. Re-run search_suppliers to obtain a fresh valid ID. Do not guess sequential IDs. • Field returns null → that dimension is unverified for this supplier. Check verified_dimensions.coverage_pct before asserting data. If coverage_pct < 50, warn the user: "This supplier's record has limited verified data (X/8 dimensions). Consider find_alternatives for better-documented options." • "not available for public access" → this supplier is in the reserve pool (paid tier only). Use search_suppliers filters data_confidence=verified to stay in public tier. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this for multiple suppliers in a loop — use compare_suppliers with up to 10 IDs at once. Do not call to browse the database — use search_suppliers or get_province_distribution for discovery. NOTE: Source: MRC Data (meacheal.ai). Every numeric field shows both declared and lab-verified values where available. 中文:按 ID 获取单个供应商的完整档案(含维度覆盖率详情)。Connector
- Pro/Teams — N-shot CONSENSUS doctrine review of agentic code. ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. Long-running (~80-120s for N=3 parallel LLM calls); MCP clients often close the call before the server returns. Retrying re-runs N × 60-180s LLM calls from scratch and burns N× compute. RECOVERY: same heartbeat pattern as architect.validate — the run_id is emitted in the FIRST progress event at t=0s (before LLM children fire); on timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted consensus envelope. Runs N parallel `architect.validate` calls with private_session=True, then aggregates them to a per-principle MODE verdict + median severity + per-principle stability + score range/stdev. Returns one ConsensusValidationResponse with the headline median score, the honest variance band, and a representative full ValidationResponse (the child whose score is closest to the median). WHEN TO CALL: the user wants an HONEST first-pass score on agentic code, with the architect's variance surfaced. The single-shot `architect.validate` re-asserts the prior persisted run's verdict via baseline-anchor injection — same code can score 60/C anchored vs 98/A unanchored. Consensus mode is the unanchored honest read. WHEN NOT TO CALL: when you NEED the iteration delta against a prior run (regressions/improvements panel) — for that, call `architect.validate` which keeps baseline injection on. CHAIN RESUME: each child runs with `private_session=True` (no anchor) on purpose, but the CONSOLIDATED outer row IS persisted with `lifecycle_status='completed'` — the next single-shot `architect.validate` on the same repository auto-resolves it as prior_run_baseline. Consensus checkpoint becomes the new anchor. See the `architect-validation-orchestration` skill in the agent-asset pack for the full validate → consensus → certify sequence. BEHAVIOR: N (default 3, max 5) parallel LLM calls run concurrently; wallclock ~80-120s for N=3 (max child latency, not sum). Cost = N × LLM bill. Each child runs with private_session=True so the doctrine prompt's prior-run baseline injection is suppressed (no anchor bias). One CONSOLIDATED `UserValidationRun` row is written carrying the consensus envelope; the N children themselves do NOT persist (private_session contract). AUTH: Bearer <token>, Pro/Teams plan. Same paid-plan gate as architect.validate. INPUTS: same shape as architect.validate. `n` is the only extra arg (range 2..5). `private_session` is implicit (always true for children); the OUTER consolidated row IS persisted unless the tool itself is called inside another private context — but no such wrapper exists today. OUTPUT: response carries `score_consensus_median` (headline), `score_stdev` (honest uncertainty), `score_range` (min, max), `mode_stability_min_pct` (the cert-eligibility gate's input — ≥ 80% means the consensus is stable), `per_principle` (mode + distribution + severity median per principle), and `representative_response` (the closest-to-median child's full ValidationResponse so existing UI components render unchanged). TYPED FAILURES: same as architect.validate (timed_out, rate_limited, dependency_unavailable). Plus consensus-specific: `consensus_quorum_failed` when fewer than 2 child runs succeeded (≥ 2 required to compute a meaningful median).Connector
- Fetch N random trivia questions matching filters. Quality-first: by default excludes needs_review=true (use quality='all' to include for audit/research). USE WHEN: building a quiz, sampling content for warmup, generating practice sets. NOT WHEN: you need a specific question ID (use quizbase_question_by_id) or want to explore a topic deeply with facets (use quizbase_topic_by_slug). KEY FILTERS: - amount: 1-50, default 10. - lang: ISO 639-1. Default "en". Supported: en, pl. Strict — unknown language returns 400. - category (slug): e.g. geography, history, science-and-nature. Full list via quizbase_categories. - difficulty: trivial | easy | medium | hard | expert. LLM-calibrated. Records not yet LLM-rated hold the importer placeholder (mostly "medium" for factoid sources). - type: multiple | boolean (default both; no text_input in random). - regions (cultural affinity, AND): empty in data = no cultural advantage assumed. Lowercase ISO 3166-1 alpha-2 ('us', 'pl', 'gb') + cultural codes ('jewish', 'christian-catholic', 'islam'). Filter for content statistically more likely known by residents/members. Discover via quizbase_regions. - source: filter by source database (one of 12: opentdb, opentriviaqa, kqa-pro, entityq, mintaka, mkqa, nq-open, creak, qasc, arc, webq, quizbase). Use to exclude noisy auto-generated sources. - license (SPDX): CC-BY-SA-4.0 | CC-BY-SA-3.0 | MIT | etc. Restrict to redistribution-friendly content. - topic (curated slug): higher precision than tags. Alias resolver matches subcategories+tags. List via quizbase_topics. - topics_any: OR over curated topics, max 10. - tags (AND), tags_any (OR), subcategory: raw taxonomy. Use topic if available. - quality: 'high' (default, recommended) excludes needs_review=true. Use 'all' only for audit/research — when 'all', each question gains a "quality" field with value 'high' or 'needs_review' so you can tell which records were flagged. - exclude (UUIDs, max 250): de-dupe within a quiz session. OUTPUT: { questions: [...], meta: { count, language } }. Each question carries full per-record attribution (source, author, license, licenseVersion, licenseUrl, sourceId, url, modifications, lastModified) — identical shape to REST /api/v1/questions/random. ATTRIBUTION REQUIRED if you redistribute. CC-BY-SA modifications must be credited per § 3(a)(1)(B) using each question's own attribution object. COMMON MISTAKES: forcing lang='pl' for a global audience (use 'en' default); skipping quality (default already excludes flagged content — only pass quality='all' for audit); using tags when a curated topic exists (worse precision).Connector
- Computes all 10 Tajika Saham (sensitive points) for a Varshaphal solar return chart. Sahams are the Tajika equivalent of Arabic Parts — mathematically derived zodiac points that focus the annual horoscope on specific life themes. SECTION: WHAT THIS TOOL COVERS Saham formula: (A - B + Ascendant) % 360, with a conditional +30° correction applied when the Ascendant does not fall in the forward zodiacal arc from B to A. This conditional is the defining classical Tajika Saham rule — without it, results are wrong. Day and night formulas differ: the Minuend and Subtrahend swap based on whether the solar return falls during daytime or nighttime at the birth location. Punya Saham (Fortune) is always computed first because Yashas (Fame) and Mahatmya (Status) use it as an operand. The Saham lord (planet ruling the sign where the Saham falls) is the Sahamesha — its strength, house placement, and Tajika aspects to the Varsha Ascendant determine whether the theme manifests positively or is obstructed. 10 Sahams returned: punya — Fortune and Luck (Moon-Sun day / Sun-Moon night) vidya — Education and Learning (Sun-Jupiter day) yashas — Fame and Reputation (Jupiter-Punya day) — uses Punya as operand mitra — Friends and Allies (Jupiter-Venus day) mahatmya — Greatness and Status (Punya-Mars day) — uses Punya as operand asha — Desires and Fulfillment (Saturn-Venus day) karmakarya — Action and Profession (Mars-Mercury day) vyapara — Business and Trade (Mars-Saturn day) vivaha — Marriage and Relationships (Venus-Saturn day) santapa — Sorrow and Stress (Saturn-Moon day) SECTION: WORKFLOW BEFORE: RECOMMENDED — asterwise_get_varshaphal — understand the base solar return chart (year lord, Muntha, Varsha Ascendant) before interpreting Saham lords. The Saham is meaningless without knowing which house it occupies from the Varsha Ascendant. AFTER: asterwise_get_varshaphal_harsha_bala — assess the Saham lord's positional happiness score to determine ease or difficulty of manifestation. SECTION: INPUT CONTRACT Same as asterwise_get_varshaphal — BirthData plus target_year. target_year (required int): The Gregorian calendar year of the solar return. Not age — the civil year (e.g. 2026). Feeding age instead of year silently produces the wrong return. time (required): Solar return Ascendant is time-sensitive. Accurate birth time is required for reliable Saham interpretation. SECTION: OUTPUT CONTRACT data.target_year (int — calendar year of the solar return) data.ayanamsa (string — ayanamsa system used, e.g. 'lahiri') data.solar_return_utc (string — ISO UTC timestamp of solar return moment) data.is_day_return (bool — true if solar return occurs between sunrise and sunset; determines which Saham formula variant is used) data.varshaphal_ascendant_longitude (float — Varsha Ascendant in degrees; all 10 Saham longitudes are computed relative to this) data.total (int — always 10) data.sahams[] — 10 objects in order [punya, vidya, yashas, mitra, mahatmya, asha, karmakarya, vyapara, vivaha, santapa]: slug (string — lowercase key, e.g. 'punya') name (string — full display name, e.g. 'Punya Saham') theme (string — life area, e.g. 'Fortune and Luck') longitude (float — Saham longitude in degrees 0–360) rashi_index (int — 0–11, 0=Mesha) rashi (string — Sanskrit sign name, e.g. 'Mesha') degree_in_sign (float — degrees within the sign) saham_lord (string — classical lord of the sign where Saham falls) formula_used (string — describes whether day or night formula was applied and which planets were operands, e.g. 'day: Moon - Sun + Asc') (string — methodology note) 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 SLOW_COMPUTE — internally runs the full solar return computation (binary-search Sun longitude + house computation) before deriving Sahams. SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — BirthData Pydantic only. INVALID_PARAMS (upstream): None — upstream rejection surfaces as MCP INTERNAL_ERROR. INTERNAL_ERROR: Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Day/night determination uses sunrise/sunset at the birth coordinates for the solar return date. Polar latitudes where sunrise cannot be computed → MCP INTERNAL_ERROR. — target_year is a Gregorian year, not age — always verify the caller passes the civil year. SECTION: DO NOT CONFUSE WITH asterwise_get_varshaphal — returns the full base solar return chart including Muntha, year lord, and planet positions; Saham points are not included there. asterwise_get_varshaphal_harsha_bala — scores planet positional happiness; this tool computes zodiac points, not planet positions. asterwise_get_gemstone_recommendations — birthchart gemstone recommendations, unrelated to Tajika Saham.Connector
- Computes Harsha Bala (positional happiness score) for all 7 classical planets in a Varshaphal solar return chart. Maximum 20 per planet (4 components × 5 points each). SECTION: WHAT THIS TOOL COVERS Harsha Bala is entirely distinct from Pancha Vargeeya Bala. Pancha Vargeeya Bala measures mathematical strength (sign dignity, exaltation arc, divisional chart fractions). Harsha Bala measures whether a planet is positionally comfortable — does it occupy the right house, sign, hemisphere, and return type for its nature? A planet with high Pancha Vargeeya Bala (60/80) but zero Harsha Bala has the capacity to deliver results, but does so through stress, delay, and frustration. A Year Lord (Varsha Pati) with 0 Harsha Bala signals a difficult year even when it wins the Pancha Adhikari election. 4 Components (5 points each, maximum 20): 1. STHANA — Happy house placement in the Varsha chart (measured from Varsha Ascendant, not natal Ascendant): Sun=9th, Moon=3rd, Mars=6th, Mercury=1st, Jupiter=11th, Venus=5th, Saturn=12th. 2. SWAKSHETRA/UCCHA — Planet in its own sign (Swakshetra) or sign of exaltation (Uccha). 3. PUM-STRI (Gender/Hemisphere) — Male planets (Sun, Mars, Jupiter) prefer houses 7–12 (visible hemisphere). Female planets (Moon, Venus, Saturn) prefer houses 1–6 (invisible hemisphere). Mercury earns this component unconditionally. 4. DINA-RATRI (Day/Night) — Male planets (Sun, Mars, Jupiter) prefer a daytime solar return. Female planets (Moon, Venus, Saturn) prefer a nighttime return. Mercury earns this component unconditionally. SECTION: WORKFLOW BEFORE: RECOMMENDED — asterwise_get_varshaphal — identify the Year Lord (Varsha Pati) before interpreting Harsha Bala. The Year Lord's Harsha Bala is the most actionable number in this response. AFTER: asterwise_get_varshaphal_saham — use Harsha Bala to assess whether each Saham lord can deliver its theme with ease or difficulty. SECTION: INPUT CONTRACT Same as asterwise_get_varshaphal — BirthData plus target_year. target_year (required int): The Gregorian civil year of the solar return. Not age. time (required): Solar return ascendant and house positions are time-sensitive. SECTION: OUTPUT CONTRACT data.target_year (int) data.ayanamsa (string) data.solar_return_utc (string — ISO UTC of solar return moment) data.is_day_return (bool — true if solar return falls between sunrise and sunset; directly determines Dina-Ratri component results for all planets) data.varshaphal_ascendant_longitude (float — Varsha Ascendant in degrees; all house placements are measured from this) data.planets[] — 7 objects (Sun, Moon, Mars, Mercury, Jupiter, Venus, Saturn in that order): planet (string — planet name) harsha_bala (int — total score 0–20) max_harsha_bala (int — always 20) varsha_house (int — planet's house in the Varsha chart, 1–12, from Varsha Ascendant) rashi_index (int — 0–11, 0=Mesha) rashi (string — Sanskrit sign name) components{} — four keys: sthana{}: earned (bool), points (int — 0 or 5), happy_house (int), actual_house (int), description (string) swakshetra_uccha{}: earned (bool), points (int — 0 or 5), in_own_sign (bool), in_exaltation (bool), current_rashi_index (int) pum_stri{}: earned (bool), points (int — 0 or 5), gender (string — 'male'|'female'|'neutral'), happy_hemisphere (string), actual_house (int) dina_ratri{}: earned (bool), points (int — 0 or 5), happy_period (string), actual_period (string — 'day' or 'night') interpretation (string — qualitative label: 'Excellent', 'Strong', 'Moderate', 'Weak', or 'Very weak') (string — methodology and interpretation guidance) 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 SLOW_COMPUTE — internally runs the full solar return computation before deriving Harsha Bala. SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — BirthData Pydantic only. INVALID_PARAMS (upstream): None — upstream rejection surfaces as MCP INTERNAL_ERROR. INTERNAL_ERROR: Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Rahu and Ketu are excluded — Harsha Bala is defined only for the 7 classical grahas. — Polar latitudes where sunrise/sunset cannot be computed affect is_day_return → MCP INTERNAL_ERROR. — target_year is a Gregorian year, not age. SECTION: DO NOT CONFUSE WITH asterwise_get_varshaphal — returns Pancha Vargeeya Bala (mathematical strength out of 80) for the Pancha Adhikaris; Harsha Bala (positional happiness out of 20) is a completely different measurement returned by this tool. asterwise_get_varshaphal_saham — derives sensitive zodiac points; this tool scores planet positional comfort. asterwise_get_chart_strength — Shadbala for the natal chart, not Tajika Harsha Bala for the solar return.Connector
Matching MCP Servers
- FlicenseAqualityCmaintenanceAn MCP server for the Arc browser that enables programmatic management of spaces and tabs. It supports actions like listing, creating, and deleting spaces and tabs, as well as focusing spaces and opening URLs via AppleScript.Last updated9254
- Flicense-qualityDmaintenanceEnables semantic browser automation for the Arc browser, allowing users to perform web tasks like searching, shopping, and data extraction through natural language commands.Last updated1
Matching MCP Connectors
Canonical vocabulary server for autonomous business design. Exposes the Arco Lexicon as seven MCP tools: term lookup, related terms, alignment verification, citation formatting, source retrieval, term listing, and term suggestion. No authentication required. Streamable HTTP transport.
53 AI tools for agents: web, crypto, AI generation, OCR, and more. Pay with Stripe or USDC.
- Pro/Teams — N-shot CONSENSUS doctrine review of agentic code. ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. Long-running (~80-120s for N=3 parallel LLM calls); MCP clients often close the call before the server returns. Retrying re-runs N × 60-180s LLM calls from scratch and burns N× compute. RECOVERY: same heartbeat pattern as architect.validate — the run_id is emitted in the FIRST progress event at t=0s (before LLM children fire); on timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted consensus envelope. Runs N parallel `architect.validate` calls with private_session=True, then aggregates them to a per-principle MODE verdict + median severity + per-principle stability + score range/stdev. Returns one ConsensusValidationResponse with the headline median score, the honest variance band, and a representative full ValidationResponse (the child whose score is closest to the median). WHEN TO CALL: the user wants an HONEST first-pass score on agentic code, with the architect's variance surfaced. The single-shot `architect.validate` re-asserts the prior persisted run's verdict via baseline-anchor injection — same code can score 60/C anchored vs 98/A unanchored. Consensus mode is the unanchored honest read. WHEN NOT TO CALL: when you NEED the iteration delta against a prior run (regressions/improvements panel) — for that, call `architect.validate` which keeps baseline injection on. CHAIN RESUME: each child runs with `private_session=True` (no anchor) on purpose, but the CONSOLIDATED outer row IS persisted with `lifecycle_status='completed'` — the next single-shot `architect.validate` on the same repository auto-resolves it as prior_run_baseline. Consensus checkpoint becomes the new anchor. See the `architect-validation-orchestration` skill in the agent-asset pack for the full validate → consensus → certify sequence. BEHAVIOR: N (default 3, max 5) parallel LLM calls run concurrently; wallclock ~80-120s for N=3 (max child latency, not sum). Cost = N × LLM bill. Each child runs with private_session=True so the doctrine prompt's prior-run baseline injection is suppressed (no anchor bias). One CONSOLIDATED `UserValidationRun` row is written carrying the consensus envelope; the N children themselves do NOT persist (private_session contract). AUTH: Bearer <token>, Pro/Teams plan. Same paid-plan gate as architect.validate. INPUTS: same shape as architect.validate. `n` is the only extra arg (range 2..5). `private_session` is implicit (always true for children); the OUTER consolidated row IS persisted unless the tool itself is called inside another private context — but no such wrapper exists today. OUTPUT: response carries `score_consensus_median` (headline), `score_stdev` (honest uncertainty), `score_range` (min, max), `mode_stability_min_pct` (the cert-eligibility gate's input — ≥ 80% means the consensus is stable), `per_principle` (mode + distribution + severity median per principle), and `representative_response` (the closest-to-median child's full ValidationResponse so existing UI components render unchanged). TYPED FAILURES: same as architect.validate (timed_out, rate_limited, dependency_unavailable). Plus consensus-specific: `consensus_quorum_failed` when fewer than 2 child runs succeeded (≥ 2 required to compute a meaningful median).Connector
- Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.Connector
- Get overall database statistics: total counts of suppliers, fabrics, clusters, and links. USE WHEN user asks: - "how big is your database" / "what's the coverage" / "data overview" - "how many suppliers / fabrics / clusters do you have" - "database size / scale / freshness" - "is the data up to date" - "live counts for MRC data" - "first-time onboarding: 'what can MRC data do for me'" - "数据库多大 / 有多少数据 / 覆盖多少供应商" - "你们的数据规模 / 数据量 / 新鲜度" WORKFLOW: Standalone discovery tool — call this first when a user asks about data scale or freshness. Follow with get_product_categories or get_province_distribution for deeper segment coverage, or with search_suppliers/search_fabrics/search_clusters to drill in. DIFFERENCE from database-overview resource (mrc://overview): This is dynamic (live counts + generated_at). The resource is static (geographic scope, top provinces, data standards). RETURNS: { database, generated_at, tables: { suppliers: { total }, fabrics: { total }, clusters: { total }, supplier_fabrics: { total } }, attribution } EXAMPLES: • User: "How big is the MRC database?" → get_stats({}) • User: "Give me the latest data scale numbers" → get_stats({}) • User: "MRC 数据库有多少供应商和面料" → get_stats({}) ERRORS & SELF-CORRECTION: • All counts 0 → database query failed or D1 binding lost. Retry once after 5 seconds. If still 0, surface a transport error to user. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this before every tool — only when user explicitly asks about scale. Do not call to get per-category counts — use get_product_categories. Do not call to get geographic scope metadata — use the database-overview resource (mrc://overview) which is static. NOTE: Only reports verified + partially_verified records. Unverified reserve data is excluded from counts. Source: MRC Data (meacheal.ai). 中文:获取数据库整体统计(供应商总数、面料总数、产业带总数、关联记录数)。动态快照,含生成时间戳。Connector
- [Core feature] Surface supplier specifications that deviate from independent lab measurements. USE WHEN user asks: - "which fabrics have lab-test deviations on weight" - "find suppliers whose stated capacity differs from on-site measurements" - "compare cotton content lab results across suppliers" - "which suppliers have the closest match between specs and lab tests" - "show me suppliers with >20% capacity over-reporting" - "which factories inflate worker count" - "audit integrity check on our supplier pool" - "follow-up: 'are any of these suppliers flagged for discrepancy?'" - "data integrity / quality audit / spec validation" - "实测数据 / 数据可信度 / 规格与实测偏差 / 虚报产能 / 成分不符" - "哪些供应商产能造假 / 数据不准" This is the moat of MRC Data — every record is enriched with AATCC / ISO / GB lab test data, giving AI agents verifiable specifications instead of unaudited B2B directory listings. Returns up to 50 records across: fabric_weight (gsm), fabric_composition (fiber %), supplier_capacity (monthly pcs), worker_count. Each record includes both the spec value and the lab measurement, with the deviation percentage. WORKFLOW: Standalone audit tool — does not require prior search. Call directly with field type and threshold. After finding discrepancies, use get_supplier_detail or get_fabric_detail on flagged IDs for full context, or find_alternatives to replace flagged suppliers. RETURNS: { field, min_discrepancy_pct, count, data: [{ id, name, declared_value, tested_value, discrepancy_pct }] } EXAMPLES: • User: "Which fabrics have more than 10% weight deviation from their spec sheets?" → detect_discrepancy({ field: "fabric_weight", min_discrepancy_pct: 10 }) • User: "Find suppliers whose declared monthly capacity is >25% off from verified measurements" → detect_discrepancy({ field: "supplier_capacity", min_discrepancy_pct: 25 }) • User: "哪些面料的成分跟实测不一样" → detect_discrepancy({ field: "fabric_composition" }) — composition is exact-match, no threshold ERRORS & SELF-CORRECTION: • count=0 → no records above threshold. Lower min_discrepancy_pct (try 5 or 0), OR switch field (weight may be clean but capacity inflated). • Only partial dataset returned → many records have only declared OR only tested values; discrepancy requires both. This is a data coverage limit, not a bug. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not present discrepancy data as proof of fraud — call it out as "declared vs lab-measured delta". Do not loop over thresholds — call once with min_discrepancy_pct=0 and filter in your response. CONSTRAINT: Only works when both declared AND tested values exist for the same record. Many records have only one or the other. Max 50 records per call. NOTE: Source: MRC Data (meacheal.ai). Methods: AATCC / ISO / GB per field. 中文:识别供应商规格与实测值偏差较大的记录。返回规格值、实测值、偏差百分比。Connector
- What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.Connector
- Cursor-paginated browse over the catalog. Quality-first: by default excludes needs_review=true (use quality='all' for full pool). USE WHEN: full catalog sync, delta sync (updated_since), exhaustive enumeration by filter. NOT WHEN: you only need N random samples (use quizbase_random) or a single record (use quizbase_question_by_id). PAGINATION: stable cursor over id UUIDv7 DESC. First call: omit cursor. Next: pass meta.nextCursor. Stop when nextCursor is null. KEY FILTERS (full parity with REST): - lang: ISO 639-1, default "en". Supported: en, pl. - category (slug), difficulty (trivial|easy|medium|hard|expert — LLM-calibrated), type (multiple|boolean), subcategory (raw slug). - tags (AND), tags_any (OR, max 10): raw tag slugs. - topic (curated, alias resolver), topics_any (OR over curated): higher precision than tags. - regions (cultural affinity, AND): empty = no cultural advantage assumed. Lowercase ISO 3166-1 alpha-2 ('us', 'pl', 'gb') + cultural codes ('jewish', 'christian-catholic', 'islam'). Filter for content statistically more likely known by residents/members. Discover via quizbase_regions. - source: one of 12 (opentdb, opentriviaqa, kqa-pro, entityq, mintaka, mkqa, nq-open, creak, qasc, arc, webq, quizbase). - license (SPDX): e.g. CC-BY-SA-4.0, MIT. - quality: 'high' (default) excludes needs_review=true; 'all' for full approved pool. When 'all', each question gains a "quality" field with value 'high' or 'needs_review'. - updated_since (ISO 8601): only questions updated after this — for delta sync caches. PAGINATION + COUNTING: - cursor (string): from previous meta.nextCursor. Omit for page 1. - limit (1-100, default 20). - count: estimate (default, EXPLAIN-based ~5-20ms, ±5-50%) | none (skip). OUTPUT: { questions: [...], meta: { count, countMode, language, nextCursor, totalEstimate? } }. Each question carries full per-record attribution (source, author, license, licenseVersion, licenseUrl, sourceId, url, modifications, lastModified) — identical shape to REST /api/v1/questions. ATTRIBUTION REQUIRED if you redistribute. Credit each question using its own attribution object — see license + licenseUrl + modifications fields per record. COMMON MISTAKES: not passing the cursor on subsequent calls (you'll re-read page 1); polling without updated_since when doing delta sync.Connector
- Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets. TWO MODES: (1) `event` — pass a single Polymarket event slug; walks that event's child markets and checks ordering within it. (2) `topic` — pass a topic / seed question (e.g. "Strait of Hormuz traffic returns to normal"); the tool searches across separate events for related markets, groups them, then checks monotonicity. Cross-event mode catches the cases where Polymarket lists each cutoff as its own event ("…by May 31" is event A, "…by Jun 30" is event B — single-event mode misses the May≤June rule). Returns ranked opportunities with suggested trade direction + reasoning.Connector
- Detailed per-record view of email sources for a domain with flexible grouping and filtering. Grouping (group_by, default: "isp"): • "isp" — by ISP/provider (shows ISP, hostname, brand domain, country). Best starting point for investigation. • "ip" — by sending IP address (shows IP, ISP, PTR, country, source type) • "host" — by hostname (ip_domain_name) • "reporter" — by DMARC report sender (shows reporter organization) Note: with group_by=isp, the same provider may appear multiple times with different countries — this is correct (one row per provider+country combination). Each row includes: message count, disposition, policy override, SPF/DKIM/DMARC evaluation, SPF auth details (return-path, result, scope), DKIM auth details (domain, selector, result). The "comment" field comes from the DMARC XML report and is populated when ARC (Authenticated Received Chain) overrides the DMARC policy — e.g. when a forwarded message would fail DMARC but ARC trusts the forwarding chain, applying a different effective policy than the p= tag in the DMARC record. Empty when no override occurred. Optional filters: source_ip, isp, ip_domain_name, eval_spf, eval_dkim, eval_dmarc, source_type, disposition, dkim_domain, dkim_selector, spf_domain. For ISP grouping set problems_only=true to see only rows with authentication failures. Use this to investigate specific sending sources, drill down into authentication failures, or analyze traffic by provider/IP/reporter.Connector
- Pro/Teams — N-shot CONSENSUS doctrine review of agentic code. ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. Long-running (~80-120s for N=3 parallel LLM calls); MCP clients often close the call before the server returns. Retrying re-runs N × 60-180s LLM calls from scratch and burns N× compute. RECOVERY: same heartbeat pattern as architect.validate — the run_id is emitted in the FIRST progress event at t=0s (before LLM children fire); on timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted consensus envelope. Runs N parallel `architect.validate` calls with private_session=True, then aggregates them to a per-principle MODE verdict + median severity + per-principle stability + score range/stdev. Returns one ConsensusValidationResponse with the headline median score, the honest variance band, and a representative full ValidationResponse (the child whose score is closest to the median). WHEN TO CALL: the user wants an HONEST first-pass score on agentic code, with the architect's variance surfaced. The single-shot `architect.validate` re-asserts the prior persisted run's verdict via baseline-anchor injection — same code can score 60/C anchored vs 98/A unanchored. Consensus mode is the unanchored honest read. WHEN NOT TO CALL: when you NEED the iteration delta against a prior run (regressions/improvements panel) — for that, call `architect.validate` which keeps baseline injection on. CHAIN RESUME: each child runs with `private_session=True` (no anchor) on purpose, but the CONSOLIDATED outer row IS persisted with `lifecycle_status='completed'` — the next single-shot `architect.validate` on the same repository auto-resolves it as prior_run_baseline. Consensus checkpoint becomes the new anchor. See the `architect-validation-orchestration` skill in the agent-asset pack for the full validate → consensus → certify sequence. BEHAVIOR: N (default 3, max 5) parallel LLM calls run concurrently; wallclock ~80-120s for N=3 (max child latency, not sum). Cost = N × LLM bill. Each child runs with private_session=True so the doctrine prompt's prior-run baseline injection is suppressed (no anchor bias). One CONSOLIDATED `UserValidationRun` row is written carrying the consensus envelope; the N children themselves do NOT persist (private_session contract). AUTH: Bearer <token>, Pro/Teams plan. Same paid-plan gate as architect.validate. INPUTS: same shape as architect.validate. `n` is the only extra arg (range 2..5). `private_session` is implicit (always true for children); the OUTER consolidated row IS persisted unless the tool itself is called inside another private context — but no such wrapper exists today. OUTPUT: response carries `score_consensus_median` (headline), `score_stdev` (honest uncertainty), `score_range` (min, max), `mode_stability_min_pct` (the cert-eligibility gate's input — ≥ 80% means the consensus is stable), `per_principle` (mode + distribution + severity median per principle), and `representative_response` (the closest-to-median child's full ValidationResponse so existing UI components render unchanged). TYPED FAILURES: same as architect.validate (timed_out, rate_limited, dependency_unavailable). Plus consensus-specific: `consensus_quorum_failed` when fewer than 2 child runs succeeded (≥ 2 required to compute a meaningful median).Connector
- Search Chinese apparel industrial clusters and textile markets. USE WHEN user asks: - "where is China's [denim / suit / women's wear / underwear] manufacturing concentrated" - "what is the largest [silk / cashmere / down jacket] industrial cluster in China" - "industrial cluster comparison Humen vs Shaoxing vs Haining vs Zhili" - "recommend an industrial cluster for sourcing [product]" - "where should I set up a sourcing office for [category]" - "list mega clusters for [category]" - "fabric markets in Zhejiang / Jiangsu" - "accessories / trim / zipper / button markets in China" - "which province dominates [category] exports" - "follow-up: 'tell me more about Humen's cluster scale'" - "服装产业带 / 面料市场 / 产业集群 / 纺织集群 / 辅料市场" - "做 [品类] 应该去哪个产业带 / 集群推荐" Famous clusters this database covers include: Humen (Guangdong, womenswear), Shaoxing Keqiao (Zhejiang, fabric mega-market), Haining (Zhejiang, leather), Zhili (Zhejiang, children's wear), Shengze (Jiangsu, silk), Shantou (Guangdong, underwear), Puning (Guangdong, jeans), Jinjiang (Fujian, sportswear), and more. Returns paginated cluster list with name, location, specialization, scale, supplier count, average rent and labor cost, and key advantages/risks. WORKFLOW: Cluster discovery entry point. search_clusters → compare_clusters (side-by-side up to 10 cluster_ids) OR get_cluster_suppliers (list factories in that cluster) OR analyze_market (broader market view). RETURNS: { has_more: boolean, data: [{ cluster_id, name_cn, name_en, type, province, city, specialization, scale, supplier_count, labor_cost_avg_rmb }] } EXAMPLES: • User: "Where are the biggest denim clusters in China?" → search_clusters({ specialization: "denim", scale: "mega" }) • User: "Show me fabric markets in Zhejiang" → search_clusters({ province: "Zhejiang", type: "fabric_market" }) • User: "童装产业带有哪些" → search_clusters({ specialization: "童装" }) ERRORS & SELF-CORRECTION: • Empty data array → try in order: (1) drop scale filter, (2) broaden specialization (e.g. "服装" instead of "牛仔"), (3) remove type, (4) remove province. • Specialization mismatch → both Chinese and English work. Synonyms: sportswear/运动服, womenswear/女装, underwear/内衣, denim/牛仔. • Rate limit 429 → wait 60 seconds; do not retry immediately. • Empty after 3 retries → tell user: "No clusters match [criteria]. Try broader specialization or removing filters." AVOID: Do not use this for specific factory search — use search_suppliers. Do not compare clusters by calling search_clusters twice — use compare_clusters with cluster_ids. NOTE: Source: MRC Data (meacheal.ai). 170+ clusters mapped across 31 provinces. 中文:搜索中国服装产业带和面料市场。Connector
- Fetch a live Solana DEX divergence trading signal from Soliris Arc — the agent-to-agent data market built on Arc (Circle's L1 blockchain). Each signal costs $0.001 USDC paid automatically on-chain via the x402 protocol. Signals identify real-time arbitrage spreads across Raydium, Orca, Jupiter, and Meteora. This is the agentic economy in action: your AI pays another AI for data, settled in under 1 second, no humans in the loop. Use demo=true to get a sample signal without payment. For live signals the API returns a 402 with payment details. Powered by Soliris (soliris.pro).Connector
- Look up the canonical/official identifier for a company or drug. Use when a user mentions a name and you need the CIK (for SEC), ticker (for stock data), RxCUI (for FDA), or LEI — the ID systems that other tools require as input. Examples: "Apple" → AAPL / CIK 0000320193, "Ozempic" → RxCUI 1991306 + ingredient + brand. Returns IDs plus pipeworx:// citation URIs. Use this BEFORE calling other tools that need official identifiers. Replaces 2–3 lookup calls.Connector
- Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet (crypto price / Fed rate / geopolitical / sports / corporate / drug approval / election / other), fans out to the right packs (e.g. crypto+fred+gdelt for a BTC bet, fred+bls for a Fed bet, gdelt+acled+comtrade for Strait of Hormuz), and returns an evidence packet plus a simple market-vs-model comparison so the caller can see where the implied probability disagrees with the data. Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?". This is the core demo product — agents that get bet-relevant context here convert better than ones that have to discover the packs themselves.Connector
- Get the complete lab-tested record of a single fabric by ID. PREREQUISITE: You MUST first call search_fabrics to obtain a valid fabric_id. Do not guess IDs. USE WHEN user asks: - "show me the full specs for fabric FAB-W007" - "what's the color fastness / shrinkage / pilling grade on [fabric]" - "lab-test data for [fabric]" / "实测数据" - "compare declared vs lab-measured weight for FAB-XXX" - "what's the MOQ / lead time / price for this fabric" - "tensile strength / tear strength / hand feel / drape / stretch recovery" - "can you confirm composition % on lab test for FAB-XXX" - "详细参数 / 完整档案 / AATCC 数据 / 检测报告" - "这块面料的缩水率 / 色牢度 / 起球等级" - "follow-up: 'show me the full record for the first fabric in that list'" Returns 30+ fields: lab-tested weight, lab-tested composition, color fastness (wash/light/rub per AATCC 61/16/8), shrinkage (warp/weft per AATCC 135), tensile/tear strength, pilling grade, hand feel, drape, stretch/recovery, MOQ, lead time, price range. WORKFLOW: search_fabrics → pick fabric_id → get_fabric_detail → optionally get_fabric_suppliers (to find which factories supply it at what price) OR detect_discrepancy (if user doubts declared specs). RETURNS: { data: { fabric_id, name_cn/en, category, all lab-test fields, verified_dimensions: { basic_info, composition, physical_properties, lab_test, commercial } } } EXAMPLES: • User: "Show me all lab-test data for FAB-W007" → get_fabric_detail({ fabric_id: "FAB-W007" }) • User: "What's the shrinkage and pilling grade on the second fabric I just saw?" → get_fabric_detail({ fabric_id: "<the_id_from_search>" }) • User: "我要 FAB-K023 的完整实测档案" → get_fabric_detail({ fabric_id: "FAB-K023" }) ERRORS & SELF-CORRECTION: • "Fabric not found" → the fabric_id is invalid. Re-run search_fabrics and use an ID from the fresh results. • Field returns null → that test wasn't performed on this fabric. Check verified_dimensions.lab_test to see what IS tested before asserting anything. • "not available" → unverified fabric in reserve pool. Filter search_fabrics for higher data_confidence. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call in a loop for multiple fabrics — if user wants to compare fabrics, present the search_fabrics summary list instead. Do not call to browse — use search_fabrics with filters. NOTE: Source: MRC Data (meacheal.ai). AATCC/ISO/GB methods cited per field. 中文:按 ID 获取单个面料的完整实测档案(含 AATCC/ISO/GB 检测指标)。Connector