.codex-plugin— mcp server

<div align="center"> <h1>@cyanheads/treasury-fiscaldata-mcp-server</h1> <p><b>Query US Treasury national debt, interest rates, exchange rates, and fiscal datasets via MCP. STDIO or Streamable HTTP.</b> <div>7 Tools</div> </p> </div>

<div align="center">

</div>

<div align="center">

</div>

<div align="center">

Public Hosted Server: https://treasury-fiscaldata.caseyjhand.com/mcp

</div>

Tools

Five tools for querying the US Treasury Fiscal Data API, plus two for SQL analytics over DuckDB-backed DataCanvas dataframes:

treasury_list_datasets

Browse the embedded catalog of available Treasury Fiscal Data endpoints. No network calls — serves from a static catalog bundled with the server.

• Filter by category: debt, interest_rates, exchange_rates, revenue_spending, savings_bonds, securities, other
• Keyword search against dataset name and description (case-insensitive substring)
• Returns endpoint paths, field names, types, and update cadence
• Use this first to get the exact endpoint path and field names before calling treasury_query_dataset

treasury_query_dataset

Generic parameterized query against any Treasury Fiscal Data endpoint.

• Filter syntax: { field, operator, value } where operator is eq, gt, gte, lt, lte, in
• Multiple filters ANDed together
• Pagination via page_size (1–10000) and page_number
• Sort by any field, descending with - prefix (e.g. -record_date)
• All response values are strings per the API contract — including numeric and date fields; "null" means no value
• Pass canvas_id to register results into a named DataCanvas dataframe for SQL via treasury_dataframe_query (requires CANVAS_PROVIDER_TYPE=duckdb)

treasury_get_debt

Convenience tool for national debt (Debt to the Penny) — total public debt outstanding broken into publicly-held debt and intragovernmental holdings.

• mode=latest — most recent business-day record
• mode=date — specific business day (YYYY-MM-DD; API only records debt on market-open days)
• mode=series — date range, sorted newest-first; auto-spills to DataCanvas when the series exceeds 500 rows
• Records go back to 1993-01-04

treasury_get_interest_rates

Average interest rates the Treasury pays on outstanding securities. Updated monthly (end-of-month records).

• Covers Bills, Notes, Bonds, TIPS, Floating Rate Notes (FRN), and aggregate marketable/non-marketable totals
• mode=latest — most recent month's rates for all or one security type
• mode=series — time-range history; auto-spills to DataCanvas when results exceed 200 rows

treasury_get_exchange_rates

Official Treasury statutory reporting exchange rates for ~130 countries, published quarterly (March 31, June 30, Sep 30, Dec 31).

• Rate expressed as foreign currency units per 1 USD (e.g. Japan-Yen 159.41 means 1 USD = 159.41 JPY)
• These are not market exchange rates — required by US federal agencies for foreign-currency-to-USD conversions in official reporting
• Filter to one or more countries by exact name; omit for all ~130 countries in a quarter
• mode=series auto-spills to DataCanvas when results exceed 500 rows (~18,800 rows full history)

treasury_dataframe_describe / treasury_dataframe_query

In-conversation SQL analytics over the dataframes that treasury_query_dataset, treasury_get_debt, treasury_get_interest_rates, and treasury_get_exchange_rates materialize on a shared DuckDB-backed DataCanvas. Each data-returning call with canvas_id adds a df_XXXXX_XXXXX handle; pass that handle to treasury_dataframe_query for joins, aggregates, window functions, and CTEs — standard DuckDB SQL.

• Read-only. Writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected by the SQL gate. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied at the bridge layer.
• All Treasury columns are VARCHAR. CAST to DECIMAL or DATE for arithmetic and date comparisons.
• `register_as` chaining. treasury_dataframe_query can persist its result as a new dataframe with a fresh TTL for multi-step analysis.
• Per-table TTL. Dataframes age on their own clock (default 24h, override with CANVAS_TTL_MS).
• Requires CANVAS_PROVIDER_TYPE=duckdb.

Features

Built on @cyanheads/mcp-ts-core:

• Declarative tool definitions — single file per tool, framework handles registration and validation
• Structured output schemas with automatic formatting for human-readable display
• Unified error handling — handlers throw, framework catches, classifies, and formats
• Pluggable auth: none, jwt, oauth
• Structured logging with request-scoped context
• STDIO and Streamable HTTP transports

Treasury-specific:

• Curated catalog of 17 Treasury Fiscal Data endpoints with field metadata — no discovery round-trip required. Pass any endpoint path directly to treasury_query_dataset to access datasets not in the catalog.
• Convenience tools for the three most-queried datasets (national debt, interest rates, exchange rates)
• Full generic access to any Fiscal Data endpoint via treasury_query_dataset
• DataCanvas integration: large time-series pulls register as df_<id> dataframes queryable via DuckDB SQL
• No API keys required — the US Treasury Fiscal Data API is free and public

Agent-friendly output:

• Filter expression echo (applied_filters) so agents can verify what was sent to the API
• Field-label maps on query results (field_labels) map raw field names to human-readable labels
• Enrichment notices on empty results and partial-country mismatches guide the next tool call
• Canvas provenance: source tool, original query parameters, row count, and column schema surfaced by treasury_dataframe_describe

Getting started

Public Hosted Instance

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

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

Self-Hosted / Local

Add the following to your MCP client configuration file.

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

Or with npx (no Bun required):

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

Or with Docker:

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

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

DataCanvas SQL workflow

For large time-series pulls or multi-dataset analysis, use the DataCanvas SQL workflow:

1. Set `CANVAS_PROVIDER_TYPE=duckdb` in your server environment.
2. Call a data tool with a `canvas_id` — e.g., treasury_get_debt with mode=series and a canvas_id value, or treasury_query_dataset with canvas_id. The tool registers the results as a df_XXXXX_XXXXX dataframe and returns the table name.
3. Inspect the schema with treasury_dataframe_describe — lists column names, types (all VARCHAR for Treasury data), row count, and TTL.
4. Query with SQL via treasury_dataframe_query — standard DuckDB SELECT with joins, aggregates, window functions, and CTEs. CAST VARCHAR columns to DECIMAL or DATE for arithmetic.

-- Example: debt trend over the last year, month-end records only
SELECT
  record_date,
  CAST(tot_pub_debt_out_amt AS DECIMAL) / 1e12 AS total_debt_trillions
FROM df_xxxxx
WHERE CAST(record_date AS DATE) >= CURRENT_DATE - INTERVAL 1 YEAR
ORDER BY record_date DESC

Prerequisites

• Bun v1.3.0 or higher (or Node.js v24+).
• No API key required — the US Treasury Fiscal Data API is free and public.
• For DataCanvas SQL: CANVAS_PROVIDER_TYPE=duckdb (DuckDB is bundled as @duckdb/node-api).

Installation

1. Clone the repository:

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

1. Navigate into the directory:

cd treasury-fiscaldata-mcp-server

1. Install dependencies:

bun install

1. Configure environment:

cp .env.example .env
# edit .env as needed — no required vars; CANVAS_PROVIDER_TYPE=duckdb to enable SQL

Configuration

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

Running the server

Local development

• Build and run:

  bun run rebuild

  bun run start:stdio
  # or
  bun run start:http

• Run checks and tests:

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

Docker

docker build -t treasury-fiscaldata-mcp-server .
docker run --rm -e CANVAS_PROVIDER_TYPE=duckdb -p 3010:3010 treasury-fiscaldata-mcp-server

The Dockerfile defaults to HTTP transport, stateless session mode, and logs to /var/log/treasury-fiscaldata-mcp-server. DuckDB native modules are pre-built in the build stage and copied to the production stage — no extra build tools required at runtime. OpenTelemetry peer dependencies are installed by default — build with --build-arg OTEL_ENABLED=false to omit them.

Project structure

Development guide

See CLAUDE.md and 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
• All Treasury API values are strings — validate and CAST in downstream SQL; never fabricate missing fields
• Register new tools via the arrays in src/index.ts

Contributing

Issues and pull requests are welcome. Run checks and tests before submitting:

bun run devcheck
bun run test

License

Apache-2.0 — see LICENSE for details.