Provides tools for interacting with the Hugging Face Dataset Viewer API, allowing users to search datasets, fetch metadata, retrieve rows, and perform full-text search or SQL-like filtering on dataset content.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@HF Dataset MCP Search for the top 5 most downloaded text classification datasets"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for the Hugging Face Dataset Viewer API. Search datasets, fetch rows, filter data, and more.
npx @cfahlgren1/hf-dataset-mcpRelated MCP server: Hugging Face MCP Server
Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"hf-datasets": {
"command": "npx",
"args": ["-y", "@cfahlgren1/hf-dataset-mcp"],
"env": {
"HF_TOKEN": "hf_..."
}
}
}
}Variable | Description |
| Hugging Face API token (required for private/gated datasets) |
| Custom Dataset Viewer API URL (default: |
search_datasetsFind datasets on the Hugging Face Hub by name, tag, or author.
search_datasets(search?: string, author?: string, filter?: string[], sort?: string, limit?: number)validate_datasetCheck if a dataset is accessible and which viewer features are available.
validate_dataset(dataset: string)list_splitsGet all available configurations and splits for a dataset.
list_splits(dataset: string)get_dataset_infoGet the schema, metadata, and row counts for a dataset configuration.
get_dataset_info(dataset: string, config: string)get_rowsFetch a slice of rows from a dataset split.
get_rows(dataset: string, config: string, split: string, offset?: number, length?: number)search_datasetFull-text search within a dataset split using BM25 ranking.
search_dataset(dataset: string, config: string, split: string, query: string, offset?: number, length?: number)filter_rowsFilter dataset rows using SQL-like WHERE conditions.
filter_rows(dataset: string, config: string, split: string, where: string, orderby?: string, offset?: number, length?: number)WHERE syntax: Column names in double quotes, strings in single quotes. Supports =, <>, >, <, >=, <=, AND, OR, NOT.
Example: "label"=1 AND "text" LIKE '%hello%'
get_dataset_sizeGet row counts and byte sizes for all configs and splits.
get_dataset_size(dataset: string)list_parquet_filesGet URLs for the dataset's Parquet files for direct download or processing.
list_parquet_files(dataset: string)get_statisticsGet descriptive statistics for each column in a dataset split.
get_statistics(dataset: string, config: string, split: string)search_datasets(filter: ["task_categories:text-classification"], sort: "downloads", limit: 10)list_splits(dataset: "stanfordnlp/imdb")
get_dataset_info(dataset: "stanfordnlp/imdb", config: "plain_text")get_rows(dataset: "stanfordnlp/imdb", config: "plain_text", split: "train", offset: 0, length: 10)search_dataset(dataset: "stanfordnlp/imdb", config: "plain_text", split: "train", query: "amazing movie")filter_rows(dataset: "stanfordnlp/imdb", config: "plain_text", split: "train", where: "\"label\"=1", length: 10)MIT
Filter dataset rows using SQL-like WHERE conditions
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | Dataset ID (e.g., 'stanfordnlp/imdb') | |
| config | Yes | Configuration name | |
| split | Yes | Split name (train, test, validation) | |
| where | Yes | Filter condition (e.g., "age">30 AND "city"='Paris'). Column names in double quotes, strings in single quotes. | |
| orderby | No | Sort column and direction (e.g., "score" DESC) | |
| offset | No | Result offset (default: 0) | |
| length | No | Number of results (default: 100, max: 100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Omits critical behavioral details: whether operation is read-only (implied but not confirmed), pagination behavior (offset/length exist in schema but result handling isn't described), maximum result limits, or error behavior for invalid SQL syntax.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise (6 words) and front-loaded with the core action. No wasted sentences. However, brevity sacrifices structural elements like prerequisite context or usage conditions that would help an agent navigate the 7-parameter interface.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Inadequate for a 7-parameter filtering tool with no output schema. Missing: return value structure, relationship between required parameters (dataset/config/split), error handling for malformed SQL, and guidance on result cardinality. Schema documents parameters but description doesn't integrate them into operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. Description adds 'SQL-like' framing which reinforces the syntax requirements for the 'where' parameter, but adds no context for the dataset/config/split relationship or pagination semantics beyond what the schema explicitly documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States specific verb ('Filter'), resource ('dataset rows'), and method ('SQL-like WHERE conditions'). The 'SQL-like' hint helps distinguish from sibling 'get_rows' (likely raw retrieval) and 'search_dataset' (likely text search), though it doesn't explicitly articulate these distinctions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides no guidance on when to use this versus siblings like 'get_rows' or 'search_dataset'. Does not mention prerequisites (e.g., needing dataset/config/split identifiers from other tools) or when filtering is preferable to retrieving full datasets.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the schema, metadata, and row counts for a dataset configuration
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | Dataset ID (e.g., 'stanfordnlp/imdb') | |
| config | Yes | Configuration name (from list_splits) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. 'Get' implies read-only safety. Discloses return content (schema, metadata, counts) but omits output format, caching behavior, performance characteristics, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single efficient sentence, front-loaded with action and deliverables. No redundancy or waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for 2-parameter read tool: describes return values to compensate for missing output schema. Could strengthen by clarifying relationship to `list_splits` prerequisite and distinguishing from statistical/size siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions (including example format for dataset ID and provenance hint for config). Description adds no param-specific guidance, warranting baseline 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States specific deliverables (schema, metadata, row counts) and target resource (dataset configuration). Lacks explicit differentiation from siblings like `get_dataset_size` or `get_statistics` which may also return counts/stats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides no guidance on when to prefer this over siblings (e.g., `get_dataset_size` for just size, `list_splits` to first discover configs). No workflow context despite the `config` parameter implying dependency on `list_splits`.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get row counts and byte sizes for all configs and splits
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | Dataset ID (e.g., 'stanfordnlp/imdb') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable scope information ('all configs and splits') indicating the tool aggregates data across configurations, but fails to disclose safety characteristics, computational cost, rate limits, or return value structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It front-loads the action ('Get') and immediately qualifies what is retrieved and for what scope, earning its brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter tool with complete schema coverage, the description adequately covers the tool's purpose. While the lack of output schema could warrant description of return structure, the straightforward nature of 'size' retrieval makes the description sufficiently complete, though not exemplary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, the schema adequately documents the single 'dataset' parameter with a helpful example. The description adds no additional parameter context, but given the complete schema coverage, it meets the baseline expectation without needing to compensate for missing documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and clear resources ('row counts and byte sizes'), and importantly specifies scope ('for all configs and splits'). However, it does not explicitly distinguish from close siblings like get_statistics or get_dataset_info, leaving the agent to infer the difference.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like get_statistics, get_dataset_info, or get_rows. While the name suggests size retrieval, there is no explicit 'when-to-use' or 'when-not-to-use' guidance regarding overlaps with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a slice of rows from a dataset split
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | Dataset ID (e.g., 'stanfordnlp/imdb') | |
| config | Yes | Configuration name (from list_splits) | |
| split | Yes | Split name (train, test, validation) | |
| offset | No | Row index to start from (default: 0) | |
| length | No | Number of rows to fetch (default: 100, max: 100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Mentions 'slice' implying pagination, but fails to disclose: return format/structure, behavior when offset exceeds dataset bounds, performance implications of large offsets, or whether operation is read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single 9-word sentence is efficiently structured and front-loaded with the verb. No filler content. However, extreme brevity results in under-specification for a 5-parameter tool with pagination logic.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 100% schema coverage, input parameters are adequately documented. However, no output schema exists, and description fails to specify row format, return structure, or error conditions. Relationship to prerequisite tools (list_splits) is unexplained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing complete documentation for all 5 parameters. Description adds minimal semantic value beyond the schema, merely echoing 'slice' which aligns with offset/length parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb 'Fetch' and resource 'rows from a dataset split'. Specifies 'slice' implying contiguous pagination, which subtly distinguishes from sibling 'filter_rows'. However, lacks explicit contrast with alternatives like 'search_dataset' or 'filter_rows'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides no guidance on when to use this versus siblings (filter_rows, search_dataset) or prerequisites (e.g., that config/split values likely come from list_splits). No error handling or typical use case patterns mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get descriptive statistics for each column in a dataset split
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | Dataset ID (e.g., 'stanfordnlp/imdb') | |
| config | Yes | Configuration name | |
| split | Yes | Split name (train, test, validation) |
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. While 'descriptive statistics' hints at the operation type (mean, std, etc.), it fails to specify the exact statistics returned, whether the operation is cached, computational cost for large datasets, or the output format/structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The single sentence is front-loaded with the action verb and contains zero redundancy. However, given the absence of annotations and output schema, the extreme brevity leaves significant behavioral context undocumented, preventing a score of 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 3 well-documented parameters (100% schema coverage) and a clear core purpose, the description is minimally viable. However, lacking annotations, output schema, and specifics about what statistics are computed, it leaves gaps in the agent's understanding of the tool's full capabilities and return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, establishing a baseline of 3. The description adds value by contextualizing the parameters as targeting a specific 'dataset split' and operating on 'each column' within it, which helps the agent understand the scope of the analysis beyond the individual parameter definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verb 'Get' with clear resource 'descriptive statistics' and scope 'for each column in a dataset split'. It effectively distinguishes from siblings like get_dataset_info (general metadata) and get_rows (raw data) by specifying statistical aggregation at the column level.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like get_dataset_info or list_splits. It does not mention prerequisites (e.g., knowing valid config/split names) or when this might be computationally expensive versus lightweight alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get URLs for the dataset's Parquet files for direct download or processing
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | Dataset ID (e.g., 'stanfordnlp/imdb') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full behavioral disclosure burden. It states the return type (URLs) and format (Parquet), but omits critical operational details like URL expiration, pagination limits, or response structure (array vs object).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, efficient sentence with action-frontloaded structure ('Get URLs...'). No redundant phrases; every clause serves to specify the operation, target resource, or intended use case.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a single-parameter tool with complete schema coverage, but lacks output structure details (since no output schema exists) and doesn't clarify how results map to dataset splits or file organization.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage for the single 'dataset' parameter, which is well-documented with an example. The description does not add semantic depth beyond the schema, but meets the baseline expectation given the high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States specific action ('Get') and resource ('URLs for the dataset's Parquet files'), clearly distinguishing from sibling data-access tools like 'get_rows' or 'search_dataset' by specifying file-based download URLs rather than queried data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage context ('for direct download or processing') suggesting when to use it over data-querying tools, but lacks explicit 'when not to use' guidance or direct comparisons to siblings like 'get_rows'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get all available configurations and splits for a dataset
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | Dataset ID (e.g., 'stanfordnlp/imdb') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only implies a read-only operation via 'Get' but fails to disclose idempotency, return format, whether the operation is expensive, or what 'configurations' specifically refers to in this context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The single sentence is front-loaded with the verb 'Get' and contains no redundant or wasted words. However, it is so terse that it sacrifices helpful context that could have been added in a second sentence without harming conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter tool with complete schema coverage, the description is minimally viable. However, given the rich sibling ecosystem (filter_rows, get_rows, etc.), it should mention that this is typically used before fetching data to discover available splits, or describe the return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage (the 'dataset' parameter is fully documented in the schema with an example), the baseline is 3. The description adds no additional parameter context, constraints, or usage notes beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific resource terms 'configurations and splits' which clearly identifies this as a dataset metadata discovery tool for ML splits (train/test/val). While it doesn't explicitly name sibling alternatives, the specificity of 'configurations and splits' distinguishes it from generic 'dataset info' or 'statistics' tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states what the tool returns but provides no guidance on when to use it versus siblings like 'get_dataset_info' or prerequisites such as requiring a valid dataset ID first. No alternatives or exclusion criteria are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full-text search within a dataset split using BM25 ranking
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | Dataset ID (e.g., 'stanfordnlp/imdb') | |
| config | Yes | Configuration name | |
| split | Yes | Split name (train, test, validation) | |
| query | Yes | Text to search for | |
| offset | No | Result offset for pagination (default: 0) | |
| length | No | Number of results (default: 100, max: 100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite having no annotations, description only discloses the BM25 algorithm. It omits critical behavioral details: return format (scores? excerpts? row IDs?), pagination behavior (cursor vs. offset), rate limits, and what happens when queries match no documents.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single dense sentence front-loads the operation type and scope. However, given zero annotations and six parameters, extreme brevity becomes under-specification rather than efficient communication.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 6-parameter search tool with no output schema and multiple related siblings, one sentence is insufficient. Missing: return value structure, pagination strategy explanation, and differentiation from 'search_datasets' (plural) which appears in the same toolkit.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, parameters are self-documenting. Description adds semantic grouping by stating 'dataset split' (binding the dataset+config+split trio) and 'Full-text search' (relating to query), meeting the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description specifies exact action ('Full-text search'), target resource ('dataset split'), and ranking algorithm ('BM25'), clearly distinguishing from sibling 'search_datasets' (global vs. split-scoped) and 'filter_rows' (BM25 text ranking vs. filtering).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use full-text search versus 'filter_rows' (structured filtering) or 'get_rows' (direct access), nor prerequisites like needing valid dataset/config/split combinations from 'list_splits' first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find datasets on the Hugging Face Hub by name, tag, or author
| Name | Required | Description | Default |
|---|---|---|---|
| search | No | Query to match against dataset names and descriptions | |
| author | No | Filter by dataset owner (user or organization) | |
| filter | No | Tag filters (e.g., task_categories:text-classification, language:en) | |
| sort | No | Sort order for results | |
| direction | No | Sort direction (default: desc) | |
| limit | No | Max results to return (default: 20, max: 100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden of behavioral disclosure. It successfully identifies the external service (Hugging Face Hub) and search scope. However, it omits critical behavioral details: return format (list of metadata objects), pagination behavior (only limit is mentioned, no offset/cursor), and whether private datasets require authentication.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the action and resource, placing optional search dimensions at the end where they serve as supporting context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the rich input schema (6 well-documented parameters, 100% coverage) and lack of output schema, the description provides sufficient context for an AI to understand the tool's role. A minor gap remains regarding the return value structure, though the schema completeness reduces the burden on the description to explain inputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description adds value by mapping the conceptual search terms ('name, tag, or author') to the parameter purposes, helping the agent understand the relationship between the 'search', 'filter', and 'author' parameters. It could further clarify the 'filter' syntax (key:value pairs).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description provides a clear verb ('Find'), specific resource ('datasets on the Hugging Face Hub'), and search dimensions ('by name, tag, or author'). However, it does not distinguish from the sibling tool 'search_dataset' (if distinct) or clarify when to use this versus 'get_dataset_info' for known datasets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states what the tool does but provides no explicit guidance on when to use it versus alternatives like 'get_dataset_info' or 'filter_rows'. There is no mention of prerequisites, required auth, or when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check if a dataset is accessible and which viewer features are available
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | Dataset ID (e.g., 'stanfordnlp/imdb') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full disclosure burden. While it implies a read-only 'check' operation, it fails to define what 'accessible' means (existence vs. permissions), what specific 'viewer features' are evaluated, how errors are signaled, or what return structure to expect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no filler text. Key information (action, target, scope) is front-loaded and every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description inadequately fails to describe the return value structure or payload. It also omits clarification on how this validation differs from simply fetching dataset info.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage (the 'dataset' parameter includes a description and example), the baseline score applies. The tool description adds no parameter-specific context, but the schema is self-sufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses clear verbs ('Check') and identifies the specific resources and aspects being validated (dataset accessibility and 'viewer features'). However, it does not explicitly differentiate from the sibling tool 'get_dataset_info', which also retrieves dataset metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as 'get_dataset_info' or 'search_dataset', nor does it mention prerequisites or conditions for invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0filter_rowsget_dataset_infoget_dataset_sizeget_rowsget_statisticslist_parquet_fileslist_splitssearch_datasetsearch_datasetsvalidate_datasetScored across 10 tools
Each tool has a clearly distinct purpose with no overlap: filtering rows, getting metadata, fetching rows, searching datasets, etc. The descriptions clearly differentiate operations like get_rows vs. filter_rows vs. search_dataset, preventing misselection.
All tools follow a consistent verb_noun pattern with snake_case (e.g., filter_rows, get_dataset_info, list_splits). The naming is predictable and readable throughout the set, with no deviations in style.
With 10 tools, this is well-scoped for a dataset management server. Each tool serves a specific function in exploring, querying, and validating datasets, with no redundant or missing tools that would make the set feel too thin or bloated.
The toolset provides complete coverage for dataset operations: discovery (search_datasets, list_splits), inspection (get_dataset_info, get_statistics), access (get_rows, list_parquet_files), querying (filter_rows, search_dataset), and validation (validate_dataset). No obvious gaps exist for the domain.
This MCP server provides seamless access to Malaysia's government open data, including datasets, w…
Token-free MCP server for structured RevoGrid Core, Pro, and Enterprise knowledge retrieval.
Hosted MCP server for live public-data APIs and Skills for AI agents.
