A
licenseNot graded
qualityD
maintenanceUnified MCP server for discovering open datasets across Hugging Face, Zenodo, and Kaggle, with ranked search results and one-click Colab starter code generation.
1
MIT
Mozilla Data Collective
Server Details
Search the Mozilla Data Collective catalog of ethically sourced AI training datasets.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
3 toolsfetchFetch dataset detailsARead-onlyIdempotentInspect
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Dataset id or slug, as returned in the id field of search results. |
TDQS
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior; the description adds that the data is 'public' and enumerates which fields will be returned. This extends the annotation context without contradicting it.
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 entire description is one dense sentence: the operation, input key, and expected return fields are all front-loaded with no filler or repetition of the title.
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?
For a one-parameter read operation with full annotations, the description covers what is fetched and what is returned well enough. It omits explicit not-found or error behavior, but the absence of an output schema and low complexity keep this a minor gap.
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 coverage is 100% and the parameter description already states it is a dataset id or slug from search results. The tool description adds no new semantic detail for the parameter beyond what the schema provides.
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?
Description names a specific verb ('Fetch'), a bounded resource ('one Mozilla Data Collective dataset'), and the lookup key ('by id or slug'), then enumerates the returned details. This is distinguishable from siblings 'search' and 'list_filters' without needing their schemas.
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 makes clear this is for retrieving full details for a single known dataset, which implies a post-search or post-list step. It does not explicitly name when to prefer 'search' or 'list_filters', so it falls short of full exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_filtersList dataset filter valuesARead-onlyIdempotentInspect
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
A4.7/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds useful behavioral context: value types are abbreviations, labels are provided to spell them out, and matches are exact. It stops short of describing the output structure, but for a no-argument listing tool this is sufficient.
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?
Three sentences, front-loaded with the core purpose, and every sentence adds value: what is listed, the abbreviation nuance, and when to call it. There is no filler or repetition of schema information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-argument, read-only enumeration tool, the description is complete. It tells the agent what values will be returned, highlights the label/abbreviation distinction, and explains the appropriate usage timing. No output schema exists, but the description sufficiently conveys the content and purpose of the output.
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 zero parameters and the schema already reflects that with an empty properties object. The description redundantly but helpfully states 'Takes no arguments,' leaving no ambiguity. Since there are no parameters to document, this dimension is fully satisfied.
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 uses a specific verb-resource pairing: 'List every value the search tool's filters accept' and enumerates the exact categories (tasks, locales, licenses, formats, sort, date-range). This clearly distinguishes list_filters from the sibling tools fetch and search, which retrieve data rather than enumerate filter options.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly tells the agent when to use the tool: 'call this before filtering a search rather than guessing values.' It also warns that filter values are matched exactly, giving a concrete reason to use the tool rather than rely on assumptions. The instruction is direct and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchSearch datasetsARead-onlyIdempotentInspect
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | Result ordering. Defaults to 'relevance'; use 'newest' or 'size' only when the user asks for it. | |
| task | No | Restrict to these machine-learning tasks, e.g. ['ASR', 'TTS']. | |
| limit | No | Maximum number of results to return (1-25). | |
| query | Yes | Natural-language search query describing the datasets you are looking for, e.g. 'Spanish speech recordings for TTS training'. Descriptive phrases retrieve better than single keywords. | |
| format | No | Restrict to these file formats, e.g. ['WAV', 'MP3']. Values must match exactly (case-sensitive); call the list_filters tool to get the valid ones. | |
| isPaid | No | true returns only paid datasets, false only free ones. Omit to include both. | |
| locale | No | Restrict to these language/locale codes, e.g. ['sw', 'pt-BR']. Values must match exactly (case-sensitive); call the list_filters tool to get the valid ones. | |
| license | No | Restrict to these license abbreviations, e.g. ['CC0-1.0', 'CC-BY-4.0']. Values must match exactly (case-sensitive); call the list_filters tool to get the valid ones. | |
| hasSample | No | true returns only datasets that publish a downloadable sample, useful when the user wants to try data before committing. false behaves the same as omitting it. | |
| uploadDate | No | Restrict to datasets published within this recent window. | |
| sortDirection | No | Direction for the sort field. Only meaningful alongside sort='newest' or sort='size'. |
TDQS
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds meaningful behavioral context beyond those: the exact return shape ({id, title, url}) and the exact-match requirement for filter values, which is important operational detail for the agent.
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?
Three sentences, each earning its place: what the tool does, what it returns and how to continue with fetch, and the critical precondition for filtering. The information is front-loaded and tightly written.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 11 parameters, the schema fully covers them, and the description covers the essential non-schema context: return format, follow-up tool routing, and exact filter matching. With no output schema present, the explicit return shape fills the gap. Nothing critical is missing for an agent to invoke this correctly.
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 11 parameters thoroughly. The description's filter list adds a useful high-level summary but does not meaningfully enhance individual parameter semantics beyond what the schema already provides.
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 a specific action ('Search'), a specific resource ('Mozilla Data Collective catalog of AI training datasets'), and the natural-language query mechanism. It also distinguishes itself from siblings by noting that matching dataset ids go to fetch and that list_filters should be called for filter values.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit when-to-use guidance: use natural-language search to find datasets, call list_filters before filtering, and pass an id to fetch for full details. This effectively routes the agent between search, list_filters, and fetch without ambiguity.
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. Dates show when Glama detected each change.
3 tool updates
- First observed
fetch - First observed
list_filters - First observed
search
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TDQS
A4.5/5.0
Disambiguation5/5
Each tool has a clearly distinct purpose: list_filters enumerates accepted filter values, search returns matching dataset summaries, and fetch retrieves full details for a single dataset. There is no meaningful overlap or ambiguity between them.
Naming Consistency4/5
All names are lowercase imperative verbs and are easy to understand. list_filters follows a verb_noun pattern while search and fetch are single-word verbs, but the inconsistency is minor and does not hurt usability.
Tool Count5/5
Three tools is an appropriate, well-scoped size for a read-only catalog search and retrieval server. Each tool serves a necessary part of the discovery workflow: filter discovery, search, and detail fetch.
Completeness5/5
The tool surface fully covers the stated domain: discover available filters, search the catalog, and fetch detailed dataset information. There are no obvious dead ends or critical missing operations for this read-only purpose.