Imf Mcp Server

Overview

IMF SDMX 3.0 macroeconomic data — hundreds of dataflows spanning WEO projections, balance of payments, CPI, exchange rates, and national accounts across 190 countries. Browse the dataflow catalog, resolve dimension codes, and query time series from any MCP client, with large multi-country results staged to DataCanvas for SQL analysis. Runs as a stdio process, a local Streamable HTTP server, or the public hosted endpoint above.

Tools

Resources

Continuation beyond the resource's bounded codelist preview runs through imf_get_database.

Capability reference

imf_list_databases _tool

• Case-insensitive substring filter across ID, name, and description, matched against the full text — not the shortened preview this tool returns
• Vintage (historical snapshot) dataflows such as WEO_2025_OCT_VINTAGE are excluded by default; set include_vintages=true to include them
• Paged: limit (default 50, max 200) and offset; total_count reports total matches, returned_count the page size, and a notice names the next offset while matches remain
• Descriptions are cut to 200 characters here — imf_get_database and the imf://database/{dataflow_id} resource return the full text

imf_get_database _tool

• Resolves human-readable terms to SDMX dimension codes (e.g. "United States" → USA) and returns each dimension's DSD concept-scheme label
• Country codes are ISO 3-letter (USA, GBR, DEU), not ISO 2-letter (US, GB, DE)
• key_format names the exact dot-separated dimension order imf_query_dataset requires
• Codelist previews are capped at 50 entries by default; set dimension_id to page one dimension with limit/offset (max 200), and codelist_filter applies before paging
• Set available_only=true to page codes the dataflow actually publishes, with series count and time coverage, instead of the full codelist
• A codelist_filter that matches nothing is reported distinctly from a codelist that could not be resolved — the two need opposite next steps

imf_query_dataset _tool

• Dot-separated key in DSD keyPosition order; + combines codes at one position, * matches every code there — every position needs a code or *, a blank segment is rejected
• start_period/end_period accept YYYY, YYYY-SN, YYYY-QN, YYYY-MM, or a calendar-valid YYYY-MM-DD; each bound covers its whole period (end_period: 2023 includes 2023-M12)
• Returns time_period, value, status, and series attributes (unit, scale, decimals); a key resolving to multiple series carries one series_metadata entry per series, since attributes can differ between them
• unit/scale are upstream codes (PT, USD, XDC, IX, NUM); a null unit means the dataflow publishes none, and scale "0" means no multiplier
• Large multi-country or long-range results automatically spill to DataCanvas (output_mode: "canvas" forces staging); staged reports storage, truncated reports only whether observations is an incomplete preview — a staged result can still be untruncated
• no_data errors carry availability context naming codes that do have coverage; a key with data entirely outside the requested range fails as no_data_in_range and reports the range that does

imf_dataframe_describe _tool

• Lists every table staged on a canvas, with row count and column schema (name + DuckDB type)
• Requires canvas_id from a prior imf_query_dataset call that returned staged: true
• Call before imf_dataframe_query to confirm table and column names

imf_dataframe_query _tool

• One read-only SQL SELECT per call; a leading WITH … SELECT common table expression is accepted, DML and DDL are rejected
• Results are capped first by the canvas row limit (default 10,000), then by a 100,000-character serialized response budget — row_count always equals the returned rows, and truncated: true means either cap trimmed the result
• Page past a cap with a stable ORDER BY plus LIMIT/OFFSET; response_too_large means even one row didn't fit and asks for fewer columns or aggregation
• Requires CANVAS_PROVIDER_TYPE=duckdb

imf_dataframe_drop _tool

• Removes one named table or view from a canvas without affecting the others; requires the exact name from imf_dataframe_describe
• Idempotent — a repeated or absent drop returns dropped: false rather than an error
• Disabled by default; set IMF_ENABLE_DATAFRAME_DROP=true to register it in tools/list

imf://database/{dataflow_id} _resource

• Bounded discovery metadata for one dataflow — every dimension with up to 50 codelist entries, counts, key_format, name, description
• dataflow_id comes from imf_list_databases
• Carries continuation metadata pointing to imf_get_database (with dimension_id/limit/offset) for a codelist beyond the preview

Features

Built on @cyanheads/mcp-ts-core: stdio and Streamable HTTP transports, pluggable auth (none / jwt / oauth), swappable storage (in-memory, filesystem, Supabase, Cloudflare KV/R2/D1), structured logging with optional OpenTelemetry tracing.

IMF-specific:

• Keyless access — no API key required; the IMF SDMX 3.0 portal is fully public
• Type-safe SDMX 3.0 compact JSON client with dimension/codelist parsing and DSD validation
• Key dimension count validated against the DSD before each query to catch format mismatches early
• Dataflow catalog and full availability constraints cached in-session to minimize round trips on multi-step workflows
• DuckDB-backed DataCanvas spill for large multi-country or long time-range observations

Agent-friendly output:

• Codelist entries carry both the machine code and human-readable label — agents can present meaningful names without a follow-up lookup
• key_format field in every dataflow response explicitly states the dimension order, removing guesswork for key construction
• Observations include status flags (e.g. E for estimate) so agents can communicate data quality caveats
• Canvas placement is explicit — staged distinguishes storage from truncated preview completeness, and staged results carry canvas_id, table_name, and retrieval guidance

Getting started

Public Hosted Instance

A public instance is available at https://imf.caseyjhand.com/mcp — no installation required. Point any MCP client at it via Streamable HTTP:

{  "mcpServers": {    "imf-mcp-server": {      "type": "streamable-http",      "url": "https://imf.caseyjhand.com/mcp"    }  }}

Self-Hosted / Local

No API key required. Add the following to your MCP client configuration file.

{  "mcpServers": {    "imf-mcp-server": {      "type": "stdio",      "command": "bunx",      "args": ["@cyanheads/imf-mcp-server@latest"],      "env": {        "MCP_TRANSPORT_TYPE": "stdio",        "MCP_LOG_LEVEL": "info"      }    }  }}

Or with npx (no Bun required):

{  "mcpServers": {    "imf-mcp-server": {      "type": "stdio",      "command": "npx",      "args": ["-y", "@cyanheads/imf-mcp-server@latest"],      "env": {        "MCP_TRANSPORT_TYPE": "stdio",        "MCP_LOG_LEVEL": "info"      }    }  }}

Or with Docker:

{  "mcpServers": {    "imf-mcp-server": {      "type": "stdio",      "command": "docker",      "args": [        "run", "-i", "--rm",        "-e", "MCP_TRANSPORT_TYPE=stdio",        "ghcr.io/cyanheads/imf-mcp-server:latest"      ]    }  }}

To enable SQL analytics over large result sets, add CANVAS_PROVIDER_TYPE=duckdb to the env block. Add IMF_ENABLE_DATAFRAME_DROP=true only when agents should be able to remove staged tables.

For Streamable HTTP, set the transport and start the server:

MCP_TRANSPORT_TYPE=http MCP_HTTP_PORT=3010 bun run start:http# Server listens at http://localhost:3010/mcp

Prerequisites

• Bun v1.4.0 or higher (or Node.js v24+).
• No API key required.

Installation

1. Clone the repository:

git clone https://github.com/cyanheads/imf-mcp-server.git

2. Navigate into the directory:

cd imf-mcp-server

3. Install dependencies:

bun install

4. Configure environment:

cp .env.example .env# edit .env as needed — no required vars for basic use

Configuration

See .env.example for the full list of optional overrides.

Running the server

Local development

• Build and run:

  sh

  bun run rebuild
  bun run start:stdio# orbun run start:http

• Run checks and tests:

  sh

  bun run devcheck   # Lint, format, typecheck, securitybun run test       # Vitest test suitebun run lint:mcp   # Validate MCP definitions against spec

Docker

docker build -t imf-mcp-server .docker run --rm -p 3010:3010 imf-mcp-server

The Dockerfile defaults to HTTP transport, stateless session mode, and logs to /var/log/imf-mcp-server. OpenTelemetry peer dependencies are installed by default — build with --build-arg OTEL_ENABLED=false to omit them.

Project structure

Development guide

See CLAUDE.md/AGENTS.md for development guidelines and architectural rules. The short version:

• Handlers throw, framework catches — no try/catch in tool logic
• Use ctx.log for request-scoped logging, ctx.state for tenant-scoped storage
• Register new tools and resources via the barrels in src/mcp-server/*/definitions/index.ts
• Wrap external API calls: validate raw → normalize to domain type → return output schema; never fabricate missing fields

Data source

Data is sourced from the International Monetary Fund SDMX 3.0 portal under the IMF Copyright and Terms of Use. The IMF's terms permit redistribution of statistical data with attribution. Each data-returning tool response includes a source field with the required attribution: Source: International Monetary Fund, <dataflow name>, https://data.imf.org/.

Contributing

Issues are welcome. Run checks and tests before submitting:

bun run devcheckbun run test

License

Apache-2.0 — see LICENSE for details.