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dataville-mcp

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dataville-mcp

Glama MCP server score M8ven Score npm CI License: MIT

Install in Cursor Install in VS Code

MCP server exposing Dataville's data source API as tools for MCP clients (Claude Desktop, Claude Code, etc.).

Dataville is a unified REST API over ten public datasets — Wikipedia, arXiv, Project Gutenberg, US Census, USDA FoodData, Papers with Code, SEC EDGAR, OpenAlex, PyPI, and Stack Exchange — behind one interface and one API key, with CSV/Parquet export and SQL query support. This package lets an MCP client search any of those sources as a tool call.

Requires a Dataville API key — get one from the Dataville dashboard.

Docs: docs.dataville.com · Connect an AI tool · Data sources. Examples: dataville-wikimedia, dataville-sec-edgar, company-intelligence-agent. Live demos: app.dataville.com/demos.

Tools

Tool

What it does

list_dataville_sources

Every source, with the SQL tables (if any) you can query for it.

describe_dataville_source

One source's keyword format, a working example, and its tables' columns: { source }.

search_dataville

Look one thing up by keywords and get the best match: { source, keywords, summary? }.

query_dataville

Read-only SQL SELECT over the stored tables for lists, filters, counts, joins, and paging with LIMIT/OFFSET: { sql }.

All four only read data. query_dataville covers every stored source except census and news, which are fetched live per search. It returns at most 1,000 rows per call and is billed per row returned. It reads Dataville's stored copy: some sources are loaded in bulk, others only hold records fetched before, so for one specific item search_dataville is the reliable route.

Related MCP server: Exasol MCP Server

What you can ask

Once connected, ask in plain language and the client picks the source:

  • "Find an arXiv paper on retrieval-augmented generation and summarize it."

  • "What did Apple report in its latest SEC filing?"

  • "What's the median household income in Travis County, Texas, per the US Census?"

  • "How much protein is in 100 g of cooked lentils, according to USDA FoodData?"

  • "Look up the requests package on PyPI — what's the latest version and license?"

  • "List ten Project Gutenberg books by Jane Austen, with their subjects."

  • "List five USDA FoodData entries with 'lentils' in the name, with their categories."

Setup

This package is a local (stdio) MCP server: the client launches it on your machine via npx. If you don't need it running locally, connecting to Dataville's hosted endpoint instead takes one line and no install.

Hosted, in one line (no install)

Dataville also serves MCP directly over HTTP, so a client can connect without running anything locally — no Node, no config file, no restart:

claude mcp add --transport http dataville https://api.dataville.com/mcp

That works with no credentials at all (anonymous limits). Add --header "Authorization: Bearer dataville_your_key_here" for the full quota. Cursor and VS Code users can use the install buttons at the top of this page. Other clients take the same URL; the app's Integrations page has the exact snippet for each. Use the hosted endpoint unless you specifically want to pin a version or work offline — the rest of this section covers that local setup.

Prerequisites

  • Node.js (LTS) installed — this is what runs npx. Without it the server fails to start. Check with node --version.

  • A Dataville API key — get one from https://app.dataville.com/api-keys.

The config block is the same everywhere; only where you put it differs:

{
  "mcpServers": {
    "dataville": {
      "command": "npx",
      "args": ["-y", "@dataville/dataville-mcp"],
      "env": {
        "DATAVILLE_API_KEY": "dataville_your_key_here"
      }
    }
  }
}

No install step needed — npx fetches and runs the package on demand.

Claude Desktop

  1. Open Settings → Developer → Edit Config. This opens claude_desktop_config.json:

    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

    • Windows: %APPDATA%\Claude\claude_desktop_config.json

  2. Add the block above (merge into mcpServers if the file already has one), with your real key.

  3. Fully quit and reopen Claude Desktop — quit from the menu bar / system tray, not just closing the window.

  4. The dataville tools now appear under the tools icon in the chat box, and Settings → Developer shows dataville running.

Note: the server appears as tools, not in the Connectors directory — that directory only lists remote (hosted) connectors and will not find a local server. Ask naturally ("get Apple's latest revenue from dataville") and the client calls the tool.

Claude Code

claude mcp add dataville -e DATAVILLE_API_KEY=dataville_your_key_here -- npx -y @dataville/dataville-mcp

Restart the session so the tools load. Add -s user to make it available in every project instead of just the current one.

Configuration

DATAVILLE_API_BASE_URL is optional and defaults to https://api.dataville.com; set it to http://localhost:5000 to point at a local backend during development.

Where your API key goes

The server reads DATAVILLE_API_KEY from its environment and sends it only as an Authorization: Bearer header to DATAVILLE_API_BASE_URL, which is https://api.dataville.com unless you change it. It never logs the key or sends it anywhere else. The base URL must use https. Plain http is accepted only for localhost, 127.0.0.1 and [::1], so the key never crosses the network unencrypted.

All four tools are read-only: they search and query Dataville and never create, change or delete anything.

Running from source

git clone https://github.com/datavilleorg/dataville-mcp.git
cd dataville-mcp
npm install
npm run build

Check that it works

Ask your client one of these. Each answer is checkable on purpose — a model that skipped the tool and answered from memory sounds just as confident, so a reply on its own proves nothing.

Ask

What proves it

Which data sources does Dataville have?

Calls list_dataville_sources and names all eleven.

Using Dataville, what is the latest version of the requests package on PyPI?

A version you can confirm on pypi.org — and it moves, so it can't come from memory.

Using Dataville, get the latest SEC filing for AAPL and its revenue.

A form type, filing date, revenue figure, and a sec.gov link to open.

Using Dataville, how much protein is in 100g of uncooked quinoa?

The exact USDA figure, 14.1 g per 100 g.

Using Dataville SQL, list 5 Project Gutenberg books by Mark Twain.

Calls query_dataville and returns titles with Gutenberg IDs you can open at gutenberg.org/ebooks/.

Clients show when a tool ran. If you don't see that, say "use dataville" in the prompt to make it explicit, and check the answer against the source.

Development

npm run dev    # tsx watch
npm test       # node test runner
npm run build  # tsc

Releasing

Publishes run from CI. npm uses trusted publishing (OIDC), so no npm token is stored; the MCP Registry step signs in with the MCP_PRIVATE_KEY repo secret. To cut a release: bump the version, update CHANGELOG.md, then publish a GitHub Release for the new tag. The Publish workflow builds, tests, and publishes to npm, then publishes server.json to the MCP Registry (its version is set from package.json, so don't bump it by hand).

The registry name com.dataville/dataville-mcp is authorized by a DNS TXT record on the dataville.com apex (v=MCPv1; k=ed25519; p=<public key>), checked against the Ed25519 private key in the MCP_PRIVATE_KEY repo secret (hex). If the mcp-registry job fails at login, check that record and secret, then re-run just that job.

Available Tools

2 tools
list_dataville_sourcesList Dataville data sourcesAInspect

List the data sources available through Dataville's search_dataville tool.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states 'List' without confirming read-only behavior, output format, or potential limitations. This minimal information provides little transparency beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, compact sentence with no filler. It efficiently communicates the tool's purpose without unnecessary words or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no output schema), the description sufficiently conveys the core purpose. It could optionally detail what information is included in the list of sources, but the current description is adequate for the tool's scope.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and the schema is empty. The baseline is 4, and the description needs to add no parameter details since there are none to explain.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List') and the resource ('data sources'), and explicitly ties them to Dataville's search_dataville tool. This distinguishes it from the sibling search tool, which focuses on querying, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The implication is that listing data sources is a precursor to using search_dataville, but the description does not explicitly say when to use this tool versus search_dataville or mention any exclusions. Context is present but not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_datavilleSearch a Dataville data sourceAInspect

Query one of Dataville's data sources (see list_dataville_sources for valid source names) with a keyword string and optional query params.

ParametersJSON Schema
NameRequiredDescriptionDefault
paramsNoOptional additional query parameters
sourceYesData source name, e.g. 'wikipedia', 'arxiv', 'edgar'
keywordsYesSearch keywords or identifier for the query

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It clearly states the tool is a query operation, implying no destructive behavior. It does not detail potential behaviors like pagination, rate limits, or result limits, which would elevate transparency. With no annotations, this is adequate but minimal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that efficiently delivers the core purpose, references the sibling tool for additional context, and mentions both the keyword and optional params. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters (100% schema coverage), no output schema, and no annotations, the description is reasonably complete for a simple query tool. It tells the agent what to query and where to find valid sources. However, it lacks information about what the output looks like (e.g., result format) which would be needed for full completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters. The description adds minimal value beyond the schema, only hinting that 'keywords' is a search string and 'params' are optional. It does not explain how params are used or provide examples. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Query'), resource ('one of Dataville's data sources'), and key components (keyword string, optional query params). It distinguishes itself from the sibling tool by referencing list_dataville_sources for valid source names.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says to use list_dataville_sources to find valid source names, providing clear context for when to use this tool. However, it does not exclude any scenarios or mention when not to use it, missing a point for a perfect score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv0.1.0
    • First observedlist_dataville_sources
    • First observedsearch_dataville
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one lists available data sources, the other queries a specific source. There is no overlap or ambiguity.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with the 'dataville' prefix ('list_dataville_sources', 'search_dataville'), making them predictable and easy to understand.

Tool Count2/5

With only 2 tools, the server is severely under-scoped for a data platform. While listing and searching are core, many essential operations (e.g., adding/removing sources, metadata exploration) are missing, making the count feel inadequate.

Completeness1/5

The tool surface is extremely bare. Missing lifecycle operations for sources, lack of search result pagination or filtering, and no way to inspect source schemas or metadata. Agents will hit dead ends quickly.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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