macro-mcp

MCP server for international macro statistics, company identity and FX rates. Every source is free and keyless.

Why

Most finance MCP servers wrap one API with one tool per endpoint. That does not survive going international: there is no global EDGAR, no shared company key, and each provider codes countries differently. This takes a different route.

• One grammar, many providers. SDMX is ISO 17369, and the BIS, ECB, IMF, OECD, Eurostat, World Bank, ILO and a dozen national offices all publish through it. One set of tools reaches all of them. Sources that do not speak SDMX are adapted to the same grammar rather than given tools of their own: Singapore's Table Builder is plain JSON, but a table becomes a dataflow, its rows become one dimension's codes, and its periods are rewritten from 2024 Jan to 2024-01 so they join against everything else.

• LEI as the join key. GLEIF covers 2.8M entities across 200+ jurisdictions, free, including the ownership graph. Tickers and CIKs do not travel.

• Rate conventions are explicit. Converting flows at an end-of-period rate is a silent error, so the convention is a required argument, not a default. The ECB quotes everything against the euro, so a pair without EUR is a cross derived from two fixings; the response says so rather than presenting it as a published rate.

• No translation layer. SDMX names are multilingual and providers ship English in the same response, so search matches every localization and returns English where it exists. crops finds ISTAT's Coltivazioni; chomage finds INSEE's unemployment series. Entity names come from GLEIF's registered alternative-language name, which is a legal fact rather than a translation.

Related MCP server: mcp-server-fxmacrodata

Install

Nothing to clone and no keys to obtain. Point an MCP client at the repository and uv does the rest:

{
  "mcpServers": {
    "macro": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/rabidlego25/macro-mcp", "macro-mcp"]
    }
  }
}

uvx resolves and caches the environment on the first run, and that run is slow: four minutes and 306MB here, nearly all of it downloading pandas and lxml. Every start after it was under two seconds. Run the command once in a terminal before registering it, since a client that starts servers with a timeout will give up long before the first one finishes.

To work on the server rather than use it, clone and run from the checkout:

uv sync

{
  "mcpServers": {
    "macro": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/macro-mcp", "macro-mcp"]
    }
  }
}

Tools

Discovery is progressive: a single codelist can hold hundreds of entries, so metadata is never returned whole.

The same restraint applies to data. A flat row per observation repeats the whole key on every row, which on a 16-dimension flow is around 450 wasted bytes an observation, so fetch_data hoists the invariant part of the key, groups the rest into series and returns [period, value] pairs. A year of daily Bund yields goes from 173KB to 6KB.

{
  "key": {"FREQ": "M"},
  "units": {"UNIT_MEASURE": "Per cent per year", "UNIT_MULT": "Units"},
  "columns": ["period", "value"],
  "series": [{"key": {"REF_AREA": "JP"}, "observations": [["2024-01", -0.1]]}],
  "range": ["2024-01", "2024-12"], "total": 24
}

Units

A bare 634751300000000.0 is not an answer to what Japan's GDP was. It is ¥634.75tn or ¥634.75bn depending on a multiplier the provider ships and sdmx1 discards unless asked. So fetch_data returns what the number is measured in, resolved from the provider's code to its label: BIS sends UNIT_MEASURE="368", which is no more use than the number was.

Providers spell it differently and attach it at different levels. BIS and ILO write UNIT_MEASURE/UNIT_MULT, ECB adds UNIT_INDEX_BASE, Bundesbank prefixes its own BBK_UNIT, IMF publishes no unit at all but does populate SCALE, and Singapore states one per row. So units are matched by pattern rather than by a list, at whatever level they arrive.

They are returned beside the key, never inside it: a unit is not a dimension, and an agent that echoed one back to fetch_data would get an error from the provider. A unit that is invariant across the response is hoisted once; one that varies lands on each series, so a response mixing percent with an index says so instead of interleaving the two silently.

Nothing else a provider attaches is returned. BIS ships around 2.5KB of compilation notes and source references per series, against a response format whose whole point is 6KB.

names gives the English label for every code in the response, so a series keyed XDC or CP01 reads without another round trip. It comes off the structure the units already needed, and only the codes that actually appear are returned. range is the span actually returned, so truncation is visible rather than inferred from a total that does not match. Truncation keeps the most recent observations, and limit is shared across the series in the response rather than spent oldest-first over the whole of it. Otherwise a two-country request came back with only the country whose history ran latest. The split is max-min fair, so a short series hands its unused share to a long one and a response that fits under the budget is never clipped. Where there are more series than the budget can seat, the ones left out are named under dropped_series instead of going missing. Periods with no value are omitted and counted under empty.

limit bounds the download as well as the response. The provider is asked for only the newest limit + 1 observations per series, through SDMX's own lastNObservations. Three observations cost 10KB from BIS rather than 314KB, 3KB from ECB's daily reference rates rather than 1.4MB, and 4KB from the Bundesbank 10-year Bund yield rather than 2.3MB; twelve months of Japanese CPI cost 53KB and 4.4s from IMF rather than 1.5MB and 12.1s.

The extra one is what keeps truncation visible: asked for exactly limit, a clipped series comes back the same length as a complete one. When a series does arrive at the cap, total is a floor rather than the length of the series, and the truncated note says so: counting the rest would mean downloading it.

Point in time

A series read today is as-revised, not as-known, which quietly gives a backtest numbers nobody had at the time. IMF republishes whole dataflows as monthly vintages beside the current one, so compare_vintages can read the same key from each and report what moved:

{"period": "2024", "was": 634226000000000.0, "now": 634751300000000.0,
 "between": ["ANEA_2026_APR_VINTAGE", "ANEA"], "change_pct": 0.0828}

Japan's 2024 nominal GDP, revised up by ¥525.3bn since the April 2026 vintage. Coverage varies between vintages as well as values. One 2026 vintage of the national accounts carries 18,068 observations and another 204, so a vintage that does not have the key is listed under no_data rather than counted as agreeing with its neighbours.

Caching

Metadata is cached to disk under $XDG_CACHE_HOME/macro-mcp for a week. Structures are large and slow to build (ISTAT takes 29s cold and 1.5s warm, Eurostat 52s and 11s) and providers republish them rarely. In-process memoisation alone threw all of that away when the server exited.

Nothing else is cached. The policy denies by default and names the structure endpoints it will keep, rather than naming the data paths it will skip. That ordering matters: the first version listed the data paths, and Singapore's /tabledata/ was not among them, so observations would have been served up to a week stale. Under default-deny, an adapter whose paths nobody declared costs a round trip instead of correctness.

The data patterns are declared first because the first match wins and BIS puts /data/dataflow/ in its data URLs, which the structure patterns would otherwise claim. Set MACRO_MCP_NO_CACHE=1 to bypass caching entirely.

Pacing and retries

Every request goes through one transport adapter that bounds how hard a provider is asked and absorbs the failures that are not answers.

• Per host, not per provider. Four requests in flight at once by default, and no delay. HKMA is the exception at one at a time, 4/s: it started answering 502 on every path after eight parallel requests and did not recover for minutes.

• Transient statuses are retried. 429, 500, 502, 503 and 504 get three attempts with exponential backoff and jitter, so ISTAT's intermittent 500s no longer reach the agent as errors. A 404 is an answer and is not repeated, and neither is a TLS failure: UY110's self-signed certificate will not verify on the second attempt either.

• Retry-After is honoured, and held against the whole host. A 429 is addressed to this client rather than to the request that drew it, so everything queued behind it waits too.

The adapter sits below the cache, so a cached read neither waits nor spends a slot. The state is per process: two servers on one machine do not coordinate, and the shared User-Agent means a provider throttling it throttles every install at once.

Tests

uv run pytest                                        # offline, ~1s
MACRO_MCP_LIVE=1 uv run pytest -m "not revisable"    # + structure, ~70s
MACRO_MCP_LIVE=1 uv run pytest -m revisable          # what the data did

The live suite is split because its two halves mean opposite things. Unmarked tests assert structure (a flow exists, a key resolves, a period joins) and a failure means a provider moved or this server broke. Tests marked revisable assert that a number is still the number it was, and a failure there means a CPI was rebased or a national account revised: the provider doing its job, and the event this project exists to surface. They run as separate CI jobs, and only the first can fail the build.

The offline suite replays saved responses in tests/fixtures/. Every case in it was a real failure, and each asserts a value rather than the absence of an exception, because these paths fail by returning a plausible wrong answer with a 200 status rather than by raising.

One test spawns the server as its own process and speaks JSON-RPC to it over stdin, which is the only thing that covers main(), the stdio transport and the handshake. It is how the empty serverInfo.version was found. The rest of the tools are exercised through call_tool, not by calling the functions underneath, so argument validation, the published schema and the JSON an agent actually parses are all in the path. What those tools publish is asserted literally: the names, which arguments may be omitted, what they then default to, and the docstrings themselves. Every part of it can drift from the code beneath without failing anything else.

The recorded responses are mounted under a real sdmx1 session rather than fed to the parser directly, so a test drives URL construction, the Accept header, sdmx1 and the packing here together. That is where several of the failures were: an SDMX key is positional, so getting the dimension order wrong returns somebody else's series with a 200 status. ECB stands in for the spine that 27 of the 29 providers traverse; Bundesbank, Hong Kong, Singapore and BIS have fixtures of their own because each is an exception to it.

The live suite pins historical values, so a failure means a provider moved, renamed something, or revised a series.

Evals

Twenty questions a person would actually ask, run through the tools against live providers, with every call and response kept in evals/log. Thirteen were answered, three partly, four blocked, one of those because HKMA was down.

Almost nothing crashed. The server returned 200 and a well-formed response and the agent was stuck anyway, which is the failure this project is about: an empty result that echoed nothing back, a 15-digit GDP figure with no currency attached, a search for "national accounts" that reported one hit and hid the flow, and a search for Banco Santander led by an unrelated company that matched the city. Six of the thirteen findings are fixed and verified against the live providers; evals/README.md lists what was fixed, what was only improved, and what still stands.

Current problems and limitations

Defects and constraints in this server, as distinct from properties of the data (below) and gaps in provider coverage (further below). Roughly worst first.

• Observations are never cached, so every fetch pays full price. That is deliberate (see Caching) but it means repeated identical queries re-download each time. It bites hardest on HKMA: a bound coarser than the endpoint's own period cannot be sent to the service, so the adapter pulls the full history (up to 6,302 rows) and filters locally, on every call.

• sdmx1 cannot query SDMX 3.0 data. It builds ?c=TIME_PERIOD instead of c[TIME_PERIOD]=ge:…, puts the source id where the agency belongs in the path, and raises TypeError: unhashable type: 'MemberValue' when a key is passed as a dict. Nothing hits this today because IMF is wired to its 2.1 endpoint, but the first genuinely 3.0-only provider will need an adapter.

• Point-in-time is IMF-only. No other provider here republishes vintages, so compare_vintages cannot answer the question anywhere else. It also spends its request budget before it knows which vintages carry the key, so asking for five can leave fewer readable; those appear under no_data rather than being topped up, since the alternative is an unbounded number of calls to a slow service.

• HKMA datasets are searchable only by slug. It publishes no titles through the API, so find_dataflows matches hk-interbank-ir-daily and not the words a person would use for it. Its quarterly datasets also report the month the quarter ended (2024-03), which is indistinguishable from a monthly period when joined against another provider.

• The disk cache only grows. Entries expire after a week but nothing prunes or vacuums the SQLite file, which reached 96MB here across a few live runs. Delete $XDG_CACHE_HOME/macro-mcp when it gets large.

• Pacing is per process and the User-Agent is shared. Two servers on one machine, or two installs anywhere, do not coordinate, so the limits in transport.HOSTS bound one client rather than the traffic a provider actually sees. Nothing here can fix that; a hosted deployment would have to.

• A paced host is a slow host. HKMA is asked one request at a time, so reading several of its datasets in one turn now costs at least 250ms each rather than going out together. That is the trade the 502s bought.

• A cold fetch downloads the structure twice. sdmx1 resolves a dict key by fetching the DSD itself, and _unit_labels then fetches it again through _dsd, which does not know about the first: the same 497KB URL twice on ECB, 3.5MB twice on an IMF vintage. Calling describe_flow first, which is the prescribed order, saves one of the two, and after the first fetch of a flow the process pays neither again. Passing a rendered key string rather than a dict would fix it.

• sdmx1 memoises structures on the Client class, not the instance. So MACRO_MCP_NO_CACHE=1 does not force a fresh structure read within one process, and neither does discarding the client: the dict outlives both. The live suite is weaker than it reads for that reason, and a before-and-after measurement taken in one process is worthless: the second half reads what the first downloaded.

• The download is capped per series, not per response. lastNObservations bounds each series the key matches, so a wildcard over 300 series still fetches limit + 1 observations for every one of them. limit bounds what comes back; only the key bounds what is fetched.

• total is a floor once the cap binds. It used to be the length of the series, which was free only because the whole series had been downloaded. Now it counts what arrived, and a series that came back at the cap has older observations nobody counted. The truncated note says which of the two it is; there is no way to report the exact length without paying for it again.

• Not every provider honours the cap, and one applied it wrongly. It is sent to all of them. BIS, ECB, IMF, Bundesbank and OECD truncate at the source; UNSD and UNICEF returned the same bytes with the parameter as without, so they appear to ignore it. ILO does something worse: it drops whole series, returning 13 of 39 at the default limit and 39 at 2001. That shipped for a while and is the reason the first capped fetch of a provider is now checked against a detail=nodata count of the keys, and the verdict recorded against the cap it was measured at. A provider that refuses the parameter, or comes back short, is asked again without it and remembered for the life of the process.

• A query that matches nothing costs two requests. A service that answers 200 to a parameter it does not understand looks exactly like a key that matched nothing, so an empty capped response is checked against an uncapped one before it is believed. Both are cheap when the query really is empty, but compare_vintages pays it once per vintage that does not carry the key.

• A narrow query gains nothing from the cap and pays a little. It bounds the history, so a key already pinned to one series inside a one-year window is the same size either way: comparing four vintages of Japanese GDP moves 21.3KB of observations before the cap and 25.3KB after, the difference being the extra request above.

• Nothing bounds the structure metadata, which is the larger half. That same vintage comparison spends 31MB on five DSDs of around 3.5MB each against 21KB of observations. It is paid once a week rather than once a call (see Caching) but on a cold process the download cap addresses the smaller part of the bill.

• The cap does not reach the non-SDMX adapters. Singapore and Hong Kong have no such parameter, so limit still bounds only their responses.

• limit binds evenly, not by importance. The budget is split max-min fair across the series in a response, so nothing is clipped while there is room and a short series hands its surplus to a long one. Once it does bind, though, every long series is cut to the same depth regardless of which one the question was about, and a response with more series than the budget can seat drops the excess, named under dropped_series but dropped all the same. Narrow start/end, or raise limit, when querying several series at once.

• The first BIS fetch of a session pays for a failed parse. BIS serves structure-specific data referencing a DSD sdmx1 cannot resolve, so the payload is downloaded and parsed once before the generic form is tried. The provider is remembered after that and every later fetch goes generic-first, but the knowledge dies with the process, and sdmx1 prints its parse stack and the failing element to stdout on the way, though the MCP SDK claims that descriptor and diverts it to stderr, so it is noise rather than corruption. Not hardcoded to BIS on purpose: IMF answers 500 to the generic header, so a wrong guess would take a provider down rather than waste a header.

• Units are only as good as the provider's own metadata. HKMA states none at all, so its numbers come back bare. Bundesbank labels its unit in German (PROZENT) because the English one is published as an empty element, and its multiplier resolves to a raw 0 because no CL_BBK_UNIT_MULT codelist is served. A raw code is left in place rather than guessed at.

• Resolving a unit code needs the flow's structure. fetch_data now reads the DSD to turn 368 into "Per cent per year", so a cold fetch against a provider whose structures are slow (ISTAT is 29s cold) pays for that metadata once a week. describe_flow has usually already warmed it, since the prescribed order goes through it.

• The non-SDMX adapters expose a single dimension. Singapore and Hong Kong return one wide table per dataset, so SERIES is the only thing to slice on. There is no REF_AREA to filter, because there is no country dimension.

• sdmx1 emits a DeprecationWarning from its own internals (it passes a deprecated provider= to itself). Left visible rather than filtered, since suppressing it would also hide the same warning if it came from here.

What it will not do for you

Properties of the data itself. The server surfaces these; it does not silently fix them.

• Geo codelists mix aggregates and members (EU27 next to France). Summing both double-counts, and nothing errors.

• Nominal, PPP and constant-price series are not interchangeable.

• Fiscal years differ. India, Japan and Australia are not calendar-year.

• Seasonal adjustment differs: X-13 in the US, TRAMO/SEATS across much of Europe.

• Most providers publish revisions without point-in-time access, so history reads as-revised rather than as-known. IMF is the exception, and compare_vintages reads it; everywhere else the caveat still stands.

• Entity search matches broadly and may rank a subsidiary above its parent. Hits carry country and status; use entity_ownership to walk up the group.

Known gaps

What is not covered, and why.

• Asian national sources are mostly gated. Headline macro for Asia is already covered by the international providers: BIS carries all of JP, CN, IN, KR, SG, HK, TW, TH, MY, ID, PH, VN, PK and BD for policy rates and property prices, and the IMF, World Bank and ILO are comparably broad. What is missing is national detail, and there the constraint bites: e-Stat (Japan), ECOS (Korea), KOSIS and data.gov.in all require registration, so they cannot be included while the project stays keyless. Singapore is in via SINGSTAT and Hong Kong via HKMA. Malaysia's OpenDOSM (api.data.gov.my) is keyless and works, but exposes no catalogue endpoint at all, so dataset ids would have to be invented from the documentation rather than derived from it.

• HKMA's dataset list is a snapshot. HKMA publishes no catalogue endpoint, so the 125 datasets in hkma.py were read off the documentation and then verified one request each against the live API. That table goes stale as HKMA adds and retires datasets; a retired one is reported as such rather than as a bare 404. Regenerate it with uv run python -m scripts.hkma_catalogue.

• IMF publishes three endpoints and only one serves data. sdmx1 ships IMF (sdmxcentral, which answers 501 on data), IMF_DATA (SDMX 2.1) and IMF_DATA3 (SDMX 3.0). The 3.0 service returns structures but a header and one empty row for every flow, so IMF_DATA is the one wired up. Its dimensions carry no local representation, so codes are reachable only through the concept each dimension identifies, and it writes monthly periods as 2024-M01, which is rewritten to 2024-01 so the series joins against everything else.

• Endpoints drift. sdmx1 hardcodes base URLs that go stale as institutions move. URL_FIXES corrects ABS (the old host stopped resolving) and Lithuania (moved to an APEX path); AGENCY corrects INEGI, whose flows are filed under a different agency id. Re-verify these if a provider starts failing.

• Bundesbank needs its own adapter (bundesbank.py). It serves valid SDMX-ML from non-standard paths, writes URNs missing their class segment, publishes codelists separately from the DSD, and returns 100MB+ for an unpinned query.

• Genuinely down: DG COMP 404s on every path and Uruguay serves a self-signed certificate. ISTAT returns intermittent 500s. See QUIRKS.

• WB_WDI, StatCan, NBB and AR1 serve data but not dataflow metadata, so flow ids must be known in advance. list_providers flags this. Which providers serve what is measured, not assumed: sdmx1's own capability table is a static declaration that disagrees with the live services in both directions. Regenerate the measured one with MACRO_MCP_NO_CACHE=1 uv run python -m scripts.probe, which retries once so a dropped connection is not recorded as a missing endpoint.

• Filings are out of scope. There is no free global equivalent until ESAP opens its API in July 2027.