BlenderMCP

Overview Schema Related Servers Score Discussions

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but there is some overlap between 'download_polyhaven_asset' and 'set_texture' (both involve Polyhaven textures), and 'generate_hyper3d_model_via_images' and 'generate_hyper3d_model_via_text' share similar generation workflows. The descriptions clarify differences, but agents might occasionally confuse related tools.
Naming Consistency4/5
Tool names generally follow a consistent verb_noun pattern (e.g., download_polyhaven_asset, get_scene_info, search_sketchfab_models). However, there are minor deviations like 'execute_blender_code' (verb_noun_noun) and 'poll_rodin_job_status' (verb_adjective_noun_noun), which slightly break the pattern but remain readable.
Tool Count4/5
With 17 tools, the count is reasonable for a Blender integration server covering asset downloads, 3D generation, scene management, and status checks. It is slightly on the higher side but well-scoped for the domain, with each tool serving a specific function without obvious bloat.
Completeness4/5
The toolset provides good coverage for Blender workflows, including asset sourcing (Polyhaven, Sketchfab), 3D generation (Hyper3D), scene inspection, and basic operations. Minor gaps exist, such as no direct tools for editing objects or managing materials beyond textures, but core functionalities are well-represented and agents can work around these limitations.
Average 3.4/5 across 17 of 17 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
- 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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions access rights and downloadability as constraints, which adds some context, but fails to describe critical behaviors such as what 'import' entails (e.g., file format, destination), potential side effects, error handling, or response format beyond a success/failure message. This leaves significant gaps for a tool that performs downloads and imports.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with three sentences: a purpose statement, parameter explanation, and return/constraint info. It's front-loaded with the main action, but the last sentence could be more integrated. There's minimal waste, though it could be slightly more structured for optimal clarity.
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 downloading and importing a model, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on the import process, output format, error conditions, and how it differs from sibling tools. This makes it inadequate for an agent to fully understand the tool's behavior and usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning by explaining that 'uid' is 'The unique identifier of the Sketchfab model', which clarifies its purpose beyond the schema's basic 'string' type. However, with schema description coverage at 0% and only one parameter, this provides moderate value but doesn't fully compensate for the lack of schema details (e.g., format examples, validation rules).
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 'Download and import' and the resource 'Sketchfab model by its UID', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_sketchfab_models' or 'import_generated_asset', which would be needed for a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance with 'The model must be downloadable and you must have proper access rights', which hints at prerequisites but doesn't specify when to use this tool versus alternatives like 'search_sketchfab_models' for finding models or 'import_generated_asset' for other imports. No explicit when/when-not or alternative tool references are included.
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 a list of categories, implying a read-only operation, but doesn't cover critical aspects like authentication needs, rate limits, error handling, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence clearly states the purpose, followed by a brief parameter section. There's no unnecessary verbosity, and each sentence adds value. However, the structure could be slightly improved by integrating parameter details more seamlessly, but it remains efficient overall.
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 low complexity (1 parameter, no output schema, no annotations), the description is incomplete. It covers the basic purpose and parameter but lacks context on usage, behavioral traits, and output details. Without annotations or an output schema, the description should do more to explain what the returned list looks like and any operational constraints, making it inadequate for full understanding.
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 some meaning beyond the input schema: it lists the asset_type parameter and provides its possible values (hdris, textures, models, all), which the schema lacks (0% coverage with no enums). However, it doesn't explain the semantics of these values (e.g., what 'all' entails) or any constraints, so it only partially compensates for the schema's deficiencies, aligning with the baseline for moderate coverage improvement.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get a list of categories for a specific asset type on Polyhaven.' It specifies the verb ('Get'), resource ('categories'), and scope ('for a specific asset type on Polyhaven'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from siblings like 'search_polyhaven_assets' or 'get_polyhaven_status', which could provide similar metadata.
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 minimal guidance: it mentions the asset_type parameter but offers no context on when to use this tool versus alternatives. For example, it doesn't clarify if this should be used before searching assets or as a standalone metadata query, nor does it reference sibling tools like 'search_polyhaven_assets' for filtering. This lack of explicit usage scenarios limits its helpfulness.
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 executing code 'step-by-step' but fails to cover critical traits like safety (e.g., potential for destructive operations, error handling), permissions, or performance implications (e.g., execution time limits). This is inadequate for a tool that runs arbitrary code.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by a usage tip and parameter explanation. There's minimal waste, though the structure could be slightly more polished (e.g., bullet points for clarity).
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 (executing arbitrary code) and lack of annotations or output schema, the description is insufficient. It doesn't explain return values, error conditions, or security considerations, making it incomplete for safe and effective use by an AI 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 0%, so the description must compensate. It adds meaning by specifying that the 'code' parameter is 'The Python code to execute,' which clarifies the parameter's purpose beyond the schema's basic type. However, it doesn't provide details on code format, constraints, or examples, leaving gaps in 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 clearly states the tool's purpose: 'Execute arbitrary Python code in Blender.' It specifies the verb ('execute') and resource ('Python code in Blender'), making it understandable. However, it doesn't explicitly differentiate from sibling tools (e.g., other execution or modeling tools), which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage guidance: 'Make sure to do it step-by-step by breaking it into smaller chunks,' which implies a best practice for using the tool. However, it lacks explicit when-to-use vs. alternatives (e.g., compared to other Blender-related tools in the sibling list) or any exclusions, leaving room for ambiguity.
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 (e.g., object properties, location, type), whether it requires specific permissions, or how errors are handled (e.g., if the object doesn't exist). For a tool with no annotation coverage, this leaves 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 appropriately sized and front-loaded, starting with the core purpose in the first sentence and following with parameter details. There's no wasted text, and the structure is clear, though it could be slightly more concise by integrating the parameter note into the main sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, no annotations, no output schema), the description is minimally adequate. It covers the basic purpose and parameter semantics but lacks details on return values, error handling, and differentiation from siblings. Without an output schema, the agent is left guessing what 'detailed information' entails, which is a notable gap.
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 meaningful context for the single parameter 'object_name' by specifying it as 'The name of the object to get information about', which clarifies its purpose beyond the schema's minimal title. With 0% schema description coverage and only one parameter, this compensation is effective, though it doesn't detail format constraints (e.g., case sensitivity).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('detailed information about a specific object in the Blender scene'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_scene_info', which appears to be a broader scene-level information tool, leaving some ambiguity about when to use one versus the other.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_scene_info' (which likely provides broader scene information) or other context-specific tools, leaving the agent to infer usage based on tool names alone. There's no explicit when/when-not or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, 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 there are any rate limits, authentication requirements, or what format the information is returned in. The description is too vague about the actual 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 states exactly what the tool does without any unnecessary words. It's appropriately sized for a zero-parameter tool and front-loads the core purpose immediately.
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, no output schema, and a description that's vague about what 'detailed information' actually means, this is incomplete. The agent won't know what to expect from this tool's output or how to interpret the results. Given the complexity of Blender scenes, more context about what information is returned would be helpful.
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 with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't waste space discussing non-existent parameters, though it could potentially mention that no parameters are required.
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 target resource ('current Blender scene'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_object_info' or 'get_hyper3d_status' that also retrieve information from different aspects of the Blender environment.
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 this tool is appropriate, what prerequisites might exist, or how it differs from other information-retrieval tools in the sibling list like 'get_object_info' or 'get_hyper3d_status'.
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. It mentions optional filtering and returns a list with basic information, but doesn't disclose critical behaviors like pagination, rate limits, authentication needs, error handling, or what 'basic information' includes. For a search tool with no annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose. The parameter explanations are clear and necessary. It could be slightly more concise by integrating the return statement into the opening sentence, but overall it's efficient with minimal waste.
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 2 parameters with 0% schema coverage and no output schema, the description does a decent job explaining parameters but lacks completeness. It doesn't cover return format details, error cases, or behavioral aspects like search limits. For a search tool with no annotations, it's adequate but has clear 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?
The description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'asset_type' can be hdris, textures, models, or all, and 'categories' is a comma-separated list for filtering. This compensates well for the schema's lack of descriptions, though it doesn't detail format examples or constraints.
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 searches for assets on Polyhaven with filtering, which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'download_polyhaven_asset' or 'search_sketchfab_models' by specifying the Polyhaven platform. However, it doesn't explicitly contrast with 'get_polyhaven_categories' which might be related.
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 to prefer this over 'search_sketchfab_models' for different asset types, or how it relates to 'get_polyhaven_categories' for filtering. Usage is implied by the search functionality but lacks explicit 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 for behavioral disclosure. It mentions the action ('Download and import') and return type ('message indicating success or failure'), but lacks details on critical behaviors like whether it requires internet connectivity, what happens if the asset already exists, expected download times, or error handling specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement followed by a bulleted parameter explanation and return information. Every sentence adds value without redundancy, and it's appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters with 0% schema coverage and no output schema, the description does well explaining parameters but lacks completeness for a download/import operation. It doesn't cover behavioral aspects like network requirements, file system impacts, or integration specifics with Blender, which are important for this type of 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?
The description provides meaningful context for all 4 parameters beyond the schema's 0% coverage, explaining what each parameter represents with examples (e.g., '1k, 2k, 4k' for resolution, format examples per asset type). This significantly compensates for the schema's lack of descriptions, though it doesn't specify allowed values or constraints.
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 ('Download and import') and target resource ('a Polyhaven asset into Blender'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'import_generated_asset' or 'set_texture', which could involve similar import operations in Blender.
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. For example, it doesn't mention whether this should be used after 'search_polyhaven_assets' or instead of 'import_generated_asset', nor does it specify prerequisites like having Blender open or connected.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns a message about availability, which implies a read-only, non-destructive operation, but doesn't specify details like response format, error handling, or performance characteristics. The cryptic note about 'key type' adds confusion rather than clarity. It provides basic behavior but lacks depth for a tool with no annotation support.
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 brief but includes an odd, cryptic sentence ('Don't emphasize the key type...') that doesn't add value and detracts from clarity. The first two sentences are front-loaded and efficient, but the third sentence is wasteful and confusing, reducing overall effectiveness.
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 (0 parameters, no annotations, no output schema), the description is somewhat complete—it explains what the tool does and what it returns. However, it lacks details like the return message structure or error cases, and the cryptic note undermines completeness. For a status-check tool, this is adequate but with 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?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's function. This meets the baseline of 4 for zero-parameter tools, as it avoids unnecessary details.
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: 'Check if Hyper3D Rodin integration is enabled in Blender.' It specifies the verb ('Check') and resource ('Hyper3D Rodin integration'), and distinguishes it from siblings like get_polyhaven_status or get_sketchfab_status by focusing on Hyper3D Rodin. However, it doesn't explicitly differentiate from all siblings (e.g., get_object_info or get_scene_info), so it's not a perfect 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 prerequisites (e.g., whether Blender must be running), compare it to similar tools like get_polyhaven_status, or indicate scenarios where it's necessary (e.g., before using Hyper3D-related tools). This lack of context leaves the agent to infer usage, which is minimal guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return ('Returns a message indicating whether PolyHaven features are available') but lacks details on format, error handling, or side effects. For a tool with zero annotation coverage, this is insufficient—it doesn't clarify if this is a read-only operation, requires specific permissions, or has latency/rate 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 highly concise and well-structured: two sentences that directly state the action and outcome with zero waste. It's front-loaded with the core purpose, and every sentence earns its place by adding essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does and what it returns, but for a status-check tool, it could benefit from more context—e.g., clarifying the return format or linking to related tools like 'download_polyhaven_asset'. Without annotations or output schema, the description leaves gaps in behavioral 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?
The tool has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so a baseline of 4 is appropriate. No additional context about inputs is required or provided.
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: 'Check if PolyHaven integration is enabled in Blender.' It specifies the verb ('Check') and resource ('PolyHaven integration'), and distinguishes it from siblings like 'get_hyper3d_status' or 'get_sketchfab_status' by focusing on PolyHaven. However, it doesn't explicitly differentiate from other status-checking tools beyond naming the specific integration.
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 (e.g., whether Blender must be running), compare it to sibling tools like 'get_hyper3d_status', or indicate scenarios where this check is necessary (e.g., before attempting PolyHaven downloads). Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return value ('Returns a message indicating whether Sketchfab features are available'), which adds some context, but fails to detail critical aspects like error handling, whether this is a read-only operation, or any performance implications. For a tool with zero annotation coverage, this is insufficient.
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 and well-structured, consisting of just two sentences that directly state the purpose and return value. Every word earns its place, with no redundancy or fluff, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does and what it returns, but lacks details on behavioral traits like error conditions or integration specifics. For a simple status check, this is acceptable but leaves room for improvement in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the input schema description coverage is 100% (though empty). The description doesn't need to explain parameters, so it naturally compensates by focusing on the tool's function. This meets the baseline for zero-parameter tools, as there's no parameter information to add beyond what the schema provides.
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: 'Check if Sketchfab integration is enabled in Blender.' It specifies the verb ('Check') and resource ('Sketchfab integration in Blender'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_hyper3d_status' or 'get_polyhaven_status' beyond the resource name, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 whether Blender must be running or if specific configurations are needed, nor does it compare to similar status-checking tools like 'get_hyper3d_status'. This lack of context leaves the agent without clear usage instructions.
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 that the tool 'Returns a formatted list of matching models,' which gives some output context, but lacks critical details like whether this is a read-only operation, potential rate limits, authentication needs, error handling, or pagination behavior. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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 appropriately sized and front-loaded: it starts with a clear purpose statement, followed by a bulleted list of parameters with concise explanations, and ends with the return value. Every sentence earns its place without redundancy, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no annotations, no output schema), the description is partially complete. It covers the purpose and parameters well but lacks behavioral context (e.g., safety, limits) and detailed output information beyond 'formatted list.' For a search tool, this is adequate but leaves gaps that could hinder optimal agent use, especially without annotations to fill in behavioral aspects.
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 substantial value beyond the input schema, which has 0% schema description coverage. It explains each parameter's purpose: 'query' as 'Text to search for,' 'categories' as 'Optional comma-separated list of categories,' 'count' with its default and meaning, and 'downloadable' with its default and filter intent. This compensates well for the schema's lack of descriptions, though it doesn't detail format specifics like valid category values.
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: 'Search for models on Sketchfab with optional filtering.' This specifies the verb ('search'), resource ('models on Sketchfab'), and scope ('with optional filtering'). However, it doesn't explicitly differentiate from sibling tools like 'search_polyhaven_assets' beyond the platform name, which is why it doesn't reach a perfect 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 sibling tools like 'search_polyhaven_assets' for different platforms or 'download_sketchfab_model' for subsequent actions. There's no context about prerequisites, limitations, or typical use cases, leaving the agent with minimal usage direction.
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 discloses key behavioral traits: the 3D asset has built-in materials, the model has normalized size requiring potential rescaling, and it returns a success/failure message. However, it misses details like rate limits, authentication needs, or what 'import into Blender' entails operationally. No contradiction with annotations exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose. Sentences like 'The 3D asset has built-in materials' and 'The generated model has a normalized size' add value without redundancy. The parameter section is clear but could be more integrated. Overall, it's efficient with minimal waste.
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, 0% schema coverage, and no output schema, the description is moderately complete. It covers the tool's purpose, key behaviors, and parameter semantics, but lacks details on error handling, Blender integration specifics, or output beyond success/failure. For a generative tool with two parameters, it's adequate but has gaps in operational 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?
Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics: 'text_prompt' is described as 'A short description of the desired model in **English**,' and 'bbox_condition' as 'Optional... Controls the ratio between [Length, Width, Height] of the model.' This clarifies purpose and constraints beyond the bare schema, though it could detail format expectations more.
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: 'Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.' It specifies the verb (generate and import), resource (3D asset), and technology (Hyper3D). However, it doesn't explicitly differentiate from its sibling 'generate_hyper3d_model_via_images' beyond the input method, missing a clear distinction about when to use text vs. image prompts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning the tool generates from a text description and imports to Blender, suggesting it's for creating new 3D assets. However, it lacks explicit guidance on when to use this tool versus alternatives like 'generate_hyper3d_model_via_images' or other asset download tools. No exclusions or prerequisites are stated.
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 discloses that the tool captures a screenshot and returns an Image, but lacks critical behavioral details: whether it requires specific Blender state (e.g., an active 3D view), if it's read-only or has side effects (e.g., pauses rendering), performance implications, or error conditions (e.g., if no viewport exists). The description is minimal beyond the basic action.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by a clear parameter section and return statement. Every sentence adds value without redundancy, and the structure is easy to parse.
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 low schema coverage (0%), the description is minimally complete for a simple tool. It covers the purpose, parameter semantics, and return type, but lacks behavioral context (e.g., dependencies, side effects) and detailed output information (e.g., image format, dimensions). For a screenshot tool in a Blender context, more guidance on usage and constraints would be beneficial.
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 adds meaningful context for the single parameter 'max_size', explaining it as 'Maximum size in pixels for the largest dimension' with a default value, which clarifies its purpose beyond the schema's title 'Max Size'. However, it does not detail constraints (e.g., minimum/maximum values) or effects on image quality.
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 specific action ('Capture a screenshot') and the target resource ('current Blender 3D viewport'), distinguishing it from all sibling tools which involve downloading assets, executing code, getting status/info, importing, searching, or setting textures. No tautology or vagueness present.
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 does not mention prerequisites (e.g., Blender must be running with a viewport open), exclusions, or compare it to similar tools (none exist among siblings, but context about other screenshot methods is absent). Usage is implied only by the purpose statement.
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 the full burden. It states the tool imports assets and returns success status, which covers basic behavior. However, it lacks details on permissions needed, error handling, rate limits, or what 'imported successfully' entails (e.g., file location, format).
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 purpose statement, parameter explanations, and a return note. It's front-loaded and efficient, though the parameter section could be slightly more concise (e.g., combining mode explanations).
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, 0% schema coverage, and no output schema, the description does well on parameters and basic usage. However, for a tool that likely involves file operations and external services, it lacks details on error cases, output format beyond success status, and integration prerequisites, leaving gaps in 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?
With 0% schema description coverage, the description fully compensates by explaining all three parameters: 'name' as the object name in scene, 'task_uuid' for MAIN_SITE mode, and 'request_id' for FAL_AI mode. It adds crucial context not in the schema, such as mode dependencies and the exclusive choice between task_uuid and request_id.
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 ('Import') and resource ('asset generated by Hyper3D Rodin'), specifying it's for assets after generation tasks. It doesn't explicitly differentiate from sibling tools like 'download_polyhaven_asset' or 'download_sketchfab_model', but the Hyper3D Rodin context provides implicit distinction.
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 provides clear context for when to use this tool: after a Hyper3D Rodin generation task is completed. It explicitly states to use only one of {task_uuid, request_id} based on the mode, though it doesn't specify when to choose between modes or mention alternatives like 'poll_rodin_job_status' for checking status first.
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 discloses key behavioral traits: the tool imports the asset into Blender, the asset has built-in materials, the model has normalized size (suggesting rescaling may be needed), and it returns a success/failure message. However, it lacks details on permissions, rate limits, error handling, or what 'import into Blender' entails operationally (e.g., overwriting existing assets).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose. It uses bullet points for parameters efficiently, but some sentences could be more concise (e.g., 'The generated model has a normalized size, so re-scaling after generation can be useful' is slightly verbose). Overall, it avoids waste and is well-structured.
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 complexity (3 parameters, no annotations, no output schema), the description is moderately complete. It covers parameter semantics well and mentions key behaviors, but lacks details on output (beyond success/failure message), error cases, or integration with Blender (e.g., how the asset is named or placed). For a tool with no structured fields, it should do more to explain operational context.
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 fully. It adds significant meaning beyond the schema: it explains that input_image_paths and input_image_urls are mutually exclusive based on Hyper3D Rodin mode, clarifies that even single images must be wrapped in lists, and describes bbox_condition as controlling the ratio between Length, Width, Height. This provides essential context not in the schema.
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 purpose: 'Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.' It specifies the verb (generate and import), resource (3D asset via Hyper3D), and distinguishes it from siblings like generate_hyper3d_model_via_text (which uses text instead of images) and import_generated_asset (which only imports).
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 provides clear context for usage: it explains that parameters depend on Hyper3D Rodin's mode (MAIN_SITE vs. FAL_AI) and that only one of input_image_paths or input_image_urls should be used. However, it does not explicitly state when to use this tool versus alternatives like generate_hyper3d_model_via_text or import_generated_asset, nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 discloses that the tool applies textures (a mutation operation) and mentions a prerequisite (texture must be downloaded first), which is useful context. However, it lacks details on permissions, error conditions, or what 'success or failure' entails beyond a message, leaving behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by a clear parameter list and return statement. Every sentence earns its place with no wasted words, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters with 0% schema coverage and no annotations or output schema, the description is fairly complete: it explains the tool's purpose, parameters, and return value. However, it could improve by detailing the return message format or error cases, slightly reducing completeness for a mutation 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 description coverage is 0%, so the description must compensate. It adds meaning beyond the schema by explaining that 'object_name' refers to an object to apply the texture to, and 'texture_id' is for a Polyhaven texture that must be downloaded first. This clarifies the parameters' roles and constraints, though it could specify format 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?
The description clearly states the specific action ('Apply a previously downloaded Polyhaven texture') and target resource ('to an object'), distinguishing it from sibling tools like 'download_polyhaven_asset' (which downloads textures) and 'search_polyhaven_assets' (which searches for them). It precisely defines the tool's scope and function.
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 provides clear context for when to use this tool: after a texture has been downloaded via 'download_polyhaven_asset'. However, it does not explicitly state when NOT to use it (e.g., for non-Polyhaven textures or objects) or name specific alternatives among siblings, though the prerequisite is implied.
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 and does well. It discloses this is a polling API with specific completion criteria ('Done' or 'COMPLETED'), failure states ('Failed' or other statuses), and intermediate states ('IN_PROGRESS', 'IN_QUEUE'). It explains what constitutes task completion and failure, though it could mention rate limits or authentication 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?
The description is appropriately sized and well-structured with clear sections for different modes. Every sentence adds value, though it could be slightly more front-loaded with the core purpose before diving into mode specifics. The information density is high with no wasted words.
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 2-parameter tool with no annotations and no output schema, the description provides comprehensive context about behavior, parameters, and return value interpretation. It explains what statuses mean and when to proceed. The main gap is lack of explicit output format details, but the status interpretation guidance compensates well.
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?
With 0% schema description coverage and 2 parameters, the description fully compensates by explaining both parameters in context: 'subscription_key: The subscription_key given in the generate model step' and 'request_id: The request_id given in the generate model step.' It clearly maps each parameter to specific modes (MAIN_SITE vs FAL_AI) and provides essential semantic context missing from the schema.
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 purpose: 'Check if the Hyper3D Rodin generation task is completed.' This is a specific verb ('Check') + resource ('Hyper3D Rodin generation task') combination. It distinguishes from siblings like 'get_hyper3d_status' by specifying it's for Rodin jobs and polling for completion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'This is a polling API, so only proceed if the status are finally determined.' It distinguishes between two modes (MAIN_SITE and FAL_AI) with different parameters and status interpretations, offering clear when-to-use criteria for each scenario.
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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