Hugging Face Hub MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: there are separate tools for datasets vs. models, and within each category, tools for getting info, listing all, getting tags, and dataset-specific tools for Croissant metadata and parquet files. The descriptions make the boundaries explicit, preventing misselection.
Naming Consistency5/5
All tools follow a consistent 'hf_' prefix with verb_noun pattern (e.g., hf_get_dataset_info, hf_list_models). The naming is uniform across the set, using snake_case and clear verbs like 'get', 'list', and 'get' for specific actions, making it predictable and readable.
Tool Count5/5
With 8 tools, this server is well-scoped for interacting with the Hugging Face Hub. It covers core operations for datasets and models (info, listing, tags) plus dataset-specific features like Croissant and parquet files, avoiding bloat while providing essential functionality.
Completeness4/5
The tool set covers read operations comprehensively for datasets and models, including metadata, listings, tags, and file access. A minor gap is the lack of write operations (e.g., upload or modify datasets/models), but for a read-focused server, it handles the domain well with no dead ends.
Average 3.2/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
- No code scanning findings
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

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 states the tool retrieves metadata, implying a read-only operation, but doesn't disclose any behavioral traits such as authentication needs, rate limits, error handling, or what the output looks like (e.g., format, structure). This leaves significant gaps for an agent to understand how to use it effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences. The first sentence directly states the purpose, and the second adds context about Croissant. There's no wasted text, making it efficient, though it could be slightly more front-loaded by integrating the context into the purpose statement for a perfect score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simple retrieval), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what Croissant metadata entails, how it's returned, or any prerequisites. For a tool with no structured behavioral data, more detail is needed to ensure the agent can use it correctly without guesswork.
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?
The description adds no parameter semantics beyond what the input schema provides. The schema has 100% coverage with a clear description for 'repo_id' as 'Dataset repository ID.' Since the description doesn't explain parameters further, it meets the baseline of 3 where the schema does the heavy lifting, but doesn't add extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the Croissant metadata for a dataset.' It specifies the verb ('Get') and resource ('Croissant metadata'), and distinguishes it from siblings by focusing on metadata rather than dataset info, parquet files, or tags. However, it doesn't explicitly differentiate from all siblings (e.g., 'hf_get_dataset_info' might also provide metadata), keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. It mentions Croissant is 'a high-level format for machine learning datasets,' which implies usage for ML datasets, but doesn't specify when to choose this over siblings like 'hf_get_dataset_info' or 'hf_get_dataset_parquet.' There's no explicit when/when-not or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states what is retrieved without disclosing behavioral traits like rate limits, authentication needs, or response format. It adds minimal context beyond the basic operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose. It could be slightly more structured but wastes no words, earning its place clearly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is adequate for a read-only tool but lacks details on return values or error handling. It covers the basic operation but leaves gaps in behavioral context.
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 both parameters well. The description does not add meaning beyond the schema, such as examples of metadata or configuration details, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('detailed information for a specific model'), specifying what metadata is included. It distinguishes from siblings like hf_get_model_tags (tags only) and hf_list_models (listing vs. detailed info), though not explicitly named.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like hf_get_model_tags or hf_list_models is provided. The description implies usage for detailed model info but lacks context on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 states what information is retrieved but doesn't describe the response format, potential rate limits, authentication needs, or whether this is a read-only operation (though implied by 'Get'). For a tool with zero annotation coverage, this leaves significant behavioral gaps.
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, efficient sentence that front-loads the core purpose ('Get detailed information for a specific dataset') and adds useful specifics ('including metadata, files, configuration, and more') without any wasted words. Every part of the sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, 1 required) and lack of annotations or output schema, the description is minimally adequate. It covers the basic purpose but doesn't compensate for missing behavioral details or output expectations, leaving the agent to infer the full context from the schema alone.
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 three parameters thoroughly. The description adds no additional parameter semantics beyond what the schema provides, such as examples for 'repo_id' beyond the schema's 'squad' and 'imdb', or clarification on how 'full' interacts with the other parameters. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('detailed information for a specific dataset'), and it enumerates the types of information retrieved (metadata, files, configuration). However, it doesn't explicitly differentiate from sibling tools like 'hf_get_dataset_tags' or 'hf_get_dataset_parquet' that might provide more focused information subsets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. It doesn't mention sibling tools like 'hf_list_datasets' for browsing datasets or 'hf_get_dataset_tags' for just tags, nor does it specify prerequisites or contextual constraints for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions retrieving 'auto-converted parquet files' and optional filtering, but fails to disclose critical behaviors such as whether this is a read-only operation, potential rate limits, authentication requirements, or the format and structure of the returned list. This leaves significant gaps for an AI agent to understand how to use the tool effectively.
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 extremely concise with two sentences that directly convey the tool's function and optional capabilities. Every word earns its place, and it's front-loaded with the core purpose, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations and no output schema, the description is incomplete. It doesn't explain what the returned 'list of auto-converted parquet files' looks like (e.g., format, structure, or example output), nor does it cover behavioral aspects like error handling or performance characteristics. For a tool with 4 parameters and no structured output documentation, this leaves too much undefined.
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 input schema already documents all parameters thoroughly. The description adds minimal value by mentioning 'subset (config) and split' as optional filters, but doesn't provide additional semantic context beyond what's in the schema descriptions. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get the list of auto-converted parquet files') and resource ('for a dataset'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'hf_get_dataset_info' or 'hf_get_croissant' that might also retrieve dataset-related information, preventing a perfect score.
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 description implies usage by mentioning optional parameters ('Can specify subset (config) and split to get specific files'), suggesting when to use these features. However, it lacks explicit guidance on when to choose this tool over alternatives like 'hf_get_dataset_info' or 'hf_list_datasets', which might provide different types of dataset metadata or listings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 mentions the tool 'Gets' data and organizes it by type, but fails to describe critical behaviors like whether it requires authentication, rate limits, pagination, or error handling. This leaves significant gaps for a tool with no annotation coverage.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simple read operation with no parameters) and lack of annotations or output schema, the description is minimally adequate. It explains what is retrieved but omits details on return format, error cases, or behavioral traits, leaving room for improvement in completeness.
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 with 100% schema description coverage, so the schema fully documents the absence of inputs. The description adds value by explaining what is retrieved (dataset tags organized by type), which provides context beyond the empty schema. A baseline of 4 is appropriate as no parameters need compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Gets') and resource ('all available dataset tags hosted in the Hub'), and specifies the organization by type (e.g., task categories, languages, licenses). However, it does not explicitly differentiate from sibling tools like 'hf_get_model_tags' or 'hf_list_datasets', which reduces it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'hf_get_model_tags' for model tags or 'hf_list_datasets' for listing datasets. It lacks explicit context, exclusions, or prerequisites, leaving usage decisions ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool 'gets' data (implying read-only) and organizes tags by type, but lacks details on permissions, rate limits, response format, or error handling. This is inadequate for a tool with zero annotation coverage.
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, efficient sentence that front-loads the core purpose ('Gets all available model tags') and adds useful detail ('organized by type'). There is no wasted verbiage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless tool with no output schema, the description adequately covers the purpose and organization of data. However, without annotations, it lacks behavioral context like authentication needs or response structure, leaving gaps in completeness.
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 with 100% schema description coverage, so no parameter documentation is needed. The description adds value by explaining what the tool retrieves (model tags organized by type), which is appropriate for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Gets all available model tags') and resource ('hosted in the Hub'), with specific organization details ('organized by type'). It distinguishes from some siblings like hf_get_model_info (which gets model metadata) but not explicitly from hf_get_dataset_tags (which gets dataset tags).
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 description implies usage for retrieving model tags by type, but does not specify when to use this tool versus alternatives like hf_get_model_info or hf_list_models. No explicit exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 usefully mentions pagination and the types of metadata returned (downloads, likes, tags), which goes beyond the input schema. However, it doesn't address important behavioral aspects like rate limits, authentication requirements, error conditions, or what happens when no filters are applied (does it return all datasets?).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core purpose and filtering capabilities, the second describes the return format. Every element serves a purpose with no wasted words, though it could be slightly more front-loaded by mentioning pagination earlier given its importance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 8 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It covers the basic purpose and return format but lacks details about authentication, rate limits, error handling, and how results are structured beyond 'paginated results with dataset metadata.' The absence of an output schema increases the need for more behavioral transparency.
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?
The description mentions filtering by 'search terms, authors, tags, and more' which aligns with parameters like search, author, and filter. However, with 100% schema description coverage, the input schema already documents all 8 parameters thoroughly. The description adds minimal value beyond what's in the schema, meeting the baseline expectation for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get information from all datasets in the Hub' with specific filtering capabilities. It distinguishes itself from siblings like hf_get_dataset_info (single dataset) and hf_list_models (different resource type), though it doesn't explicitly contrast with hf_get_dataset_tags or hf_get_dataset_parquet which serve more specialized purposes.
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 description implies usage for listing datasets with filtering options, but provides no explicit guidance on when to choose this tool over alternatives like hf_get_dataset_info (for single dataset details) or hf_list_models (for models instead of datasets). It mentions filtering capabilities but doesn't clarify trade-offs or specific use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 effectively describes key behaviors: it's a read operation ('Get information'), supports filtering and pagination, and returns metadata. However, it doesn't cover important aspects like rate limits, authentication needs, error handling, or whether the operation is idempotent, leaving gaps for a tool with 8 parameters.
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 efficiently structured in two sentences: the first states the core purpose and filtering support, the second covers return behavior. Every phrase adds value without redundancy, making it easy to parse and front-loaded with essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (8 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and return format but lacks details on authentication, rate limits, error cases, and parameter constraints. For a tool with rich filtering capabilities and no structured output documentation, more contextual guidance would be beneficial.
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?
The description mentions filtering capabilities ('search terms, authors, tags, and more') which aligns with parameters like author, filter, and search, but doesn't add significant meaning beyond the schema's 100% coverage. It doesn't explain parameter interactions, default values, or provide examples beyond what's in the schema descriptions, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get information from all models in the Hub' with specific filtering capabilities. It distinguishes itself from sibling tools like hf_get_model_info (which gets info for a specific model) by focusing on listing all models with filtering. However, it doesn't explicitly contrast with hf_list_datasets, which follows a similar pattern for datasets.
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 description implies usage context through 'Supports filtering by search terms, authors, tags, and more' and mentions pagination, suggesting when to use it for broad model discovery. However, it lacks explicit guidance on when to choose this tool versus alternatives like hf_get_model_info for single-model details or hf_list_datasets for datasets, and doesn't mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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