# Mozilla Data Collective

> Search the Mozilla Data Collective catalog of ethically sourced AI training datasets.

- name: com.mozilladatacollective/datasets
- version: 0.1.0
- connection class: R0 (autonomous)
- trust: 79/100 flags: no-repository
- quality: 96/100 (strong)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/com.mozilladatacollective%2Fdatasets
- tools: https://api.protogrid.dev/v1/servers/com.mozilladatacollective%2Fdatasets/tools

**An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe.

## Connection (mcpServers)

```json
{
  "mcpServers": {
    "datasets": {
      "type": "http",
      "url": "https://mozilladatacollective.com/api/mcp"
    }
  }
}
```


## Trust

- hygiene: 80
- liveness: 99
- freshness: 85
- provenance: 55
- drivers: +dns-namespace +website +reachable +uptime-97% ~updated-43d-ago -no-repository
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 96/100 (strong)

### protocol: 88 (weight 25)

- pass `protocol.modern`: supports 2026-07-28 (server/discover)
- pass `protocol.stateless`: answers without a session
- pass `protocol.transport`: streamable HTTP
- warn `protocol.list_ttl`: tools/list declares ttlMs 0, so clients re-fetch it on every use

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 100 (weight 30)

- pass `tools.descriptions`: every tool has a description
- pass `tools.description_length`: descriptions are concise
- pass `tools.schemas`: every tool has an object input schema
- pass `tools.annotations`: 3 of 3 tools declare readOnlyHint or destructiveHint
- pass `tools.directory_hints`: every tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
- pass `tools.token_cost`: about 976 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: 100 (weight 15)

- pass `stability.changes`: no tool changes in 30 days
- pass `stability.rug_pull`: no tool changed its meaning under the same name

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package

- tools: 3, about 976 tokens to load them all
- tool set hash: d7b3e6e12208462f542a13bf075bafef
- badge: [![protogrid quality](https://protogrid.dev/badge/com.mozilladatacollective/datasets.svg)](https://protogrid.dev/servers/com.mozilladatacollective/datasets)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

_No tool definition changes recorded._

## Remotes

- https://mozilladatacollective.com/api/mcp (streamable-http, auth none, reachable true, uptime30d 0.974359)

## Packages

_none_

## Tools (3)

- `fetch`: Fetch the full public details of one Mozilla Data Collective dataset by id or slug: description, organization, task, locale, license, format, size, pricing, and its page URL.
- `list_filters`: List every value the search tool's filters accept: the tasks, locales, licenses and formats present in the catalog, plus the sort and date-range options. Task and license values are abbreviations, so taskLabels and licenseLabels spell them out. Filter values are matched exactly, so call this before filtering a search rather than guessing values. Takes no arguments.
- `search`: Search the Mozilla Data Collective catalog of AI training datasets by natural-language query, optionally narrowed by task, language, license, format, price, sample availability or publish date. Returns matching datasets as {id, title, url}; pass an id to the fetch tool for full details. Call list_filters first if you intend to filter — filter values must match the catalog exactly.