Hugging Face

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

Annotations provide openWorldHint=true, indicating this tool can handle diverse inputs. The description adds some behavioral context: it mentions optional prompt enhancement, configuration of generation parameters, and use of a 'diffusion transformer pipeline.' However, it doesn't disclose important traits like rate limits, computational cost, or error conditions. No contradiction with annotations exists.

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 moderately concise but includes some unnecessary details. The first sentence clearly states the purpose. The second sentence about UI triggering is irrelevant for an AI agent. The third sentence adds some behavioral context but is vague. The return value explanation is useful but could be integrated more smoothly.

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 tool's complexity (image generation with 6 parameters) and lack of output schema, the description is somewhat incomplete. It explains the return tuple but doesn't describe the 'gallery_images' format or provide examples. With openWorldHint annotation, more guidance on input flexibility would be helpful. The description covers basics but leaves gaps for effective use.

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%, so parameters are well-documented in the schema itself. The description doesn't add meaningful semantic details beyond what's in the schema (e.g., it doesn't explain how 'shift' affects output or typical values for 'steps'). It mentions 'processes the input prompt (optionally enhancing it)' but doesn't clarify how enhancement works or when it applies.

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 clearly states the tool's purpose: 'Generate an image using the Z-Image model based on the provided prompt and settings.' It specifies the verb ('generate'), resource ('image'), and model ('Z-Image'), though it doesn't differentiate from sibling tools (none appear to be image generators). The UI trigger detail ('when the user clicks the "Generate" button') is slightly extraneous but doesn't detract from clarity.

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 is provided on when to use this tool versus alternatives. The description mentions it's triggered by a UI action, but this doesn't help an AI agent decide when to invoke it programmatically. There's no mention of prerequisites, constraints, or comparison to other image generation tools.

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

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, and openWorldHint=true, covering safety and scope. The description adds useful behavioral context about handling large documents with offsets, which isn't covered by annotations. However, it doesn't disclose other traits like rate limits, authentication needs, or response format details.

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 two sentences with zero waste: the first states the core purpose, and the second provides essential usage guidance for large documents. It's front-loaded and appropriately sized for the tool's complexity.

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 tool's moderate complexity (2 parameters, no output schema), the description is reasonably complete. It covers the main purpose and a key behavioral aspect (offset usage). With annotations handling safety and scope, and schema covering parameters, the description fills in useful gaps without being exhaustive.

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%, so the schema fully documents both parameters. The description adds marginal value by mentioning offset usage for large documents, but doesn't provide additional semantic details beyond what the schema already specifies (e.g., format of doc_url or offset calculation).

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 clearly states the verb 'fetch' and resource 'document from the Hugging Face or Gradio documentation library', making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'hf_doc_search' or 'paper_search', which might have overlapping domains.

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 clear context for when to use this tool ('for large documents, use offset to get subsequent chunks'), which helps guide usage. It doesn't explicitly mention when not to use it or name alternatives among siblings, but the context is sufficient for basic decision-making.

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

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

Annotations already indicate read-only, non-destructive, and open-world behavior. The description adds valuable context beyond this: it specifies knowledge is up-to-date as of 8 April 2026, mentions the ability to discover structure with empty queries, and suggests combining with product filters. No contradictions with annotations exist.

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 front-loaded with the core purpose, followed by specific usage instructions and a date constraint. Every sentence adds value: the first states the purpose, the second gives key usage tips, the third provides a recency note, and the fourth offers optimization advice. No wasted words.

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 tool's moderate complexity (search with filters), rich annotations (read-only, open-world), and full schema coverage, the description is largely complete. It covers purpose, usage, and constraints. The main gap is lack of output schema, but the description compensates by hinting at structure discovery. Slightly more detail on result format could improve completeness.

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 clear documentation for both parameters. The description adds some semantic context by explaining that an empty query is for structure discovery and semantic queries are for targeted searches, but this mostly reinforces the schema's descriptions without significant additional insight.

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 clearly states the tool's purpose with specific verbs ('Search and Discover') and resources ('Hugging Face Product and Library documentation'), distinguishing it from sibling tools like hf_doc_fetch (likely for fetching specific docs) and hub_repo_search (for repositories). It explicitly mentions documentation search, not general content or code search.

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 explicit guidance on when to use this tool: send an empty query for structure/navigation discovery, use semantic queries for targeted searches, and combine with the product filter for focused results. It also implicitly distinguishes from alternatives by specifying its scope (documentation vs. repositories, papers, or spaces in sibling tools).

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

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

Annotations provide readOnlyHint=true and openWorldHint=false, indicating it's a safe read operation with limited scope. The description adds valuable context beyond this: it discloses that tools are used anonymously and may be rate limited, and that this tool provides authentication instructions. This enhances transparency about the operational environment and tool behavior without contradicting annotations.

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 two sentences that are front-loaded with key information: the anonymous usage and rate limiting context, followed by the specific call-to-action. There is no wasted text, and each sentence adds value, though it could be slightly more structured for clarity.

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 tool's simplicity (0 parameters, read-only, no output schema), the description is moderately complete. It covers usage context and purpose but lacks details on what specific information or instructions are returned. With no output schema, more clarity on expected outputs would improve completeness, but it's adequate for a basic tool.

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?

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter information, and it appropriately focuses on the tool's purpose and usage. A baseline of 4 is justified as it compensates well for the simple parameter structure.

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 states 'Call this tool for instructions on joining and authenticating,' which provides a vague purpose rather than specifying what the tool actually does. It doesn't clearly state that this tool retrieves current user information or authentication status, making it tautological by focusing on why to call it rather than what it does.

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 explicitly provides usage guidance: 'Call this tool for instructions on joining and authenticating' and mentions that 'Hugging Face tools are being used anonymously and may be rate limited.' This clearly indicates when to use this tool (to get authentication instructions) and why (due to anonymous usage and rate limits), with no misleading information.

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

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

Annotations indicate read-only, non-destructive, and closed-world behavior, which the description aligns with by describing a retrieval operation. The description adds value beyond annotations by specifying auto-detection of repo types and the ability to handle multiple repos, enhancing context without contradiction.

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 that front-loads the core purpose and key behavior (auto-detection). Every word contributes meaning without redundancy, making it appropriately sized and well-structured.

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 tool's moderate complexity, rich annotations, and no output schema, the description is mostly complete. It covers purpose and key behavior but could benefit from mentioning output format or error handling to fully compensate for the lack of output schema.

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 full documentation for parameters. The description adds minimal semantics beyond the schema, mentioning auto-detection for repo_type and the ability to handle multiple repos, but does not elaborate on format or constraints, aligning with the baseline for high 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?

The description clearly states the action ('Get details') and resource ('one or more Hugging Face repos'), specifying the types (model, dataset, or space) and the auto-detection behavior. It distinguishes from sibling tools like hub_repo_search by focusing on details retrieval rather than searching.

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 clear context for usage ('Get details for one or more Hugging Face repos') and implies when to use it (for retrieving repo details). However, it does not explicitly state when not to use it or name alternatives like hub_repo_search for broader searches, leaving some guidance gaps.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, covering safety and scope. The description adds valuable context beyond annotations: it explains the tool can 'target models, datasets, spaces, or aggregate across multiple repo types in one call' and provides a specific instruction about including links in responses. No contradictions with annotations exist.

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 efficiently structured in three sentences with zero waste. The first sentence states the core purpose, the second provides usage guidance and sibling differentiation, and the third gives a specific response instruction. Every sentence 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 tool's moderate complexity (6 parameters, no output schema), the description provides good contextual completeness. It explains the tool's scope, differentiates from siblings, and gives response instructions. The main gap is lack of information about return format or pagination behavior, but annotations cover safety aspects adequately.

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 schema already fully documents all 6 parameters. The description adds minimal parameter semantics beyond the schema, mentioning only the ability to 'target models, datasets, spaces' which relates to the repo_types parameter. This meets the baseline 3 when schema coverage is high.

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 clearly states the specific action ('Search Hugging Face repositories'), the resource ('repositories'), and scope ('with a shared query interface'). It explicitly distinguishes from sibling 'space_search' by noting that tool is for 'semantic-first discovery of Spaces', establishing clear differentiation.

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 explicit guidance on when to use this tool vs. alternatives: 'Use space_search for semantic-first discovery of Spaces.' It also provides context about when to use this tool ('aggregate across multiple repo types in one call') and includes a usage instruction ('Include links to repositories in your response').

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

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

Annotations indicate read-only, non-destructive, and open-world behavior, which the description aligns with by not contradicting. The description adds value by specifying to include 'Link to paper' in results and consider tabulation, which are behavioral details not covered by annotations, enhancing transparency about output formatting and user interaction.

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 concise with two sentences that directly address the tool's function and output considerations. It is front-loaded with the core purpose, though the second sentence could be more streamlined, but overall it avoids unnecessary verbosity and earns its place efficiently.

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 tool's moderate complexity (3 parameters, no output schema) and rich annotations, the description is somewhat complete but has gaps. It covers purpose and output formatting but lacks details on error handling, result structure beyond links, or integration with sibling tools, making it adequate but not fully comprehensive for an agent's needs.

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%, so the schema fully documents parameters like 'query', 'concise_only', and 'results_limit'. The description does not add semantic details beyond the schema, such as explaining parameter interactions or use cases, but this is acceptable given the high schema coverage, resulting in a baseline score.

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 clearly states the action ('Find') and resource ('Machine Learning research papers on the Hugging Face hub'), making the purpose evident. However, it does not explicitly differentiate from sibling tools like 'hf_doc_search' or 'space_search', which might also search Hugging Face content, leaving some ambiguity about scope.

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 implies usage for finding ML papers on Hugging Face, but lacks explicit guidance on when to use this tool versus alternatives like 'hf_doc_search' or 'hub_repo_search'. It mentions considering tabulation for user intent, which provides some context, but does not specify exclusions or clear alternatives.

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

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

Annotations provide readOnlyHint=true, openWorldHint=true, and destructiveHint=false, covering safety and scope. The description adds some behavioral context: it mentions semantic search (implying fuzzy matching vs exact), includes a note about MCP Servers, and requests presentation formatting ('Include links'). However, it doesn't disclose rate limits, authentication needs, or detailed output behavior beyond presentation hints.

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 brief but somewhat disjointed. The first sentence is clear, but the second sentence is confusing ('IMPORTANT Only MCP Servers can be used with the dynamic_space toolInclude links...')—it appears to have a typo or missing punctuation, reducing clarity. It's front-loaded with the core purpose but includes an awkwardly phrased instruction.

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 tool's complexity (semantic search with 3 parameters), annotations cover safety and scope well, but there's no output schema. The description mentions result presentation ('Include links') but doesn't explain return values, error handling, or search limitations. It's moderately complete but lacks details on output structure and edge cases.

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%, with clear parameter descriptions in the schema. The description adds minimal parameter semantics: it implies the 'query' parameter is for semantic search and mentions MCP Servers (related to the 'mcp' parameter). However, it doesn't provide additional meaning beyond what's in the schema, such as search result details or query examples.

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 clearly states the tool's purpose: 'Find Hugging Face Spaces using semantic search.' It specifies the verb ('Find'), resource ('Hugging Face Spaces'), and method ('semantic search'). However, it doesn't explicitly differentiate from sibling tools like 'hub_repo_search' or 'paper_search' beyond the resource type, which prevents 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.

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

The description provides minimal usage guidance. It includes an 'IMPORTANT' note about MCP Servers and dynamic_space tool, but this is confusing and not clearly actionable for when to use this tool versus alternatives. No explicit when-to-use or when-not-to-use scenarios are provided, and it doesn't reference sibling tools for comparison.

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