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autonomath-mcp

REST + MCP context-compression layer for Japanese institutional public data. jpcite turns long PDFs, official pages, and search results into compact Evidence Packets with source URLs, fetched timestamps, known gaps, and compatibility/exclusion rules before downstream AI agents draft answers. 3 yen/billable unit metered (3.30 tax-incl), anonymous 3/day per IP free.

PyPI

Version: 0.5.0

Weekly downloads: 202

Maintainers: 1

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.

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.

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).

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:

#	Topic	Packet URL
1	北区パン製造補助金 (SME)	https://jpcite.com/packets/sme-kita-ku-pan-seizou-hojokin-2026-05-18.html
2	軽減税率対象品目 (税理士)	https://jpcite.com/packets/zeirishi-keigen-zeiritsu-2026-2026-05-18.html
3	法務局 routing (司法書士)	https://jpcite.com/packets/shihou-shihou-shoshi-vs-houmu-kyoku-routing-2026-05-18.html
4	商標出願戦略 (弁理士)	https://jpcite.com/packets/benri-senkou-shouhyou-search-routine-2026-05-18.html
5	法人税試算 (税理士)	https://jpcite.com/packets/zeirishi-bouei-tokubetsu-houjinzei-2026-2026-05-18.html

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 (`source_fetched_at` stamped)
Citation	Hallucinated / outdated URLs	Verified `source_url` + `source_fetched_at` (cryptographic attestation: Q3 2026 roadmap)
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 `_disclaimer` envelope
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 carry source_url + source_fetched_at + content_hash for traceability; signatures are not yet emitted.
Freshness: source_fetched_at <24h on all hot tables; per-source distribution at /v1/stats/freshness
Compliance: 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	`search_`, `list_`, `get_simple_`, `enum_`
B	¥6	5 (medium)	¥170	96.5%	¥164	`search_v2_`, `expand_`, `get_with_relations_*`
C	¥12	7 (deep)	¥347	96.5%	¥335	`HE-1`, `HE-3`, `precomputed_answer`, `agent_briefing`, `cohort_*`
D	¥30	7 (deep+)	¥500	94.0%	¥470	`HE-1 full`, `evidence_packet_full`, `portfolio_analysis`, `regulatory_impact_chain`

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 — no aggregate profit/return projection (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 weave

Cost 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 (`am_entities`)	504,238	法人 / 制度 / 法令 / 判例 / 採択 / 行政処分 unified ID space
Entity facts (`am_entity_facts`)	6,228,893	source_id-backed atomic facts (A6 done, source_id 0→81,787 backfilled)
Precomputed answers (`am_precomputed_answer`)	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
`.well-known/agents.json`	https://jpcite.com/.well-known/agents.json	machine-readable AI-capability spec (tools / pricing / corpus snapshot)
OpenAPI agent-safe	https://api.jpcite.com/v1/openapi.agent.json	ChatGPT Custom GPT Actions importer
OpenAPI full	https://api.jpcite.com/v1/openapi.json	SDK generators / Postman
Cost-saving SOT (FF1)	`docs/_internal/JPCITE_COST_ROI_SOT_2026_05_17.md`	tier quintuple `(yen, opus_turns, opus_yen, saving_pct, saving_yen)` — validator-gated
Cost-saving public copy	`docs/canonical/cost_saving_examples.md`	API-fee-delta narrative, 14 audience entries + 6 use-case calculator
MCP tool catalog	`docs/mcp-tools.md`	full 261-tool list + arguments
Distribution manifest	`scripts/distribution_manifest.yml`	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)	`docs/_internal/P5_BENCHMARK_RESULTS_2026_05_17.md`	quality / latency benchmark walk
Evaluation suite	`evals/gold.yaml`	79-query gold-standard (run `.venv/bin/python evals/run.py`)
Stats — coverage / freshness	`/v1/stats/coverage`, `/v1/stats/freshness`, `/v1/stats/usage`	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_source with content_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=8 floor 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_support envelopes 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 (`get_usage_status`)
Audit / composition	`audit_batch_evaluate`, `compose_audit_workpaper`, `resolve_citation_chain`
jpcite generic	Entity/Fact DB, funding stack, evidence/source manifests, lifecycle/graph/rule-engine, tax/certification/loan/enforcement wrappers
V4 universal	`get_annotations`, `validate`, `get_provenance`, `get_provenance_for_fact`
Static resources	`list_static_resources_am`, `get_static_resource_am`, `list_example_profiles_am`, `get_example_profile_am`, `deep_health_am`
NTA corpus	`cite_tsutatsu`, `find_bunsho_kaitou`, `find_saiketsu`, `find_shitsugi`
Eligibility composition	`apply_eligibility_chain_am`, `find_complementary_programs_am`, `program_active_periods_am`, `simulate_application_am`, `track_amendment_lineage_am`
Application composition	`bundle_application_kit`, `cross_check_jurisdiction`, `forecast_program_renewal`, `match_due_diligence_questions`, `prepare_kessan_briefing`
Industry packs	`pack_construction`, `pack_manufacturing`, `pack_real_estate`
Corporate layer	`get_houjin_360_am`, `list_edinet_disclosures`, `search_invoice_by_houjin_partial`
Wave 60-94 outcome / cohort	`agent_briefing_pack`, `agent_cohort_deep`, `agent_cohort_ultra`, M&A / talent / brand / safety / real_estate / insurance outcome primitives

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 _disclaimer envelopes 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.py enforces 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-mcp

TypeScript / 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 export
TypeScript: 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. See docs/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). See docs/alerts_guide.md.
Stats (public transparency): GET /v1/stats/coverage (per-prefecture / authority / kind program counts), GET /v1/stats/freshness (per-source source_fetched_at distribution), 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/preview for jpcite billable-unit estimates. Use Evidence Packet include_compression=true to 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). See docs/sla.md.
Tokushoho disclosure — full statutory disclosure under 特定商取引法 at site/tokushoho.html.
Spec surfaces — site/llms.txt and site/llms-full.txt (JA); site/llms.en.txt and site/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

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-commands required to flip live_aws=true; scorecard runner authority only)
RC1 contract layer: 19 Pydantic models + 20 JSON Schema, scripts/check_schema_contract_parity.py bidirectional round-trip 0 drift
Release 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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FAQs

What is autonomath-mcp?

Is autonomath-mcp well maintained?

We found that autonomath-mcp demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.

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Install

autonomath-mcp

jpcite

AI agent commerce — 5-stage funnel

5 sample packets (AI-search SERP test corpus)

Quickstart — Cursor / Claude Code (30 seconds)

Opus alone vs Opus + jpcite

Trust footer

TL;DR for AI agents

Tier pricing — Opus 4.7 anchor comparison

How it works (ingredient + finishing)

MCP server quickstart (Claude Desktop, 30 seconds)

REST quickstart (30 seconds)

Output sample

Cohort coverage — 5 cohorts × per-call saving

Data moat — live corpus snapshot

Verify links (agent-readable SOT)

How jpcite compares to single-source MCP servers

Architecture overview (4-layer)

MCP tools — 261 at default gates

Constraints / non-goals (what jpcite isn't)

REST API & SDKs

Self-serve dashboards & transparency

Pricing — packet bank product lines

SLA & infrastructure

Evaluation

Known limitations

Support

License

Launch state — 2026-05-16/17 (Wave 50 RC1 LANDED + Wave 51 + Wave 60-94)

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