mcp-blender-server

by tinhtinhcd

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes: downloading from different sources, generating via different services, checking status, etc. Some potential confusion between generation tools (Hunyuan vs Hyper3D) and between search/download for different asset stores, but descriptions clarify the differences.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern in snake_case (e.g., download_polyhaven_asset, generate_hyper3d_model_via_text). No mixing of styles or irregular naming.
Tool Count3/5
22 tools is on the high side, bordering on heavy for a single server, but still manageable given the integration of multiple asset services and generation pipelines. It exceeds the typical 3-15 range but doesn't feel excessive for the scope.
Completeness4/5
Covers the full lifecycle for asset integration: search, preview, download, generation, status polling, import, and texture application. Missing object manipulation, creation of primitives, or export, which are beyond its specialized focus.
Average 3.8/5 across 22 of 22 tools scored. Lowest: 2.7/5.
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
- No stable releases found
- 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?
No annotations provided; description only states 'Get detailed information' without specifying what information is returned, side effects, or error conditions. The output schema exists but is not referenced.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and to the point, but lacks structure (e.g., sections) and the parameter list is redundant with the schema. No wasted words, but could be better organized.
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 a single parameter and an output schema, the description is minimal. It does not explain what 'detailed information' entails, nor does it mention constraints like object existence. Slightly under-specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description repeats the parameter name and a trivial description ('The name of the object to get information about') that adds no value beyond the schema title.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'get detailed information' and the resource 'specific object in the Blender scene', distinguishing it from sibling like 'get_scene_info' which returns overall scene data.
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 on when to use this tool versus alternatives (e.g., get_scene_info) or any prerequisites. The description lacks any context for appropriate 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?
No annotations exist, and the description fails to disclose that the tool is read-only or non-destructive. It does not warn about any side effects or performance impact.
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 concise sentence with no unnecessary words, efficiently conveying the core purpose.
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?
With an output schema present, the description does not need to detail return values. However, 'detailed information' is imprecise; listing key fields (e.g., objects, materials, settings) would improve completeness.
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 tool has zero parameters, and the input schema is empty. The description adds no meaning beyond the schema, which already signals no inputs. With 100% schema coverage, baseline is 3, but the vague 'detailed information' does not enrich understanding.
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 'Get detailed information about the current Blender scene' clearly states the verb 'get' and resource 'scene information', distinguishing it from sibling 'get_object_info'. However, it is vague about what 'detailed information' includes.
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 on when to use this tool versus alternatives like get_object_info or other scene-related tools. No prerequisites or context for appropriate usage provided.
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 provided, so description carries full burden. Mentions code execution but does not disclose potential destructive side effects, error handling, or safety precautions. Inadequate for arbitrary code execution.
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?
Concise two-sentence description with front-loaded purpose. However, lacks structured detail; could benefit from including safety notes or output info.
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 arbitrary code execution and lack of annotations, the description is incomplete. It does not explain return values, error states, or the behavior of Blender context, which are critical for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet description only restates 'The Python code to execute', adding minimal meaning beyond the parameter name. Does not provide additional details like format, limits, or examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it executes arbitrary Python code in Blender, which is a specific verb and resource. Differentiates from sibling tools that handle asset importing, model generation, etc.
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?
Provides a usage tip to break code into smaller chunks, but lacks explicit guidance on when to use this tool versus alternatives. No warning about potential dangers or prerequisites.
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?
Discloses built-in materials, normalized size, and return format. Does not mention if the operation is synchronous or asynchronous, potential limits, or effect on Blender scene beyond import.
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?
Concise with a structured 'Parameters:' section. Every sentence adds value, no wasted words.
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?
Covers core purpose and params, but lacks details on process (time, async) and impact on scene. Output schema exists but is only summarized as 'message indicating success or failure'.
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?
Adds significant meaning beyond schema: text_prompt requires English, bbox_condition controls L/W/H ratio and must be list of 3 floats. Schema coverage 0% so description carries the burden.
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?
Clearly states it generates a 3D asset from text and imports into Blender. Does not explicitly differentiate from siblings like generate_hunyuan3d_model, but the verb+resource is specific.
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 on when to use this tool versus alternatives (e.g., Hunyuan, image-based). Only specifies that text_prompt must be in English, but lacks context for tool 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?
No annotations are provided, and the description does not disclose any behavioral traits such as idempotency, authentication requirements, rate limits, or side effects. Since there are no annotations, the description carries full burden but only minimally describes the action.
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 brief and front-loaded, with a clear statement followed by a parameter list. No extraneous information, though the parameter list could be integrated into the sentence for even greater conciseness.
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 presence of an output schema, the description does not need to explain return values. However, it could mention that the output is a list of category names or structures. It partially compensates for zero schema coverage on parameters.
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 description adds meaning by listing possible values for the asset_type parameter (hdris, textures, models, all), which is not present in the schema. This helps the agent understand valid inputs beyond the default.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a list of categories for a specific asset type on Polyhaven, using a specific verb ('Get') and resource ('list of categories'). It distinguishes from sibling tools like search_polyhaven_assets or download_polyhaven_asset.
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 does not provide any guidance on when to use this tool versus alternatives, nor does it mention prerequisites or exclusions. It merely states what the tool does.
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 fails to disclose important behavioral traits such as whether the asset overwrites existing files, requires an internet connection, or has any side effects in Blender. Only the basic action and return message are mentioned.
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: two sentences plus a parameter list. It is front-loaded with the core purpose and uses a clear bullet format for parameters. No extraneous 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?
The description covers the parameters and basic return, but lacks context about the import process (e.g., where in Blender it appears) and prerequisites (e.g., Blender must be open). Given that an output schema exists (though not detailed), the absence of return value details is acceptable but the tool feels minimally documented.
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?
Despite 0% schema description coverage, the description adds meaningful examples and acceptable values for each parameter (e.g., asset_type: 'hdris, textures, models'; resolution: '1k, 2k, 4k'; file_format: 'gltf, fbx for models'). This significantly aids the agent in understanding parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Download and import'), the resource ('Polyhaven asset'), and the destination ('into Blender'). The tool name itself indicates the source (Polyhaven), distinguishing it from sibling tools like download_sketchfab_model. No ambiguity.
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 (e.g., search_polyhaven_assets first, or when not to use it). There is no mention of prerequisites or 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, the description must disclose behavioral traits. It only states it returns a list with basic information but omits whether it is read-only, pagination behavior, or how invalid category strings are handled. The depth is insufficient for an agent to anticipate side effects or limits.
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: one line for purpose, a parameter list, and a one-line return description. Every sentence adds value, and there is no extraneous 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 simplicity (two optional parameters, no required fields) and the presence of an output schema, the description is minimally adequate. However, it lacks context about valid categories and does not mention that 'all' is a valid asset_type. For a complete understanding, an agent would benefit from a note about cross-referencing category tools.
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?
Schema coverage is 0%, so the description must compensate. It explains both parameters: 'asset_type' with allowed values (hdris, textures, models, all) and 'categories' as an optional comma-separated list. This adds meaningful context beyond the bare schema. However, it does not specify where valid category values come from (e.g., from 'get_polyhaven_categories').
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search for assets on Polyhaven with optional filtering'. It identifies a specific verb ('search') and resource ('assets on Polyhaven'). Among sibling tools, only this and 'search_sketchfab_models' perform searches, and the description differentiates by naming Polyhaven.
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 'search_sketchfab_models' or 'get_polyhaven_categories'. The description does not indicate prerequisites, constraints, or when not to use it.
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 exist, and the description does not disclose behavioral traits like whether the tool is read-only, requires user interaction, or has side effects. The description focuses only on the action and parameter, omitting safety or operational context.
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: two sentences plus a bullet point for the parameter. It front-loads the core purpose and uses minimal, efficient structure with no superfluous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description adequately covers the purpose and parameter. It lacks details on return format or any limitations, but given low complexity, it is reasonably complete.
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?
With 0% schema description coverage, the description adds crucial context for the 'max_size' parameter, explaining it limits the largest dimension in pixels and providing the default value. This compensates well for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Capture a screenshot') and the specific resource ('the current Blender 3D viewport'). It is unique among sibling tools, which focus on generation, download, and model retrieval, not viewport capture.
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, such as when a screenshot is preferred over code execution or direct data retrieval. There is no mention of appropriate contexts 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, the description carries full burden but only states that the tool imports and returns success. It does not disclose side effects, prerequisites (e.g., completed generation), or error behavior. This is insufficient for a mutation tool.
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 concise with two sentences and a parameter list. It is front-loaded with the purpose. Some structural improvements (e.g., clearer separation) could enhance readability, but it is efficient.
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 simple import tool with 2 parameters and no output schema, the description covers the essential information. However, it lacks details on failure scenarios, asset conflicts, or integration steps, leaving some gaps.
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?
Despite 0% schema coverage, the description adds clear meaning for both parameters: 'name' is the object name in scene, and 'zip_file_url' is from the generate model step. This compensates well for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool imports an asset generated by Hunyuan3D after generation. It distinguishes from sibling import_generated_asset by specifying the source as Hunyuan3D.
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 mentions 'after the generation task is completed' and that zip_file_url comes from the generate step, which offers some usage context. However, it does not explicitly differentiate when to use this tool versus the generic import_generated_asset or other siblings.
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 that the tool returns a formatted list but does not explicitly state that it is a read-only/non-destructive operation, nor does it disclose rate limits, data source freshness, or any side effects.
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 concise, using a clear list format for parameters and a short statement about the return value. It front-loads the main action and avoids unnecessary detail. A minor improvement would be to use markdown for better readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, 1 required) and the presence of an output schema, the description adequately covers the essentials. It explains parameters and the return format. However, it could mention sorting or pagination details for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description adds significant value by explaining each parameter's purpose: query (text to search), categories (comma-separated list), count (max results, default 20), downloadable (only downloadable models, default True). This compensates fully for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search for models on Sketchfab with optional filtering,' which is a specific verb+resource combination. It distinguishes itself from sibling tools like search_polyhaven_assets (different platform) and download_sketchfab_model (different action).
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 does not provide guidance on when to use this tool over alternatives, mention prerequisites, or specify contexts where it is appropriate or inappropriate. It only describes the tool's functionality without usage boundaries.
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 must fully disclose behavior. It mentions built-in materials, normalized size, and the need for re-scaling, but omits important traits: whether the tool modifies existing assets, required permissions, or side effects. The return is only described as a 'success or failure' message, lacking detail on what the generated asset reference is.
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 reasonably concise, with the main purpose stated upfront. It covers key points in a few sentences. However, some information (e.g., the return message) could be condensed without loss of clarity.
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 that an output schema exists (not shown), the description does not need to detail return values, but it only says 'message indicating success or failure,' which is vague. It misses details on error conditions, prerequisites (e.g., Blender must be running), and integration with sibling tools like import_generated_asset. Overall, it covers the basics but leaves gaps for a complex generation tool.
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?
Schema coverage is 0%, so the description must compensate. It clearly explains that input_image_paths require absolute paths and both must be lists even for single images, and that bbox_condition controls length/width/height ratio with a list of three ints. This adds substantial meaning beyond the schema's bare type definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a 3D asset from images using Hyper3D and imports it into Blender. It distinguishes itself from siblings like generate_hyper3d_model_via_text by specifying image input. Additional details (built-in materials, normalized size) clarify the exact output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains that only one of input_image_paths or input_image_urls should be used based on the current mode (MAIN_SITE vs FAL_AI). This provides context for parameter selection. However, it does not explicitly compare to alternatives (e.g., when to use text-based generation) or state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full behavioral disclosure burden. It states 'returns a message' but does not specify safety (read-only), side effects, or what the message contains beyond indicating availability. This is insufficient for complete transparency.
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, no redundancy, and front-loads the key action. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, output schema exists), the description covers the essential purpose. It could optionally mention that calling this tool is safe and has no side effects, but the current version is largely sufficient.
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?
With zero parameters, schema coverage is trivially 100%, meeting the baseline of 4. The description does not need to add parameter details as none exist, so no value is lost.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's verb ('Check') and resource ('Sketchfab integration'), and it distinguishes from sibling tools like get_polyhaven_status which check other integrations.
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?
No explicit when-to-use or when-not-to-use guidance is provided. It is implied that this tool is for Sketchfab status, but alternatives or prerequisites are not mentioned, leaving room for ambiguity in a complex scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes states (DONE, RUN) and return data (ResultFile3Ds with ZIP path) in detail. Also notes it is a polling API. With no annotations, this offers good transparency.
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?
Mostly concise with a clear structure (parameters, returns). Minor redundancy with the header 'For Hunyuan3D:' but overall well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given simple schema and no annotations, the description covers the parameter, expected states, and return data adequately. Could note failure states but still sufficient.
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?
Schema coverage is 0%, but the description explains job_id is 'The job_id given in the generate model step,' adding necessary context beyond the schema's minimal 'Job Id' title.
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?
Clearly states it checks if Hunyuan3D generation is completed, specifying verb and resource. However, it does not distinguish from sibling tool 'get_hunyuan3d_status', which likely performs a similar check.
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?
Provides usage context: 'This is a polling API, so only proceed if the status are finally determined.' But no guidance on when to use this vs alternative tools like get_hunyuan3d_status or poll_rodin_job_status.
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, the description carries full burden for behavioral disclosure. It states the tool returns a message indicating availability, which is appropriate for a read-only check. However, the instruction 'Don't emphasize the key type... but silently remember it' is unclear and could confuse the agent about how to handle the response.
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 two sentences, efficiently conveying purpose and return. However, the second sentence includes a cryptic instruction that is not clearly earned, slightly reducing conciseness. It is still relatively brief and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple zero-parameter tool with an output schema (not provided here), the description adequately explains the tool's function and return type. The odd instruction about key type does not detract from completeness but is not fully explained. It is reasonably complete for the context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema coverage is 100%. Since there are no parameters to document, the baseline score of 4 applies. The description does not need to add parameter information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it checks if Hunyuan3D integration is enabled in Blender, which is a specific verb-resource pair. It distinguishes from sibling tools like generate_hunyuan3d_model and poll_hunyuan_job_status by focusing on integration status.
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 does not explicitly state when to use this tool versus alternatives. The purpose is implied as a prerequisite check before using Hunyuan3D features, but no exclusions or alternatives are mentioned. The instruction about 'silently remember' is vague and not helpful for usage guidance.
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?
No annotations exist, so the description carries full burden. It says 'Returns a message' but does not disclose potential side effects or permissions, though the tool is read-only. Adequate but minimal.
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?
Two concise sentences with no superfluous text. Front-loaded with purpose, then return value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No parameters, and output schema exists (though not shown). The description mentions the return message, which is sufficient for a simple status check. Minor gap: does not specify the message format.
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?
No parameters; schema coverage is 100% trivially. Per guidelines, 0 params baseline is 4. The description adds no param info because none exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Check if PolyHaven integration is enabled in Blender', specifying a verb and resource. It distinguishes from sibling tools like download_polyhaven_asset and search_polyhaven_assets.
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?
Provides no explicit guidance on when to use this tool vs alternatives. The context is implied (e.g., before downloading assets), but lacks exclusion criteria or alternative tool names.
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?
No annotations provided, so description bears full burden. It states the tool imports an asset and returns success status but does not disclose side effects, error handling, or whether existing assets are overwritten. Adequate but could be more thorough.
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 well-structured with a clear overview and parameter list. While not extremely concise, it efficiently conveys necessary information without redundancy.
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, no output schema, and 3 parameters, the description covers purpose and parameter usage. However, it lacks details on error conditions, return value specifics, and potential conflicts, leaving some gaps for a complete understanding.
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?
Schema coverage is 0%, but the description adds meaning: explains name as object name, task_uuid and request_id as generation identifiers, and the constraint to use only one based on mode. This adds value beyond schema titles and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Import the asset generated by Hyper3D Rodin after the generation task is completed,' which is a specific verb+resource. It distinguishes from sibling tools like import_generated_asset_hunyuan by specifying the source.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use (after generation task completed) and provides explicit guidance on choosing between task_uuid and request_id based on mode. It lacks explicit 'when not to use' but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description clearly discloses the asynchronous workflow (returns job_id, status changes to DONE), the import action, and error handling. It adds context beyond the schema, though it does not cover side effects like overwriting or permissions.
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 well-structured with clear sections and bullet points. It is front-loaded with the main purpose and efficiently covers parameters and return values, though it could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (asynchronous generation, no annotations) and two optional parameters, the description provides sufficient context: input options, return format, and job lifecycle. It lacks details on combined text+image behavior and error message formats, but is adequate for an agent.
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?
Schema description coverage is 0%, but the description adds meaningful semantics: text_prompt is a short description in English/Chinese, input_image_url accepts local/remote URL or None. This compensates well for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a 3D asset using Hunyuan3D from text or image and imports it into Blender. It distinguishes from siblings by naming the specific engine (Hunyuan3D vs Hyper3D) and mentioning import into Blender, which is unique among the generation tools.
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 explains the input options (text and/or image) but provides no guidance on when to use this tool versus alternatives like generate_hyper3d_model_via_text or get_hunyuan3d_status. It does not specify prerequisites or when not to use it.
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?
No annotations are provided, so the description carries full burden. It discloses scaling and return details, and mentions access rights. However, it does not specify side effects like overwriting existing models or network requirements.
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?
Well-structured with clear sections, examples, and bullet points. A bit longer than necessary but every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (2 params, no annotations, output schema exists), the description is fairly complete, covering scaling, parameter examples, and return value hints. Could mention how to handle the returned import details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description compensates excellently by explaining each parameter with examples and required status.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it downloads and imports a Sketchfab model by UID, and explains the scaling behavior. It distinguishes from sibling tools like search_sketchfab_models and download_polyhaven_asset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides specific usage context (download by UID, scaling to target size) and includes example target_size values. However, it does not explicitly mention when not to use this tool or suggest alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. Discloses that it returns a message about availability and adds behavioral instruction about not emphasizing the key type in the message. This adds practical context beyond a simple status check.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no wasted words. First two sentences clearly state purpose and output. Third sentence adds relevant behavioral nuance. Efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose and output adequately. With output schema present, return format details are not needed. The behavioral instruction about key type adds useful context, though it could be clearer.
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?
No parameters exist, and schema coverage is 100%. Baseline for 0 parameters is 4. Description adds no parameter information, which is appropriate since none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action (check if Hyper3D Rodin integration is enabled) and the resource (Blender). Distinguishes from siblings like get_hunyuan3d_status by specifying the integration name.
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?
Implied usage (check status before using Hyper3D features), but no explicit when-to-use, when-not-to-use, or alternatives mentioned. Sibling tools exist for other integrations.
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. It notes that the tool returns a success/failure message but does not specify error conditions (e.g., missing object, invalid texture ID) or side effects. Some context is given via parameter descriptions.
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 very concise—one sentence for the main action, two bullet points for parameters, and one sentence for return value. No wasted words, and the most important information (what it does) is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and the presence of an output schema (as indicated in context signals), the description is mostly complete. It covers the action, required parameters, and return type. Minor gaps remain around error handling and prerequisites beyond 'downloaded,' but it's sufficient for a straightforward apply operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It fully explains both parameters: object_name as 'Name of the object to apply the texture to' and texture_id as 'ID of the Polyhaven texture to apply (must be downloaded first).' This adds significant meaning beyond the schema's type-only definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (apply), the resource (Polyhaven texture), and the target (object). It effectively distinguishes this tool from siblings like download_polyhaven_asset or search_polyhaven_assets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions the texture must be 'previously downloaded,' implicitly guiding usage after download_polyhaven_asset. However, it does not explicitly state when not to use or list alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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. It clearly indicates this is a read-only retrieval of a thumbnail, with no side effects. It could add details about error cases (e.g., invalid UID) but the core behavior is adequately transparent.
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: four sentences total, with the main purpose in the first sentence and usage guidance in the second. The parameter explanation is integrated succinctly. No redundant or irrelevant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description covers the essential aspects: what it does, how to use it, and what it returns (thumbnail Image). It is complete enough for an agent to use correctly, though details like image format or size are omitted.
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 input schema only defines 'uid' as a string with no description, giving 0% schema coverage. The description compensates by explaining that the uid is 'obtained from search_sketchfab_models,' providing essential context for how to populate the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action: 'Get a preview thumbnail of a Sketchfab model by its UID.' It specifies the resource (Sketchfab model) and the result (thumbnail). The usage context 'visually confirm a model before downloading' distinguishes it clearly from sibling tools like download_sketchfab_model and search_sketchfab_models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises when to use this tool: 'Use this to visually confirm a model before downloading.' It implies that download_sketchfab_model is the alternative for actual downloading. However, it does not explicitly state when not to use it or mention any prerequisites beyond having the UID.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the burden of behavioral disclosure. It explains that this is a polling API, describes the expected status values for each mode, and clarifies the condition to stop polling (all 'Done' or 'COMPLETED', or failure states).
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 well-structured with a brief summary followed by separate sections for each mode. Every sentence adds value, and the key information is front-loaded. No unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple input schema (2 parameters, no required fields, no enums, no output schema), the description provides complete context: explains both modes, their parameters, the return statuses, and the polling logic. It adequately covers the tool's behavior for selection and invocation.
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?
Schema description coverage is 0%, so the description must compensate. It does so by explaining that 'subscription_key' is for MAIN_SITE mode and 'request_id' is for FAL_AI mode, linking each parameter to its usage context and the generate model step. It does not clarify handling if both parameters are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Check if the Hyper3D Rodin generation task is completed' with specific verbs and resources, clearly distinguishing from sibling tools like 'poll_hunyuan_job_status' and other generation tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (polling for Rodin task completion) and provides explicit context for two modes (MAIN_SITE vs FAL_AI). However, it does not explicitly state when not to use it or list alternatives beyond the sibling context.
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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