CMS Provider Data Catalog MCP

A remote MCP server that lets an LLM (Claude, ChatGPT) explore, query, aggregate, and benchmark the ~234 datasets in the CMS Provider Data Catalog — hospitals, dialysis facilities, nursing homes, home health, hospice, physicians, and more — in plain language.

Runs as a Cloudflare Worker using the agents McpAgent. The PDC API (DKAN) is read-only and unauthenticated, so the Worker is a thin, stateless proxy.

What it can do: find datasets by category or keyword → inspect their columns (with CMS's human labels) → filter/sort individual rows → run GROUP BY aggregates (averages, counts, rankings) → compare one facility against its state and national benchmarks — all read-only.

• 📣 See MARKETING.md for capabilities and example questions users can ask.

• 📝 See CHANGELOG.md for version history.

Discoverability

So a client (and the user) can tell at a glance what's available:

• Server instructions — the server advertises its 10 provider-type categories and the recommended workflow on connect, so the model knows its scope without any tool call.

• pdc://catalog resource — the browsable category map (themes, dataset counts, examples) as ambient context for clients that support MCP resources.

• explore_cms_data prompt — one-click "what CMS data can I explore?" for the user.

• Typed output schemas — every tool declares an outputSchema and returns structuredContent, so clients (e.g. ChatGPT dev mode) can parse and render results reliably instead of re-reading JSON.

Tools

Intended workflow the tool descriptions steer the model toward: list_categories → search_datasets → get_dataset → get_dataset_schema → query_dataset / aggregate_dataset / compare_to_benchmarks.

compare_to_benchmarks computes benchmarks as simple averages over the distribution's own rows (transparent, in 3 upstream calls) — not CMS's separately published risk-adjusted State/National Averages datasets, whose columns don't map 1:1 to facility columns. Those remain queryable directly via the normal tools.

Aggregation uses DKAN's structured query (expression + groupings), not SQL — DKAN's SQL endpoint doesn't support GROUP BY. Numeric columns stored as text are cast automatically, and metric values are returned as numbers.

The full dataset list (used by list_categories and the catalog resource) is cached in-isolate for 10 minutes, so discovery is a single upstream call.

Develop

npm install
npm run dev          # wrangler dev, serves /mcp and /sse locally
npm run typecheck

Local smoke test (Streamable HTTP): POST an initialize to http://localhost:8787/mcp, capture the mcp-session-id response header, send notifications/initialized, then tools/call.

Deploy

npm run deploy       # wrangler deploy

This creates the Durable Object (used by McpAgent for per-session state) on first deploy.

Connect a client

After deploy you'll have a URL like https://cms-pdc-mcp.<subdomain>.workers.dev.

• Streamable HTTP (preferred): https://.../mcp

• SSE (legacy clients): https://.../sse

Add it as a custom connector in Claude, or via Developer Mode / connectors in ChatGPT. No auth is required.

Reliability & ops

All upstream calls to CMS go through one hardened req() helper (src/pdc.ts):

• Retries with backoff on transient failures (network errors, 5xx, 429); fails fast on 4xx.

• Bounded timeout (20s) with a clear timeout error rather than a hang.

• Clean error messages — DKAN's { message } is surfaced (e.g. "Column not found.") instead of a raw JSON blob.

• Short-TTL GET caching (60s, Cloudflare Cache API) so repeated identical reads within a conversation don't re-hit CMS. POST queries/aggregations are always fresh.

• Structured logs ({"at":"pdc",method,path,status,ms,cache}) surface in Workers observability (enabled in wrangler.jsonc).

Column descriptions (data dictionaries)

Sentence-level column descriptions are built once, locally and committed as JSON — the Worker never parses anything at runtime.

data_dictionaries/*.pdf  ──►  npm run build:dictionaries  ──►  src/dictionaries/*.json  ──►  git push ──► wrangler deploy
   (gitignored, local)         (one-time, local, pdftotext)       (committed)                              (imports JSON at bundle time)

• Source files (data_dictionaries/) are gitignored — the PDFs are never pushed.

• npm run build:dictionaries extracts { normalizedLabel: description } into src/dictionaries/<provider>.json and a merged descriptions.json. This runs pdftotext locally; it is not part of deploy.

• The Worker imports descriptions.json, which esbuild inlines into the bundle. At runtime get_dataset_schema does an in-memory label lookup — no PDF parsing, no reprocessing on push.

• src/dictionaries/overrides.json holds hand-authored fixes keyed by CMS label. They always win and are never overwritten by the build, so re-running it can't clobber manual work. To improve coverage: add lines to overrides.json, run npm run build:dictionaries, commit.

Coverage is partial and per-provider (dialysis ~46% after overrides; the hospital dictionary is a narrative spec that doesn't table-parse) — fill gaps via overrides.json.

Notes / next steps

• Read-only. Only GET/POST query endpoints of the PDC API are used; nothing writes.

• Raw SQL (/datastore/sql) is intentionally not exposed — structured queries only, to keep the model from writing broken/expensive queries against DKAN's bracketed SQL dialect.

• Distribution UUIDs change when CMS republishes a dataset, so always resolve them via get_dataset rather than caching them.

• Column labels come for free. DKAN stores each column's original CSV header as the field's description, so get_dataset_schema returns a human label for every column of all datasets (e.g. mortality_rate_upper_confidence_limit_975 → "Mortality Rate: Upper Confidence Limit (97.5%)") with no PDF parsing.

• Richer, sentence-level descriptions are built locally from data_dictionaries/ and attached by get_dataset_schema via a conservative label match (a missing description beats a wrong one). See Column descriptions above for the full workflow.

Roadmap

• ✅ Discovery, search, schema, structured queries

• ✅ Human column labels + typed output schemas

• ✅ Aggregation & insights (aggregate_dataset)

• ✅ Benchmark comparison (compare_to_benchmarks)

• ✅ Reliability & ops (retries, caching, clean errors, logs)

• 🟡 Rich column descriptions — mechanism live and wired into get_dataset_schema; dialysis (~39%) and physician auto-extracted from data_dictionaries/. Remaining providers use different PDF layouts (hospital is a narrative spec) and need bespoke extraction or hand-authoring; coverage improves by editing the committed src/dictionaries/*.json.