BDDK MCP Server

Türkçe | English

BDDK MCP Server is an offline-first Model Context Protocol server for searching, retrieving, and analyzing Turkish banking regulation data from BDDK and mevzuat.gov.tr. It combines catalog search, document retrieval, section-level legal lookup, semantic search, bulletin analytics, document quality checks, and operator backfill workflows.

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Türkçe

Ne İşe Yarar?

Bu proje, BDDK karar ve düzenlemelerini LLM araçları için güvenli ve izlenebilir bir MCP sunucusu olarak sunar. Amaç, modelin kendi bilgisinden cevap üretmesi yerine yerel veri deposundaki BDDK kaynaklarına dayanmasıdır.

Temel kullanım alanları:

• BDDK düzenleme kataloğunda arama
• Doküman gövdesinde semantik ve tam metin arama
• Belirli doküman sayfalarını Markdown olarak getirme
• Madde, İlke, Paragraf, Ek gibi bölümleri doğrudan getirme
• Haftalık ve aylık bankacılık bülteni verilerini sorgulama
• Regülatör değişiklikleri, duyurular ve trendler için özet üretme
• Doküman kalitesi, OCR/formül riski ve extraction hatalarını izleme

Öne Çıkan Özellikler

• MCP uyumlu araçlar: Claude, Codex ve MCP destekleyen istemcilerle çalışır.
• Offline-first çalışma: Veriler PostgreSQL/pgvector üzerinden yerel veya deployment veritabanından servis edilir.
• Katalog ve gövde araması ayrımı: search_bddk_regulations sadece başlık/metadata arar; search_document_store doküman gövdesinde semantik arama yapar.
• Bölüm bazlı erişim: get_document_section ve search_document_sections ile 943 İlke 5 veya mevzuat_22599 Madde 9 gibi referanslar doğrudan bulunur.
• Exact legal-reference koruması: Madde 9 gibi lexical eşleşmeler, semantik skor düşük olsa bile korunur.
• Kalite etiketleri: Doküman çıktıları clean, warning, fail sinyalleri ve kalite bayraklarıyla işaretlenir.
• Güvenli Markdown: Data URI, raw HTML/OCR artefact ve uzun satırlar context'e verilmeden temizlenir.
• Operatör scriptleri: kalite tarama, kalite backfill ve document_sections reindex akışları mevcuttur.
• PostgreSQL + pgvector: dokümanlar, bölümler, FTS ve vektör arama tek veritabanı üzerinde çalışır.

Araç Yüzeyi

Varsayılan public deployment BDDK_ADMIN_TOOLS=false ile 16 read-only araç expose eder.

Modül	Araçlar
Arama	search_bddk_regulations, search_document_store, search_bddk_institutions, search_bddk_announcements
Doküman	get_bddk_document, get_document_history
Bölümler	get_document_section, search_document_sections
Bülten	get_bddk_bulletin, get_bddk_bulletin_snapshot, get_bddk_monthly, bddk_cache_status
Analitik	analyze_bulletin_trends, get_regulatory_digest, compare_bulletin_metrics, check_bddk_updates

BDDK_ADMIN_TOOLS=true ile ek operatör araçları açılır. Admin/operator deployment toplam 26 tools olarak belgelenir: 16 public araç + 10 operatör aracı.

• document_store_stats
• refresh_bddk_cache
• sync_bddk_documents
• trigger_startup_sync
• document_health
• health_check
• bddk_metrics
• backfill_degraded_documents
• backfill_status
• document_quality_report

Geçerli runtime için toplam olası MCP araç sayısı 26 tools olarak belgelenir. Benchmark schema fixture sayısı runtime deployment sayısından farklı olabilir; benchmark koşuları kullandıkları exact tool listesini kaydetmelidir. Bkz. benchmark/README.md.

Hızlı Başlangıç

Gereksinimler:

• Python 3.12 veya 3.13
• uv
• PostgreSQL 14+ ve pgvector
• Opsiyonel: Docker Compose

Kurulum:

git clone https://github.com/omercagatay/bddk-mcp.git
cd bddk-mcp
uv sync

Lokal PostgreSQL:

docker compose up -d db
uv run python -c 'import asyncio, asyncpg
async def main():
    conn = await asyncpg.connect("postgresql://bddk:bddk@localhost:5432/bddk")
    exists = await conn.fetchval("SELECT 1 FROM pg_database WHERE datname = $1", "bddk_test")
    if not exists:
        await conn.execute("CREATE DATABASE bddk_test")
    await conn.close()
asyncio.run(main())'

Test:

uv run pytest tests/test_tools_sections.py tests/test_doc_store.py -k section -v
uv run ruff check .

MCP stdio çalıştırma:

BDDK_DATABASE_URL=postgresql://bddk:bddk@localhost:5432/bddk \
uv run mcp run server.py

HTTP transport:

BDDK_DATABASE_URL=postgresql://bddk:bddk@localhost:5432/bddk \
MCP_TRANSPORT=streamable-http \
PORT=8000 \
uv run python server.py

Claude / Codex Yapılandırması

Örnek MCP config:

{
  "mcpServers": {
    "bddk": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/bddk-mcp",
        "mcp",
        "run",
        "server.py"
      ],
      "env": {
        "BDDK_DATABASE_URL": "postgresql://bddk:bddk@localhost:5432/bddk"
      }
    }
  }
}

Örnek Sorgular

search_bddk_regulations(keywords="kredilerin sınıflandırılması")
search_document_store(query="TFRS 9 kredi riskinde önemli artış")
get_bddk_document(document_id="mevzuat_22599", page_number=1)
get_document_section(document_id="943", section_type="ilke", section_ref="5")
search_document_sections(query="Karşılık Yönetmeliği Madde 9 TFRS 9")
get_bddk_bulletin(metric_id="1.0.1", currency="TRY", days=90)
analyze_bulletin_trends(metric_id="1.0.1", lookback_weeks=12)
get_regulatory_digest(period="week")

Operatör Workflowleri

Kalite taraması:

uv run python scripts/scan_document_quality.py --db --out-dir quality_reports --allow-failures

Kalite problemi olan dokümanları dry-run:

uv run python scripts/backfill_quality_failures.py --dry-run

Belirli bir kalite failure dokümanını yeniden çekme:

uv run python scripts/backfill_quality_failures.py --doc-id mevzuat_21192 --execute

Mevcut dokümanlardan document_sections tablosunu yeniden oluşturma:

uv run python scripts/reindex_document_sections.py --execute

Railway production üzerinde tek seferlik section reindex:

railway run --service Postgres --environment production \
  sh -c 'uv run python scripts/reindex_document_sections.py --database-url "$DATABASE_PUBLIC_URL" --execute'

Opsiyonel retrieval telemetry:

BDDK_TELEMETRY_ENABLED=true uv run python server.py

Telemetry varsayılan olarak kapalıdır. Açıldığında tool_call_traces tablosuna latency, result count, doc ID, kalite etiketi ve relevance özeti yazar; query/prompt metni hash/uzunluk özeti olarak saklanır. Raw metin yalnızca BDDK_TELEMETRY_STORE_TEXT=true açıkça set edilirse yazılır.

Mimari

server.py                 FastMCP giriş noktası ve lifecycle
deps.py                   Paylaşılan bağımlılık container'ı
config.py                 Ortam değişkenleri ve validasyon
client.py                 BDDK katalog/cache istemcisi
doc_store.py              PostgreSQL doküman, versiyon, FTS ve bölüm deposu
vector_store.py           pgvector + hibrit semantik/FTS arama
section_index.py          Madde/İlke/Paragraf/Ek parser'ı
legal_ref.py              Türkçe legal-reference parser'ı
markdown_quality.py       Markdown sanitization ve kalite etiketleri
doc_sync.py               İndirme ve extraction pipeline'ı
tools/                    MCP tool modülleri
scripts/                  Operatör ve backfill scriptleri
benchmark/                Tool schema ve benchmark altyapısı

Veri Kalitesi ve Güvenlik Notları

• Tool cevapları sadece lokal store'dan gelir; runtime'da doküman live-fetch yapılmaz.
• Kalitesi düşük extraction çıktıları warning veya fail olarak işaretlenir.
• Formül ağır veya OCR bozuk dokümanlarda kaynak PDF incelemesi gerekebilir.
• Tool cevaplarında data URI, raw HTML ve bazı OCR artefact'ları temizlenir.
• Model cevap verirken sadece tool çıktısına dayanmalıdır; karar numarası, tarih veya hukuki sonuç uydurulmamalıdır.
• Bilinen extraction sorunları, fail doküman listesi ve backfill komutları için docs/DOCUMENT_QUALITY.md sayfasına bakın.

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English

What Is This?

BDDK MCP Server exposes Turkish banking regulation data as a safe, auditable Model Context Protocol server. It is designed to ground LLM answers in local BDDK data instead of relying on the model's prior knowledge.

Common use cases:

• Search the BDDK regulation catalog
• Search inside document bodies with semantic and full-text retrieval
• Retrieve paginated Markdown documents
• Retrieve exact legal sections such as Madde, Ilke, Paragraf, and Ek
• Query weekly and monthly banking bulletin data
• Produce regulatory digests and trend summaries
• Monitor document quality, OCR/formula risk, and extraction failures

Highlights

• MCP-compatible tools: works with Claude, Codex, and other MCP clients.
• Offline-first runtime: data is served from PostgreSQL/pgvector rather than live web fetches.
• Catalog/body separation: search_bddk_regulations searches metadata; search_document_store searches document bodies.
• Section-level retrieval: get_document_section and search_document_sections support references like 943 Ilke 5 and mevzuat_22599 Madde 9.
• Exact legal-reference preservation: lexical hits such as Madde 9 survive dense relevance filtering.
• Quality labels: document outputs include clean, warning, or fail metadata and quality flags.
• Safe Markdown: data URIs, raw HTML/OCR artifacts, and pathological long lines are sanitized before model context.
• Operator scripts: quality scan, quality backfill, and document_sections reindex workflows are included.
• PostgreSQL + pgvector: documents, sections, FTS, and vector search share one database.

Tool Surface

The default public deployment with BDDK_ADMIN_TOOLS=false exposes 16 read-only tools.

Module	Tools
Search	search_bddk_regulations, search_document_store, search_bddk_institutions, search_bddk_announcements
Documents	get_bddk_document, get_document_history
Sections	get_document_section, search_document_sections
Bulletin	get_bddk_bulletin, get_bddk_bulletin_snapshot, get_bddk_monthly, bddk_cache_status
Analytics	analyze_bulletin_trends, get_regulatory_digest, compare_bulletin_metrics, check_bddk_updates

With BDDK_ADMIN_TOOLS=true, operator tools are also exposed. The admin/operator deployment exposes 26 tools total: 16 public tools plus 10 operator tools.

• document_store_stats
• refresh_bddk_cache
• sync_bddk_documents
• trigger_startup_sync
• document_health
• health_check
• bddk_metrics
• backfill_degraded_documents
• backfill_status
• document_quality_report

Total possible MCP tools in the current runtime is 26. Benchmark schema fixture counts can differ from runtime deployment counts; benchmark runs should record the exact exposed tool list they used. See benchmark/README.md.

Quick Start

Requirements:

• Python 3.12 or 3.13
• uv
• PostgreSQL 14+ with pgvector
• Optional: Docker Compose

Install:

git clone https://github.com/omercagatay/bddk-mcp.git
cd bddk-mcp
uv sync

Local PostgreSQL:

docker compose up -d db
uv run python -c 'import asyncio, asyncpg
async def main():
    conn = await asyncpg.connect("postgresql://bddk:bddk@localhost:5432/bddk")
    exists = await conn.fetchval("SELECT 1 FROM pg_database WHERE datname = $1", "bddk_test")
    if not exists:
        await conn.execute("CREATE DATABASE bddk_test")
    await conn.close()
asyncio.run(main())'

Test:

uv run pytest tests/test_tools_sections.py tests/test_doc_store.py -k section -v
uv run ruff check .

Run MCP over stdio:

BDDK_DATABASE_URL=postgresql://bddk:bddk@localhost:5432/bddk \
uv run mcp run server.py

Run streamable HTTP:

BDDK_DATABASE_URL=postgresql://bddk:bddk@localhost:5432/bddk \
MCP_TRANSPORT=streamable-http \
PORT=8000 \
uv run python server.py

Claude / Codex Configuration

Example MCP config:

{
  "mcpServers": {
    "bddk": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/bddk-mcp",
        "mcp",
        "run",
        "server.py"
      ],
      "env": {
        "BDDK_DATABASE_URL": "postgresql://bddk:bddk@localhost:5432/bddk"
      }
    }
  }
}

Example Queries

search_bddk_regulations(keywords="kredilerin siniflandirilmasi")
search_document_store(query="TFRS 9 kredi riskinde onemli artis")
get_bddk_document(document_id="mevzuat_22599", page_number=1)
get_document_section(document_id="943", section_type="ilke", section_ref="5")
search_document_sections(query="Karsilik Yonetmeligi Madde 9 TFRS 9")
get_bddk_bulletin(metric_id="1.0.1", currency="TRY", days=90)
analyze_bulletin_trends(metric_id="1.0.1", lookback_weeks=12)
get_regulatory_digest(period="week")

Operator Workflows

Run a document quality scan:

uv run python scripts/scan_document_quality.py --db --out-dir quality_reports --allow-failures

Dry-run backfill for known quality failures:

uv run python scripts/backfill_quality_failures.py --dry-run

Re-extract one known failed document:

uv run python scripts/backfill_quality_failures.py --doc-id mevzuat_21192 --execute

Rebuild document_sections for existing stored documents:

uv run python scripts/reindex_document_sections.py --execute

One-off section reindex on Railway production:

railway run --service Postgres --environment production \
  sh -c 'uv run python scripts/reindex_document_sections.py --database-url "$DATABASE_PUBLIC_URL" --execute'

Optional retrieval telemetry:

BDDK_TELEMETRY_ENABLED=true uv run python server.py

Telemetry is disabled by default. When enabled, the server writes latency, result counts, document IDs, quality labels, and relevance summaries to tool_call_traces; query/prompt text is stored as a hash and length summary. Raw text is only stored when BDDK_TELEMETRY_STORE_TEXT=true is explicitly set.

Architecture

server.py                 FastMCP entry point and lifecycle
deps.py                   Shared dependency container
config.py                 Environment configuration and validation
client.py                 BDDK catalog/cache client
doc_store.py              PostgreSQL document, version, FTS, and section store
vector_store.py           pgvector + hybrid semantic/FTS search
section_index.py          Madde/Ilke/Paragraf/Ek parser
legal_ref.py              Turkish legal-reference parser
markdown_quality.py       Markdown sanitization and quality labels
doc_sync.py               Download and extraction pipeline
tools/                    MCP tool modules
scripts/                  Operator and backfill scripts
benchmark/                Tool schemas and benchmark infrastructure

Data Quality And Safety Notes

• Tool responses are served from the local store; documents are not live-fetched at runtime.
• Low-quality extractions are marked as warning or fail.
• Formula-heavy or OCR-corrupted documents may require source PDF review.
• Data URIs, raw HTML, and selected OCR artifacts are removed before model context.
• The model should answer only from tool output. It should not invent decision numbers, dates, or legal conclusions.
• See docs/DOCUMENT_QUALITY.md for known extraction issues, the tracked fail list, and backfill commands.

Development Commands

uv run pytest tests/ -v --tb=short
uv run ruff check .
uv run ruff format .

Focused checks used often in this project:

uv run pytest tests/test_markdown_quality.py tests/test_tools_documents.py -v
uv run pytest tests/test_legal_ref.py tests/test_section_index.py tests/test_tools_sections.py -v
uv run pytest tests/test_vector_store.py tests/test_legal_ref.py -v -rs

License

No license file is currently included. Treat reuse rights as unspecified until a license is added.