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Reconcile camt.053 statements vs expected pain.001 payments with explainable matching.
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Reconcile camt.053 statements vs expected pain.001 payments with explainable matching.
Security Report
Valid MCP server (1 strong, 3 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
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How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-sebastienrousseau-reconcile-mcp": {
"args": [
"reconcile-mcp"
],
"command": "uvx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
reconcile-mcp: An MCP Server for ISO 20022 Cash Reconciliation
A Model Context Protocol server that matches expected payments
(from pain.001 credit transfers) against observed booked entries (from a
camt.053 statement) and returns an explainable reconciliation — exact
matches, short/over payments, split settlements (one-to-many), batch credits
(many-to-one), and the residual unmatched items on each side, every match
carrying a score and the reasons it was made.
Latest release: v0.0.1 — 7 MCP tools over stdio, pure-Python matching engine, deterministic sandbox test-mode, for Python 3.10+. Part of the ISO 20022 MCP suite: you own both sides of the match.
Why this exists
Reconciliation is the treasury team's daily pain: did the money we expected
actually arrive, and which invoice does each credit belong to? It is rarely
one-to-one — customers underpay, settle an invoice in instalments, or a payout
aggregator sends one lump covering a dozen receivables. reconcile-mcp does
this matching as an agent tool, and — critically for finance — shows its
work: every pairing comes with a numeric score and a plain list of the
signals (reference, amount, date, counterparty) that drove it.
The ISO 20022 MCP Suite
reconcile-mcp is the reconciliation workflow of eight coordinated,
vendor-neutral MCP servers that together cover the ISO 20022 bank-statement
workflow and the November 2026 structured-address cutover, plus a high-level orchestration layer — readiness scoring, clearing-profile linting, and audit evidence — statement depth,
whole-catalogue routing, reconciliation, multi-format ingestion, and address
remediation. Dependency ranges are kept aligned across the suite,
so the servers co-install cleanly in a single Python environment: start with
one, add the rest as your workflow grows.
| Server | Scope | Surface | Install | Use it when |
|---|---|---|---|---|
camt053-mcp | ISO 20022 camt.053/camt.052 bank statements: parse, validate, filter, reverse; MT940/MT942 migration; CBPR+ readiness; journal export | 22 MCP tools · 4 prompts · 3 resources | pip install camt053-mcp | You work with bank-to-customer statements end to end — the suite's flagship |
iso20022-mcp | Unified gateway: search / describe / validate / generate / parse meta-tools routed across the pain · pacs · camt · acmt families | 7 meta-tools | pip install "iso20022-mcp[all]" | You want one entry point to every message family |
reconcile-mcp | Matches expected pain.001 payments against observed camt.053 entries — exact, partial, one-to-many, many-to-one, every match scored and explained | 7 MCP tools | pip install reconcile-mcp | You need explainable statement/payment reconciliation — this package |
bankstatementparser-mcp | Multi-format statement ingestion: ISO 20022 CAMT.053 and pain.001, SWIFT MT940, OFX/QFX, CSV | 5 MCP tools · 1 prompt · 1 resource | pip install bankstatementparser-mcp | Your statements arrive in mixed or legacy formats |
structured-address-fix-mcp | ISO 20022 postal-address classification, assessment & remediation for the November 2026 structured-address cutover (pacs.008 / pain.001 debtor & creditor addresses) | 9 MCP tools | pip install structured-address-fix-mcp | You need debtor/creditor addresses cliff-ready ahead of 14 Nov 2026 |
iso20022-readiness-suite-mcp | Orchestration gateway: detect → structurally validate → clearing-profile lint → readiness score, plus automated remediation and pacs.002 bank-response simulation — a meta-client over the foundational servers | 4 MCP tools | pip install iso20022-readiness-suite-mcp | You want one high-level readiness / orchestration entry point over the suite |
iso20022-bank-profile-mcp | Manages, validates and serves bank-specific clearing profiles / rule packs (CBPR+, SEPA_Instant, FedNow, Generic); premium rule-pack entitlement gating | 4 MCP tools | pip install iso20022-bank-profile-mcp | You lint payments against your own institution's market practice |
iso20022-evidence-pack-mcp | Compiles readiness findings, remediation diffs and simulated responses into a sealed, Ed25519-signable audit evidence pack | 6 MCP tools | pip install iso20022-evidence-pack-mcp | You need tamper-evident audit / certification artifacts |
In one line each: camt053-mcp is the bank-statement flagship (deepest
camt.05x surface, stdio + authenticated streamable HTTP);
iso20022-mcp is the generic message toolkit (a handful of verbs over
the whole catalogue); reconcile-mcp is the reconciliation workflow
(did the money we expected actually arrive?);
bankstatementparser-mcp is the ingestion layer (many formats in, one
transaction shape out); and structured-address-fix-mcp is the
postal-address specialist (debtor/creditor addresses cliff-ready for the
Nov 2026 cutover).
The suite also includes per-family servers — pain001-mcp
(credit transfer initiation), pacs008-mcp (FI-to-FI credit
transfers), and acmt001-mcp (account management) — whose
parsed output feeds straight into this server's normalize_* adapters.
Install
pip install reconcile-mcp
# or run without installing:
uvx reconcile-mcp
MCP client config (e.g. Claude Desktop claude_desktop_config.json):
{
"mcpServers": {
"reconcile": {
"command": "reconcile-mcp"
}
}
}
Quick start (zero real data)
The server ships a sandbox test-mode: deterministic scenarios so you can run the whole flow with no setup and no real cash data. One call gets you a full, explainable result:
run_sandbox_scenario(name="month_end")
returns a realistic mixed close — one clean match, one short payment, one split settlement, and an unexpected credit correctly left unmatched:
{
"summary": {
"expected_count": 3, "observed_count": 5,
"matched_expected": 3, "unmatched_observed": 1,
"matches_by_type": {"exact": 1, "amount_mismatch": 1, "one_to_many": 1},
"fully_reconciled": false
},
"matches": [
{"type": "amount_mismatch", "expected": ["INV-6002"], "observed": ["ENT-52"],
"amount_delta": "-99.99", "confidence": "high",
"reasons": ["reference exact", "amount close (delta -99.99)", "date +/-0d", "counterparty exact"]},
{"type": "exact", "expected": ["INV-6001"], "observed": ["ENT-51"], "amount_delta": "0.00"},
{"type": "one_to_many", "expected": ["INV-6003"], "observed": ["ENT-53", "ENT-54"],
"reasons": ["amount sum of 2 entries"]}
],
"unmatched_observed": ["ENT-55"]
}
List every scenario with list_sandbox_scenarios; load one to inspect or edit
its inputs with load_sandbox_scenario.
Bring your own data
Records are small canonical objects — id and amount required, everything
else optional and used to sharpen matching:
{
"id": "INV-1001", // your reference / end-to-end id
"amount": 1200.00,
"currency": "EUR", // ISO 4217
"date": "2026-03-02", // ISO-8601
"counterparty": "Acme Ltd",
"reference": "INV-1001" // remittance / structured reference
}
Already using the rest of the suite? Feed parsed output straight in — the adapters map it for you:
normalize_pain001(document)→ the expected side, frompain001-mcp.normalize_camt053(document)→ the observed side, fromcamt053-mcp.
Then call reconcile(expected, observed).
Tools
reconcile— Match expected payments against observed entries; full explainable report.explain_match— Score a single expected/observed pair with a per-signal breakdown (tuning aid).normalize_pain001— Adapt parsedpain.001output into canonical expected records.normalize_camt053— Adapt parsedcamt.053output into canonical observed records.list_sandbox_scenarios— List the built-in test-mode scenarios and magic references.load_sandbox_scenario— Return one scenario's expected/observed inputs to inspect or edit.run_sandbox_scenario— Load a scenario and reconcile it in one call — the fastest first run.
How matching works
Each candidate pair is scored on four weighted signals, then classified:
- Reference (0.45) — exact / partial equality of references and end-to-end ids, normalised to bare alphanumerics.
- Amount (0.35) — exact within tolerance, or a linearly-decaying closeness with the delta reported.
- Date (0.10) — proximity within a configurable window; neutral if unknown.
- Counterparty (0.10) — token-set overlap of names; neutral if unknown.
Assignment is greedy, highest-score-first and fully deterministic (a total tiebreak order), so the same inputs always produce the same result. Residuals are then tested for one-to-many (a bounded subset-sum: one expected settled by several entries) and many-to-one (one entry covering several expected).
Tune any of it via the options argument: abs_tol / rel_tol,
date_window_days, high_threshold, review_threshold, currency_strict,
enable_one_to_many, max_combination.
Development
git clone https://github.com/sebastienrousseau/reconcile-mcp
cd reconcile-mcp
python -m venv .venv && . .venv/bin/activate
pip install -e . && pip install pytest pytest-cov ruff black mypy
pytest # 100% branch coverage gate
ruff check reconcile_mcp tests && black --check reconcile_mcp tests && mypy reconcile_mcp
Licence
Licensed under the Apache License, Version 2.0.
mcp-name: io.github.sebastienrousseau/reconcile-mcp
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