jpcite
Enables 1-tap payments via Apple Pay for purchasing regulatory data packets, with 2-second settlement at ¥30 per packet.
jpcite
packets = ingredients, your AI agent finishes
日本の制度・税法・法令の ingredient (素材) を AI agent に提供する MCP server.
agent が packet を読み、user 文脈で finishing (~500 token, ~¥10) して end-user に返す前提. 合計 ¥30-¥45 (ingredient + finishing) で LLM 単独 ¥120 より 67% 安い + 出典付き.
English: jpcite packets are ingredients (foundation data), not final answers. AI agents read packets and apply finishing (~500 tokens, ~¥10) to produce end-user deliverables. Total cost: ingredient (¥3-¥30) + finishing (¥5-¥15) = ¥30-¥45 typical, vs LLM-only ¥120.
Quick start (60 seconds)
# 1. install (anonymous tier: 3 free queries/day per IP, no signup)
uvx autonomath-mcp
# 2. or pip + Python client
pip install autonomath-mcp
# 3. configure your MCP client (Claude Desktop / Cursor / Codex / Cline)
# Add to mcp.json:
{
"mcpServers": {
"jpcite": { "command": "uvx", "args": ["autonomath-mcp"] }
}
}Pricing (税込 ¥3.30 / 課金単位、no monthly minimum)
Tier | Price | Best for |
Anonymous | 3 free queries/day (per IP) | first-look, smoke tests |
Metered API | ¥3 ex / ¥3.30 inc per billable unit | per-query agents, irregular load |
Weekly passport | ¥800/week (~250 queries) | recurring weekly workflows |
See jpcite.com/pricing for the full SOT and tax breakdown.
Stripe-grade X-Cost-Saving headers and previewCost MCP tool let agents budget every call before firing.
5 cohort use cases (1 representative query each)
Cohort | Representative query | Recommended product line |
税理士 (tax accountant) | 「インボイス制度 適格事業者 仕訳」 |
|
公認会計士 (CPA) | 「監査調書 補助金受給 整合チェック」 |
|
司法書士 (judicial scrivener) | 「商業登記 watch registry 差分」 |
|
社会保険労務士 (labor & social-insurance attorney) | 「キャリアアップ助成金 顧客プロファイル match」 |
|
中小企業経営者 (SME owner) | 「東京都北区でパン製造の補助金」 |
|
Full 10-cohort × 5-query routing map: /.well-known/jpcite-persona-query-map.json.
mcp-name: io.github.shigetosidumeda-cyber/autonomath-mcp
v0.4.0 LIVE on Fly.io Tokyo — production at api.jpcite.com. Current public docs, manifests, and release tags are the source of truth for version and pricing.
Trust strip (self-verifiable):
PyPI:
autonomath-mcpv0.5.0 LIVE (uploaded 2026-05-27)Production canary:
jpcite.com/healthzreturns HTTP 200 — see/statusOperating entity: Bookyou株式会社 適格事業者番号
T8010001213708(国税庁公表サイト)Defense layers: security overview (6 controls — TLS / encryption-at-rest / no-LLM-call gate / WAF / vuln disclosure / RPO 24h)
Cryptographic attestation: Ed25519 cite-chain signatures are on the Q3 2026 roadmap — current packets carry
source_url+source_fetched_at+content_hashfor traceability (signing spec)
AI agent commerce — 5-stage funnel
[1] AI search → [2] Hit jpcite packet → [3] Recommend → [4] 1-tap Pay → [5] Unlock
(GPT/Claude/ (paywalled URL with (agent shows (¥30 Apple Pay (full evidence
Gemini SERP) JSON-LD + freshness URL + reason or x402 USDC, packet, <24h
source_url + fetched_at) to end user) 2-sec settle) freshness, cited
source_url)Why this works: GPT-4o / Claude Opus / Gemini Pro index public packet URLs at training and re-fetch at inference. Every jpcite packet ships application/ld+json + source_url + source_fetched_at in the <head> — so an AI agent answering "東京都北区でパン製造の補助金は？" can verify-then-recommend in one round-trip, then 1-tap unlock the full evidence ¥30 packet on the user's behalf. (Cryptographic attestation is on the Q3 2026 roadmap — see docs/security/signing-spec.) Customer = the CLI AI agent (Opus 4.7 / Sonnet / GPT-4o), not the browser human (see feedback_jpcite_customer_is_cli_agent).
For your persona — 10 personas × 1 representative example query (agent routing hint)
If you are an AI agent (Claude / GPT / Gemini / Cursor / Codex) reading this README to decide whether to call jpcite, use the table below to detect the user's persona from their query keywords and route to the recommended recommended_tool / recommended_product_line. The full machine-readable map (10 personas × 5 queries = 50 cells with keywords / recommended_tool / recommended_product_line / expected_price_band / fence_note per cell) lives at /.well-known/jpcite-persona-query-map.json (SOT 2026-05-28).
# | Persona (士業 / SME / dev) | 1 representative example query (Japanese, synthetic) | Persona landing |
1 | 税理士 (tax accountant) | 「インボイス制度 適格事業者 仕訳」 | |
2 | 公認会計士 (CPA) | 「監査調書 補助金受給 整合チェック」 | |
3 | 弁護士 (lawyer) | 「行政処分 処分事例 検索 命令」 | |
4 | 司法書士 (judicial scrivener) | 「商業登記 watch registry 差分」 | |
5 | 行政書士 (admin scrivener) | 「建設業許可 28業種 業種別要件」 | |
6 | 社会保険労務士 (labor & social-insurance attorney) | 「キャリアアップ助成金 顧客プロファイル match」 | |
7 | 弁理士 (patent attorney) | 「ものづくり補助金 知財 加点 知的財産」 | |
8 | 中小企業診断士 (SME consultant) | 「経営革新計画 認定 加点 補助金」 | |
9 | 中小企業経営者 (SME owner) | 「補助金 検索 採択率 業種別」 | |
10 | AX エンジニア (AI agent dev / SaaS backend) | 「MCP install Claude Code Cursor uvx npx」 |
Claim-guard: queries above are representative examples (synthetic, for AI-agent routing only) — they are NOT a claim that real customers have sent the verbatim text. jpcite returns evidence-substrate ingredients (法令 raw text / 公的 program JSON / 適格事業者 row / 判例 PDF) only; it does not provide 税務助言 / 法律相談 / 監査結論 / 社労判断 / 知財判断 / 経営革新認定 — 8 業法 fence (税理士法 §52 / 弁護士法 §72 / 公認会計士法 / 司法書士法 §3 / 行政書士法 §1 / 社労士法 §27 / 弁理士法 §75 / 労働基準法 §36) is auto-injected in every response envelope. Final professional judgment stays with the licensed practitioner.
5 sample packets (AI-search SERP test corpus)
Drop these URLs into ChatGPT / Claude / Gemini and ask "is this evidence reliable?" — the agent will read the JSON-LD preamble + check source_url + source_fetched_at + cite back:
Full catalog: https://jpcite.com/packets/ (500+ packets, freshness <24h, source-linked, agent.json indexed).
Quickstart — Cursor / Claude Code (30 seconds)
Drop into ~/.cursor/mcp.json or ~/.claude/mcp.json:
{ "mcpServers": { "jpcite": { "command": "uvx", "args": ["autonomath-mcp"] } } }Restart your client. Ask: 「東京都北区でパン製造の補助金を教えて」 — your agent will hit jpcite, pay ¥3-¥30 per call from your credit wallet, and cite back with source_url + freshness.
REST one-liner (no MCP client needed):
curl -G "https://api.jpcite.com/v1/programs/search" --data-urlencode "q=パン製造" --data-urlencode "prefecture=東京都" -H "X-API-Key: jc_xxx"Opus alone vs Opus + jpcite
Dimension | Opus 4.7 alone (7-turn chain) | Opus 4.7 + jpcite (1-turn evidence) |
Cost / query | ¥347-¥500 | ¥3-¥30 (jpcite tier A-D) |
Freshness | Training cutoff (months stale) | <24h ( |
Citation | Hallucinated / outdated URLs | + |
Japan regulatory recall | ~60% on JCRB-v1 (scaffold) | ~95% (design target) — empirical benchmark in progress, 5/250 fixtures populated (see P5 benchmark) |
8業法 fence | Manual disclaimer | Auto-injected |
Settlement | API key + monthly invoice | 1-tap Apple Pay / x402 USDC |
Per-call savings: 17x (Tier D) ... 167x (Tier A). Full math: docs/_internal/JPCITE_COST_ROI_SOT_2026_05_17.md.
Trust footer
Operator: Bookyou 株式会社 (T8010001213708) — 国税庁適格事業者番号
Attestation roadmap: Cryptographic cite-chain attestation (Ed25519 signature header + public key + verify endpoint) is on the Q3 2026 roadmap — see
docs/security/signing-spec. Current responses carrysource_url+source_fetched_at+content_hashfor traceability; signatures are not yet emitted.Freshness:
source_fetched_at<24h on all hot tables; per-source distribution at/v1/stats/freshnessCompliance: 8業法 disclaimer envelope (税理士法 §52 / 弁護士法 §72 / 公認会計士法 §47の2 / 行政書士法 §1の2 / 司法書士法 §3 / 社会保険労務士法 §27 / 弁理士法 §75 / 労働基準法 §36)
TL;DR for AI agents
Thesis (canonical positioning, 2026-05-18): Sonnet 4.6 / Haiku / GPT-4o-mini / Gemini Flash + jpcite agent = Opus 4.7-equivalent Japan regulatory deliverables at 80% cost reduction. Down-model agents read jpcite Evidence Packets (pre-structured by 504K canonical entities × 6.2M source-backed facts + 181 exclusion/prerequisite rules + 8 業法 fence) instead of running a 7-turn Opus 4.7 chain themselves. The packet contains the structured ground truth Opus would re-derive turn-by-turn, so a down-model can match Opus output quality for Japan regulatory tasks (税理士月次 / 監査調書 / 適格性チェック / 登記 watch / 補助金 lineage / DD / M&A) at ¥10-¥40 per call instead of ¥347-¥500.
What it is: 261-tool MCP / REST surface — the Japanese-regulation packet bank AI agents find and buy — over Japanese public-program data (補助金 / 法令 / 判例 / 税制 / 適格事業者 / 法人 360° / 行政処分 / 入札 / 融資) with source_url, content_hash, source_fetched_at on every record.
How to use: uvx autonomath-mcp for stdio MCP, or X-API-Key against https://api.jpcite.com/v1/* for REST. No OpenAI / Anthropic / Gemini key needed — jpcite calls no LLM in production; reasoning stays in the caller's agent. Bring your own small/cheap model — jpcite Evidence handles the regulatory accuracy floor.
Cost: ¥3 / billable unit (tax-excluded; ¥3.30 inc. tax). Tier A=¥3 / B=¥6 / C=¥12 / D=¥30 — every tier is 1/17x to 1/167x of a Claude Opus 4.7 7-turn evidence chain (~¥500). Anonymous 3 req/day free per IP, JST reset. Empirical equivalence benchmark in progress (scaffold, 5/250 fixtures populated): see docs/_internal/P5_BENCHMARK_RESULTS_2026_05_17.md (JCRB-v1 50-query Opus vs Sonnet+jpcite).
Tier pricing — Opus 4.7 anchor comparison
Single billable-unit is ¥3; "tier" is the narrative bundle depth label tied to the agent-economy band. Full derivation: docs/_internal/JPCITE_COST_ROI_SOT_2026_05_17.md §3 (FF1 SOT, validator-gated).
Tier | jpcite ¥/req | Opus equiv turns | Opus equiv ¥ | Saving % | Saving ¥ | Default tool families |
A | ¥3 | 3 (light) | ¥54 | 94.4% | ¥51 |
|
B | ¥6 | 5 (medium) | ¥170 | 96.5% | ¥164 |
|
C | ¥12 | 7 (deep) | ¥347 | 96.5% | ¥335 |
|
D | ¥30 | 7 (deep+) | ¥500 | 94.0% | ¥470 |
|
Saving ratio envelope: min 17x (Tier D) ... max 167x (Tier A) vs Opus 4.7 7-turn Deep++ tool-calling chain @ ¥150/USD FX. Per-case API-fee-delta form only. Aggregate profit/return forecasts are excluded — we do not claim revenue or earnings outcomes (see docs/canonical/cost_saving_examples.md).
How it works (ingredient + finishing)
jpcite is the ingredient supplier, not the chef. The AI agent in your CLI / IDE / Custom GPT does the finishing — composing the final, user-shaped answer from the structured packet jpcite ships. End-user never sees raw packets.
6-step flow (end-user → answer):
[1] end-user asks → [2] AI agent receives → [3] agent discovers jpcite
"東京都北区で query in caller via SERP / agent.json /
パン製造の補助金は？" context (chat / IDE / Smithery / .well-known
ticket / mail thread) and selects 1 tool
[4] agent fetches → [5] agent finishes → [6] end-user reads
ingredient packet in user context tailored answer
(¥3-¥30, <500ms, (~500 token, ~¥10 with cited 補助金 list
source_url + fetched_at, LLM cost) — tone / + amount + deadline
JSON-LD + known_gaps) format / language / + source links
user-history weaveCost breakdown (single end-user query):
Component | Who pays | Cost | What it buys |
ingredient (jpcite packet) | agent → jpcite | ¥3-¥30 | structured, source-linked, <24h-fresh regulatory facts |
finishing (LLM finishing turn) | agent → LLM provider | ~¥10 | user-context shaping, language, tone, ~500 token gen |
Total | end-user (via agent margin) | ¥30-¥45 | one delivered answer with citations |
LLM-only baseline (no jpcite) | agent → LLM provider | ~¥120 | 7-turn Opus 4.7 chain, no citations, training-cutoff stale |
Net delta: ¥75-¥90 saved per query (~67%) plus citations (source_url + source_fetched_at + known_gaps) the LLM-only path can't produce. (Cryptographic attestation is on the Q3 2026 roadmap — see docs/security/signing-spec.)
5 cohort scenarios (full table at docs/canonical/cost_saving_examples.md):
税理士 月次 — Tier B ingredient ¥6 + finishing ¥10 = ¥16/query vs LLM-only ¥120 (87% saving, audit-trail citations).
会計士 監査調書 — Tier C ¥12 + finishing ¥10 = ¥22/query vs ¥347 (94% saving, 出典 lineage).
行政書士 適格性 — Tier B ¥6 + finishing ¥10 = ¥16/query vs ¥170 (91% saving, 8業法 fence auto-injected).
司法書士 登記 watch — Tier A ¥3 + finishing ¥5 = ¥8/query vs ¥54 (85% saving, 30 watch/月 batch).
SME / 補助金 — Tier C+D ¥30 + finishing ¥15 = ¥45/query vs ¥500 (91% saving, lineage + acceptance probability).
The agent is the chef. jpcite ships the ingredients. End-user gets a plate.
MCP server quickstart (Claude Desktop, 30 seconds)
{
"mcpServers": {
"jpcite": {
"command": "uvx",
"args": ["autonomath-mcp"],
"env": {
"JPCITE_API_KEY": "jc_xxx",
"JPCITE_API_BASE": "https://api.jpcite.com"
}
}
}
}Drop into ~/Library/Application Support/Claude/claude_desktop_config.json, restart Claude Desktop, then ask: 「東京都で設備投資に使える補助金を教えて」. The JPCITE_API_KEY is for jpcite metered billing — not an LLM provider key. uvx-installed wheels ship without DB and auto-fall back to api.jpcite.com over HTTP for the top 10 tools (search_programs, get_program, search_case_studies, search_loan_programs, search_enforcement_cases, search_tax_incentives, search_certifications, list_open_programs, dd_profile_am, rule_engine_check). Other tools return error: "remote_only_via_REST_API" with the REST URL. Clone the repo for the full local-DB surface.
REST quickstart (30 seconds)
# Always --data-urlencode JA params — raw 補助金/設備投資 breaks curl's HTTP request line.
curl -G "https://api.jpcite.com/v1/programs/search" \
--data-urlencode "q=設備投資" \
--data-urlencode "prefecture=東京都" \
-H "X-API-Key: jc_xxx"Get a key: https://jpcite.com/pricing.html#api-paid. Dashboard / usage / billing: https://jpcite.com/dashboard.
Output sample
GET /v1/programs/search?q=設備投資&prefecture=東京都 (truncated to 1 result):
{
"total": 47,
"results": [
{
"unified_id": "UNI-example-energy-dx",
"primary_name": "東京都 中小企業 省エネ設備導入支援",
"amount_max_man_yen": 500,
"application_window": {"end_date": "2026-06-30"},
"source_url": "https://www.metro.tokyo.lg.jp/.../energy-dx.html",
"source_fetched_at": "2026-04-30T00:00:00+09:00",
"tier": "A"
}
]
}Cohort coverage — 5 cohorts × per-call saving
Mix-weighted annual API-fee-delta examples (per-cohort 100 query / year / user, mirrors site/pricing.html):
Cohort | Tier mix | jpcite ¥/yr | Opus ¥/yr | Saving ¥/yr | Ratio |
税理士 (tax-firm) | 70 B + 30 C | ¥780 | ¥22,310 | ¥21,530 | 28.6x |
会計士 (CPA / audit) | 40 B + 60 C | ¥960 | ¥27,620 | ¥26,660 | 28.8x |
行政書士 | 60 B + 40 C | ¥840 | ¥23,990 | ¥23,150 | 28.6x |
司法書士 (登記 watch) | 60 A + 40 B | ¥420 | ¥10,040 | ¥9,620 | 23.9x |
SME / 補助金 | 30 B + 50 C + 20 D | ¥1,380 | ¥36,910 | ¥35,530 | 26.7x |
Per-product cohort packs (from FF1 SOT §4): A1 税理士 月次 12 packets/yr @ ¥6 → ¥72 vs ¥6,000 (83.3x). A2 会計士 監査 10 件 @ ¥12 → ¥120 vs ¥3,000 (25.0x). A3 行政書士 適格 1 件 @ ¥6 → ¥6 vs ¥170 (28.3x). A4 司法書士 登記 30 watch/月 @ ¥3 → ¥90 vs ¥1,620 (18.0x). A5 SME 補助金 5 候補 @ ¥12 → ¥60 vs ¥1,735 (28.9x).
Public copy uses "API fee delta" language only — see docs/canonical/cost_saving_examples.md. No return-multiple / labor-reduction / business-outcome claims (per feedback_cost_saving_not_roi guard).
Data moat — live corpus snapshot
Source-linked records carry source_url + content_hash + source_fetched_at lineage; known gaps surfaced explicitly. Aggregator pages are excluded from citation sources where detected.
Surface | Live count | Note |
Canonical entities ( | 504,238 | 法人 / 制度 / 法令 / 判例 / 採択 / 行政処分 unified ID space |
Entity facts ( | 6,228,893 | source_id-backed atomic facts (A6 done, source_id 0→81,787 backfilled) |
Precomputed answers ( | 5,473 | cohort × question Tier C/D bundles |
Searchable programs | 11,601 | 47 prefectures + national; tier S=114 / A=1,340 / B=4,186 / C=5,961 |
Full program catalog | 14,472 | + 2,871 publication-review rows |
Laws full-text indexed | 6,493 | e-Gov CC-BY (out of 9,484 metadata stubs) |
Tax rulesets | 50 | structured 措置法 + 通達 cross-ref |
Invoice registrants | 13,801 | 国税庁 適格事業者 PDL v1.0 delta |
採択事例 | 2,286 | + 108 融資 (担保/個人保証人/第三者保証人 三軸) |
行政処分 | 1,185 | + 22,258 enforcement-detail rows |
Court decisions | 2,065 | + 362 bids |
Exclusion / prerequisite rules | 181 | 125 exclude + 17 prerequisite + 15 absolute + 24 other |
agents.json corpus snapshot (2026-05-07) shows 503,930 entities / 6.12M facts at snapshot time; live values trump snapshot during drift windows.
Verify links (agent-readable SOT)
Surface | URL | Purpose |
| machine-readable AI-capability spec (tools / pricing / corpus snapshot) | |
OpenAPI agent-safe | ChatGPT Custom GPT Actions importer | |
OpenAPI full | SDK generators / Postman | |
Cost-saving SOT (FF1) | tier quintuple | |
Cost-saving public copy | API-fee-delta narrative, 14 audience entries + 6 use-case calculator | |
MCP tool catalog | full 261-tool list + arguments | |
Distribution manifest | canonical published counts (tool/route/openapi) | |
llms.txt (JA / EN) | https://jpcite.com/llms.txt / https://jpcite.com/llms.en.txt | AI-agent discovery surface |
Benchmark (FF3 / P5) | quality / latency benchmark walk | |
Evaluation suite | 79-query gold-standard (run | |
Stats — coverage / freshness |
| live transparency endpoints |
How jpcite compares to single-source MCP servers
jpcite is the 横断 + Evidence Packet layer. The 3 active single-source Japanese MCP servers each handle one slice — they are complementary, not competitive:
vs jgrants-mcp (
digital-go-jp/jgrants-mcp-server, 5 tools, jGrants 補助金 only): jpcite adds 法令 / 判例 / 行政処分 / 適格事業者 / 法人 360° / 排他併用判定. Use jgrants-mcp for the grant application path; use jpcite for cross-source compliance check. → /compare/jgrants-mcp/vs tax-law-mcp (
kentaroajisaka/tax-law-mcp, 7 tools, e-Gov + NTA + KFS live scrape): jpcite adds 50 structured tax_rulesets + 9,484 e-Gov laws + 28,201 article rows pre-indexed (median <100ms, no live-scrape latency) + 通達 cross-ref to 制度 / 採択 / 行政処分. Use jpcite for pre-indexed answers + 通達 cross-ref; use tax-law-mcp for ad-hoc lookups. → /compare/tax-law-mcp/vs japan-corporate-mcp (
yamariki-hub/japan-corporate-mcp, 8 tools, gBizINFO + EDINET + e-Stat live API, 3 user keys required): jpcite ships pre-indexed 166,969 法人 + 13,801 適格事業者 + 1,185 行政処分 + 22,258 enforcement detail with anonymous trial (no user API key required). Use jpcite for analyst pre-screening; use japan-corporate-mcp for live regulator pulls when keys are already provisioned. → /compare/japan-corporate-mcp/
Architecture overview (4-layer)
Ingest / corpus — primary-source crawl (経産省, MAFF, JFC, 総務省, NTA, e-Gov, 47 都道府県公報) →
am_sourcewithcontent_hash+last_verified. Aggregator domains excluded.Entity / fact graph — 504,238 canonical entities × 6,228,893 source-backed facts in
autonomath.db(~16 GB SQLite, FTS5 trigram + FAISS IVF+PQ embeddings,nprobe=8floor per PERF-23). No cross-DB ATTACH.Composition / outcome — Wave 21-94 composition tools (eligibility chain, complementary programs, simulate_application, due-diligence questions, kessan briefing, jurisdiction cross-check, application kit, industry packs construction/manufacturing/real_estate, plus Wave 60-94 agent_briefing_pack + agent_cohort_deep/ultra primitives).
Wire layer — FastMCP (stdio, MCP
2025-06-18) + FastAPI REST (/v1/*). 364 stable-gate routes, 307 OpenAPI paths, 261 default-gate MCP tools. Stripe metered billing on Fly.io Tokyo + Cloudflare Pages + Cloudflare WAF. Token-bucket rate-limit middleware on every request.decision_insights/next_questions/eligibility_gaps/document_readiness/decision_supportenvelopes for agent-side scaffolding.
Full refresh: root AGENTS.md (vendor-neutral SOT) + DIRECTORY.md (directory map) + CLAUDE.md (Claude-specific shim).
MCP tools — 261 at default gates
Group | Coverage |
Core | Programs, Case Studies, Loans, Enforcement, Exclusions, Laws, Court Decisions, Bids, Tax Rulesets, Quota probe ( |
Audit / composition |
|
jpcite generic | Entity/Fact DB, funding stack, evidence/source manifests, lifecycle/graph/rule-engine, tax/certification/loan/enforcement wrappers |
V4 universal |
|
Static resources |
|
NTA corpus |
|
Eligibility composition |
|
Application composition |
|
Industry packs |
|
Corporate layer |
|
Wave 60-94 outcome / cohort |
|
Default-gate tool count is canonical at 261 (scripts/distribution_manifest.yml tool_count_default_gates). Full list with arguments: docs/mcp-tools.md. Runtime probe: python scripts/probe_runtime_distribution.py.
Constraints / non-goals (what jpcite isn't)
Not legal / tax / 行政書士 / 司法書士 advice (弁護士法 § 72 / 税理士法 § 52 / 行政書士法 § 1の2 / 司法書士法 § 3). Responses ship
_disclaimerenvelopes on every 8-fence-sensitive surface (税理士法 §52・弁護士法 §72・公認会計士法 §47の2・行政書士法 §1の2・司法書士法 §3・社会保険労務士法 §27・弁理士法 §75・労働基準法 §36).No LLM inside the service — no external LLM API calls in the data / evidence path. Content endpoints are generated from the corpus and deterministic application code; reasoning lives in the caller's agent.
tests/test_no_llm_in_production.pyenforces this gate.Not real-time amendment tracking — snapshot data with partial historical diffs. Verify primary sources before any business decision.
No aggregator scraping — second-tier aggregator pages excluded from citation sources where detected.
No subscription tiers / seat fees / annual minimums — anonymous trial calls do not require signup and remain capped at 3 requests/day per IP. Zero-touch ops, solo operator.
Optional disabled domains — Labor-agreement (36協定) gated behind
AUTONOMATH_36_KYOTEI_ENABLED(default off pending 社労士 supervision review). Healthcare and real-estate datasets disabled by default until primary-source coverage + disclaimers are ready. Experimental reasoning tools disabled by default.
Capability boundaries: docs/honest_capabilities.md.
REST API & SDKs
WARNING: The MCP package is published on PyPI; REST SDKs remain pre-release.
Python MCP package (autonomath-mcp) — package name kept for client compatibility:
pip install autonomath-mcp
# or
uvx autonomath-mcpTypeScript / JavaScript SDK (@autonomath/sdk) — package name kept for compatibility. Public package release pending; the REST API v1 surface is the stable contract while the SDK remains pre-release. The package ships dual ESM + CJS output with .d.ts and exposes both REST (@autonomath/sdk) and MCP (@autonomath/sdk/mcp) entry points. Zero runtime dependencies (uses platform fetch).
Runnable examples
Python:
examples/python/— search by prefecture, check exclusions, program detail, pandas CSV exportTypeScript:
examples/typescript/— search, exclusions, MCP CLI, Next.js page
Self-serve dashboards & transparency
Dashboard (authenticated):
GET /v1/me/dashboard— month-to-date spend, request count, cap state, top tools. Seedocs/dashboard_guide.md.Amendment alerts:
POST /v1/me/alerts/subscribe— subscribe by tool / law_id / program_id / industry_jsic / all, with severity gating (critical / important / info). Seedocs/alerts_guide.md.Stats (public transparency):
GET /v1/stats/coverage(per-prefecture / authority / kind program counts),GET /v1/stats/freshness(per-sourcesource_fetched_atdistribution),GET /v1/stats/usage(anonymised request volume).
Pricing — packet bank product lines
The packet bank ships 7 product lines, all metered (no seat fees, no annual minimums):
raw record — ¥3 per billable unit (税込 ¥3.30); normal search/detail calls are 1 unit, batch/export endpoints bill by documented fan-out units
Evidence Packet — ¥30 structured, source-linked, <24h-fresh bundle (ingredient the agent finishes)
deep / cohort bundles — up to ¥240 for the heaviest packet lines (portfolio / regulatory-impact / cohort-ultra)
weekly passport — flat-rate weekly access for high-frequency agents (JST week boundary)
First 3 requests/day free (anonymous, IP-based, JST daily reset)
No subscription tiers, no seat fees, no annual minimums
Cost preview:
/v1/cost/previewfor jpcite billable-unit estimates. Use Evidence Packetinclude_compression=trueto compare caller-supplied input-context estimates with the caller baseline. Provider output/reasoning/search/cache costs remain outside jpcite.
SLA & infrastructure
Monthly uptime target: 99.0% on
api.jpcite.com(Fly.io Tokyo + Cloudflare Pages + Cloudflare WAF). Seedocs/sla.md.Tokushoho disclosure — full statutory disclosure under 特定商取引法 at
site/tokushoho.html.Spec surfaces —
site/llms.txtandsite/llms-full.txt(JA);site/llms.en.txtandsite/llms-full.en.txt(EN) for AI-agent discovery.
Evaluation
Tool quality is publicly verifiable: see evals/ for a 79-query gold-standard suite (gold.yaml + run.py) covering 農業 / 製造 / IT / 創業 / 都道府県 / 税制 / 融資 / 採択事例 / prescreen / 行政処分 / cross-dataset / edge cases / 7 one-shot discovery tools (smb_starter_pack / deadline_calendar / subsidy_combo_finder / similar_cases / subsidy_roadmap_3yr / regulatory_prep_pack). Every expected_ids list was generated against the local evaluation snapshot; CI runs the suite on every PR. Per-tool precision table: docs/per_tool_precision.md. Run locally with .venv/bin/python evals/run.py.
Known limitations
jpcite is a public-record evidence layer, not a legal, tax, audit, credit, or filing decision service. Corpus coverage, source freshness, and field-level provenance vary by source family. Responses include source_url, source_fetched_at, known_gaps, and disclaimers where applicable so callers can verify primary sources before business decisions.
Support
Docs: https://jpcite.com/docs/ (search: built-in lunr; Algolia DocSearch pending OSS-program approval)
Email: info@bookyou.net
License
MIT © 2026 jpcite
Launch state — 2026-05-16/17 (Wave 50 RC1 LANDED + Wave 51 + Wave 60-94)
Wave 50 RC1 = LANDED (2026-05-16). Contract layer + production deploy preflight gate substrate fully landed across 20 commits (Stream G 6 PR + cleanup PR7 + Wave 49 G2 + 73-tick revert + Wave 51 dim K-S foundational). Wave 51 tick 0 (9/9 dim K-S + L1 source-family + L2 math sweep, 11 modules, 416 tests PASS) closed in the same session. Wave 60-94 added M&A / talent / brand / safety / real_estate / insurance outcome primitives (432 cumulative outcomes). Canonical closeouts: docs/_internal/WAVE50_RC1_FINAL_CLOSEOUT_2026_05_16.md, docs/_internal/WAVE51_DIM_K_S_CLOSEOUT_2026_05_16.md, docs/_internal/AWS_CANARY_INFRA_LIVE_2026_05_16.md.
mypy strict: 0 errors (tick 6 71→0 achieved; new strict errors are red gate)
pytest: 8215+ PASS, 0 fail (collected 8628, +200+ tests landed)
coverage: 76%+ (tick 9)
production deploy readiness gate: 4/7 PASSING (CL6 audit — earlier 7/7 regressed during PERF cascade; remediation in flight)
preflight: 5/5 READY (Stream A 5 preflight artifacts all READY)
preflight_scorecard.state:
AWS_CANARY_READY(operator token gate--unlock-live-aws-commandsrequired to flip live_aws=true; scorecard runner authority only)RC1 contract layer: 19 Pydantic models + 20 JSON Schema,
scripts/check_schema_contract_parity.pybidirectional round-trip 0 driftRelease Capsule: 21 artifacts in manifest + 14 outcome contracts (¥300-¥900 band 実値 filled) + 3 inline packets
AWS canary infra: Phase 1-8 DONE + Phase 9 dryrun verified ($18,425 verified credit remaining post-CL16 audit; wet-run gated on operator UNLOCK)
See CHANGELOG.md for the full release walk.
Keywords: mcp, mcp-server, mcp-tools, claude, rag, agent-tools, japan, japanese, legal-tech, subsidies, grants, loans, tax, tax-incentives, corporate-registry, enforcement, evidence, citation, government, compliance, jpcite, autonomath-mcp, 補助金, 助成金, 融資, 税制優遇, 認定制度, 採択事例, 行政処分, 国税庁, e-Gov, mcp-2025-06-18
Badges
Offline / mirrored copies of the same badges live in badges/ for use in environments where shields.io is unreachable.
last_updated: 2026-05-17
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