Civitai MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes focused on different aspects of the Civitai platform (browsing images, getting creators, downloading models, searching with various filters). However, there is some overlap between search_models, search_models_by_creator, and search_models_by_tag that could potentially cause confusion about which to use for specific search scenarios.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with clear, descriptive names. The naming convention is uniform throughout (get_, browse_, search_) with no mixing of styles or conventions.
Tool Count5/5
14 tools is well-scoped for a Civitai server covering models, creators, tags, and images. Each tool appears to serve a specific purpose within the domain without obvious redundancy or bloat.
Completeness4/5
The toolset provides comprehensive coverage for browsing, searching, and retrieving information about models, creators, tags, and images. Minor gaps might include operations like creating or updating content (if the API supports it), but for a read-only browsing interface, the coverage is quite complete.
Average 2.9/5 across 14 of 14 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on July 26, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states a read operation ('Get') but lacks details on permissions, rate limits, pagination, or response format. While it implies a ranking based on ratings, it does not specify how ratings are calculated or if the tool is safe/read-only, leaving significant gaps in understanding its behavior.
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 with no wasted words, making it easy to parse. However, it is front-loaded but overly brief, potentially under-specifying the tool's purpose without sacrificing clarity in its minimal form.
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 (a ranking query with parameters) and lack of annotations or output schema, the description is incomplete. It fails to explain what 'highest rated' means, how results are sorted, or what the return structure entails, leaving critical context gaps for effective agent use.
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 input schema has 100% description coverage, documenting both parameters ('limit' and 'period') with details like defaults and enum values. The description adds no additional meaning beyond the schema, as it does not explain parameter usage or interactions. Baseline 3 is appropriate since the schema adequately covers parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get the highest rated models' states a clear verb ('Get') and resource ('highest rated models'), establishing a basic purpose. However, it lacks specificity about what constitutes 'highest rated' (e.g., based on user ratings, downloads, or other metrics) and does not differentiate from siblings like 'get_popular_models' or 'get_latest_models', leaving ambiguity in scope.
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 guidance is provided on when to use this tool versus alternatives. The description does not mention prerequisites, exclusions, or comparisons to sibling tools such as 'get_popular_models' or 'get_latest_models', offering no context for selection among similar list-retrieval functions.
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 mentions 'browse' but doesn't disclose behavioral traits such as pagination details (implied by 'page' parameter), rate limits, authentication needs, or what the output looks like (e.g., image metadata vs. URLs). This is a significant gap for a tool with 9 parameters and no output schema.
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's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 complexity (9 parameters, no annotations, no output schema), the description is incomplete. It doesn't cover behavioral aspects like pagination, rate limits, or output format, which are crucial for effective tool use. The schema handles parameters well, but the overall context lacks necessary operational details.
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 parameters thoroughly. The description doesn't add any meaning beyond the schema, such as explaining parameter interactions or default behaviors. Baseline 3 is appropriate as the schema does the heavy lifting, but no extra value is added.
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 ('browse') and resource ('AI-generated images from Civitai'), making the purpose evident. However, it doesn't differentiate from sibling tools like 'search_models' or 'get_latest_models', which might also involve browsing or retrieving content, so it lacks explicit sibling distinction.
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 guidance is provided on when to use this tool versus alternatives. With sibling tools like 'search_models' or 'get_popular_models', the description doesn't clarify if this is for general browsing, filtered exploration, or specific use cases, leaving the agent without usage context.
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 'browse and search' but doesn't specify if this is a read-only operation, whether it requires authentication, rate limits, pagination behavior beyond the schema, or what the output format looks like. This leaves significant gaps in understanding the tool's behavior.
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 any fluff or redundant information. It's appropriately sized and front-loaded, making it easy to parse quickly.
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 complexity of a search/browse tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what information is returned about creators, how results are structured, or any behavioral traits like error handling. This leaves the agent with insufficient context for effective use.
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 input schema has 100% description coverage, clearly documenting all three parameters (limit, page, query) with details like ranges and purposes. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline for high schema coverage without compensating 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 verb ('browse and search') and resource ('model creators on Civitai'), making the purpose understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'search_models_by_creator' or 'get_latest_models', which might also involve creators, so it doesn't fully distinguish from alternatives.
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 scenarios like when you need creator information specifically, or when to prefer other tools like 'search_models_by_creator' for models by a creator. Without such context, the agent lacks clear usage direction.
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 for behavioral disclosure. It states the tool retrieves a download URL but doesn't mention any behavioral traits, such as whether it requires authentication, has rate limits, returns a temporary or permanent URL, or what format the URL is in. This is a significant gap 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 any wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the download URL is used for, its validity period, or any error conditions. For a tool that likely involves accessing external resources, more context is needed to ensure correct usage.
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 implies the parameter 'modelVersionId' is used to specify which model version to get the URL for, but it adds no meaning beyond what the input schema provides (which has 100% coverage and fully describes the parameter). Since schema coverage is high, the baseline is 3, and the description doesn't compensate with extra details like ID format or sourcing.
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') and target resource ('download URL for a specific model version'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get_model_version' or 'get_model_version_by_hash' that might also retrieve model version data, so it's not fully specific about its unique function.
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 prerequisites, such as needing a model version ID from another tool, or compare to siblings like 'get_model_version' that might return different data. This leaves the agent with minimal context for selection.
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 full burden but offers minimal behavioral insight. It doesn't disclose whether this is a read-only operation, how it handles rate limits, authentication needs, or what the return format looks like (e.g., list of model objects). The phrase 'Get' implies retrieval but lacks specifics on behavior.
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's appropriately sized and front-loaded, making it easy to understand at a glance.
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?
For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'newest' means (e.g., sorting criteria), potential limitations, or return value structure, leaving significant gaps in understanding how to effectively use this tool in context with its siblings.
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 information beyond what's in the schema, which has 100% coverage for the single parameter 'limit'. Since schema coverage is high, the baseline score of 3 applies, as the description doesn't compensate but also doesn't need to given the schema's completeness.
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') and resource ('newest models uploaded to Civitai'), providing specific purpose. However, it doesn't explicitly differentiate from sibling tools like 'get_popular_models' or 'get_top_rated_models' which might also return recent models, so it doesn't reach the highest 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 like 'get_popular_models' or 'search_models'. It mentions 'newest models' but doesn't clarify if this means by upload date, version date, or another metric, leaving usage context 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves 'detailed information', but doesn't specify what that includes (e.g., metadata, statistics, permissions), whether it's a read-only operation, or any constraints like rate limits or authentication needs. This leaves significant gaps for a tool that likely interacts with a model database.
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's appropriately sized and front-loaded, making it easy to parse quickly.
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?
For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'detailed information' entails in the return values, nor does it address behavioral aspects like error handling or data freshness. Given the complexity implied by sibling tools (e.g., versioning, searching), more context is needed for effective use.
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 input schema has 100% description coverage, clearly documenting the 'modelId' parameter as a number. The description adds minimal value beyond this, only reinforcing that it retrieves information 'by ID'. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't provide additional semantic context.
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 about a specific model by ID'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_model_version' or 'get_latest_models', which could also retrieve model information in different contexts.
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 guidance is provided on when to use this tool versus alternatives like 'get_models_by_type', 'search_models', or 'get_model_version'. The description implies usage for retrieving details of a known model ID, but lacks explicit when/when-not instructions or references to sibling tools.
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 only states the basic filtering functionality without mentioning any behavioral traits such as pagination, rate limits, authentication requirements, or what happens when no models match the type. This leaves significant gaps in understanding how the tool behaves in practice.
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's appropriately sized and front-loaded, making it easy to parse quickly while conveying the core functionality.
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 lack of annotations and output schema, the description is incomplete for a tool with 3 parameters. It doesn't explain return values, error conditions, or behavioral nuances, leaving the agent with insufficient context to use the tool effectively beyond basic parameter passing.
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 input schema has 100% description coverage, providing clear details for all parameters including enums and defaults. The description adds minimal value by mentioning 'type' filtering but doesn't elaborate on parameter interactions or usage beyond what the schema already documents, aligning with 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') and resource ('models') with specific filtering criteria ('by type'), making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'search_models' or 'get_latest_models' that might also retrieve models with different filtering approaches, missing full sibling differentiation.
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 like 'search_models' or 'get_popular_models'. It mentions filtering by type but doesn't specify contexts where this is preferred over other filtering methods, leaving the agent without clear usage direction.
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. While 'Get detailed information' implies a read-only operation, it doesn't specify what 'detailed information' includes, whether authentication is required, rate limits, error conditions, or the format of the returned data. This leaves significant gaps in understanding the tool's behavior.
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's appropriately sized and front-loaded, making it easy to understand at a glance.
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 lack of annotations and output schema, the description is incomplete for a tool that retrieves 'detailed information'. It doesn't specify what information is returned, potential errors, or usage context, leaving the agent with insufficient guidance to effectively use the tool beyond the basic parameter requirement.
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 schema description coverage is 100%, with the single parameter 'modelVersionId' clearly documented in the schema. The description adds no additional meaning beyond what the schema provides, such as examples of valid IDs or context about where to find them. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
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 detailed information') and the target ('about a specific model version'), which is a specific verb+resource combination. However, it doesn't distinguish this tool from similar siblings like 'get_model' or 'get_model_version_by_hash', which likely retrieve related information about models or model versions.
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. With multiple sibling tools like 'get_model', 'get_latest_models', and 'get_model_version_by_hash', there's no indication of when this specific tool is appropriate or what differentiates it from others in the context.
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 full burden but offers minimal behavioral context. It states it 'gets' information (implying read-only), but doesn't disclose error handling (e.g., invalid hash formats), authentication needs, rate limits, or response format. For a lookup tool with zero annotation coverage, this is inadequate.
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 with zero waste. It front-loads the core purpose ('Get model version information') and specifies the key constraint ('by file hash') directly. Every word earns its place.
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 no annotations and no output schema, the description is incomplete. It doesn't explain what 'model version information' includes (e.g., metadata, download links, ratings) or behavioral aspects like error cases. For a tool that likely returns structured data, more context is needed to guide the agent effectively.
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%, with the hash parameter fully documented in the schema (including supported hash types). The description adds no parameter details beyond what the schema provides, so it meets the baseline of 3 when 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 verb 'Get' and the resource 'model version information', specifying the lookup method 'by file hash'. It distinguishes from siblings like 'get_model_version' (likely by ID) and 'get_model' (likely by name). However, it doesn't explicitly contrast with all siblings, keeping it at 4 rather than 5.
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 when hash-based lookup is appropriate compared to ID-based ('get_model_version'), name-based ('get_model'), or other search methods. There's no context about prerequisites or limitations.
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 full burden for behavioral disclosure but offers minimal information. It mentions 'most popular/downloaded' but doesn't clarify whether popularity is based on downloads, views, likes, or other metrics, nor does it address pagination, rate limits, authentication requirements, or response format.
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 at just 5 words, with zero wasted language. It's front-loaded with the core purpose and uses efficient phrasing, making it easy to parse while still conveying the essential function.
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?
For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'popular' means operationally, how results are sorted, what data is returned, or any behavioral constraints. Given the context of multiple sibling tools with overlapping functions, more guidance is needed.
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's already in the schema, which has 100% coverage with clear descriptions for both 'limit' and 'period'. The baseline score of 3 reflects adequate parameter documentation through the schema alone, with no additional value from the description.
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 ('most popular/downloaded models'), making it immediately understandable. However, it doesn't explicitly differentiate from siblings like 'get_latest_models' or 'get_top_rated_models', which reduces clarity about when to choose this specific tool.
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. With siblings like 'get_latest_models', 'get_top_rated_models', and 'search_models', there's no indication of when popularity-based retrieval is preferred over recency, rating, or search-based approaches.
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 'browse and search' but doesn't clarify if this is a read-only operation, how pagination works beyond the schema, or any rate limits or authentication needs. This is a significant gap 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's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., tag lists, metadata), how results are structured, or any behavioral nuances like error handling. For a tool with 3 parameters and no structured output, this leaves critical gaps for an agent.
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 input schema has 100% description coverage, clearly documenting the 'limit', 'page', and 'query' parameters. The description adds no additional meaning beyond what the schema provides, such as examples of search queries or tag formats, so it meets the baseline score 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 ('browse and search') and resource ('model tags on Civitai'), making the purpose evident. However, it doesn't explicitly differentiate this tool from sibling tools like 'search_models_by_tag', which might also involve tags, leaving some ambiguity about its unique role.
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. With sibling tools like 'search_models_by_tag' available, there's no indication of whether this tool is for general tag exploration or specific use cases, leaving the agent to guess based on context.
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 only states the basic function without mentioning important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, pagination behavior (implied by the 'page' parameter but not explained), or what the response format looks like.
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 gets straight to the point with zero wasted words. It's appropriately sized for a search tool and front-loads the core functionality.
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?
For a search tool with 8 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain the relationship to sibling tools, doesn't describe the return format, and provides minimal behavioral context. The agent would struggle to use this effectively without trial and error.
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 schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly with descriptions, constraints, and enums. The description adds no additional parameter information beyond what's in the schema, meeting the baseline expectation when schema coverage is complete.
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 ('Search for AI models') and the resource ('on Civitai'), making the purpose understandable. However, it doesn't distinguish this tool from its many siblings (like get_latest_models, get_popular_models, search_models_by_creator, etc.), which all involve retrieving models with different approaches.
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 the numerous sibling tools. It mentions 'various filters' but doesn't explain when filtered searching is preferable to the more specific sibling tools like get_latest_models or search_models_by_creator.
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 the action ('Search') but doesn't describe what the search returns (e.g., list of models with metadata), whether it's paginated, rate-limited, or requires authentication. This leaves significant gaps in understanding the tool's behavior 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core purpose without unnecessary words. It's front-loaded with the main action and resource, making it easy to parse quickly.
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 lack of annotations and output schema, the description is incomplete for a search tool. It doesn't explain what the return values are (e.g., model objects, metadata), how results are structured, or any behavioral traits like pagination or error handling. This leaves the agent with insufficient context to use the tool effectively.
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 parameters (username, limit, sort) with descriptions and constraints. The description adds no additional meaning beyond implying the username parameter is central, which is already clear from the schema. 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 verb ('Search') and resource ('models') with a specific filter criterion ('by a specific creator'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_models' or 'get_creators', which would require more specific language about scope or output format.
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. With siblings like 'search_models', 'get_creators', and 'get_latest_models', there's no indication of when this specific creator-based search is preferred or what distinguishes it from other search or retrieval tools.
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 the tool searches for models but doesn't describe what 'models' are in this context, how results are returned (e.g., pagination, format), or any limitations (e.g., rate limits, authentication needs). This leaves significant gaps in understanding the tool's behavior.
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 appropriately sized and front-loaded, making it easy to understand at a glance.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'models' are, how results are structured, or any behavioral traits like error handling. For a search tool with no structured context, more detail is needed to adequately inform an agent.
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%, with clear descriptions for all parameters (limit, sort, tag). The description adds minimal value beyond the schema, as it only mentions the 'tag' parameter without providing additional context or meaning. This meets the baseline score of 3 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 with a specific verb ('Search') and resource ('models'), and specifies the search criterion ('by a specific tag'). However, it doesn't explicitly differentiate from sibling tools like 'search_models' or 'search_models_by_creator', which reduces clarity about when to use this specific search variant.
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 like 'search_models' or 'search_models_by_creator'. It mentions the search criterion ('by a specific tag') but doesn't explain when tag-based searching is preferred over other search methods available in the sibling tools.
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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