BlenderMCP

by Namikaze-bit

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, such as downloading assets from different sources (Polyhaven, Sketchfab), generating models via Hyper3D, and getting scene/object info. However, some overlap exists between 'download_polyhaven_asset' and 'set_texture' (both involve Polyhaven textures) and between Hyper3D generation tools and 'import_generated_asset', which could cause minor confusion.
Naming Consistency4/5
Tool names largely follow a consistent verb_noun pattern (e.g., 'download_polyhaven_asset', 'get_scene_info', 'search_sketchfab_models'), with clear actions and targets. Minor deviations include 'execute_blender_code' (which uses 'execute' instead of 'run' or similar) and 'poll_rodin_job_status' (which mixes 'poll' with 'job_status'), but overall naming is predictable and readable.
Tool Count4/5
With 17 tools, the count is slightly high but reasonable for a Blender integration server that covers multiple external services (Polyhaven, Sketchfab, Hyper3D) and core Blender operations. Each tool appears to serve a specific function, though some could potentially be consolidated (e.g., status-checking tools).
Completeness3/5
The toolset covers key areas like asset downloading, 3D model generation, and scene inspection, but has notable gaps. For example, there are no tools for basic Blender operations (e.g., create/delete objects, modify materials, render scenes) or managing downloaded assets beyond applying textures. This limits the server's ability to handle full Blender workflows without external tools.
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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

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

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

How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Get a list') but doesn't describe how the list is returned (e.g., format, pagination), error conditions, rate limits, or authentication needs. For a tool with zero annotation coverage, this is a significant gap in 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?
The description is appropriately sized with two sentences: one stating the purpose and another detailing parameters. It's front-loaded with the core function. There's minimal waste, though it could be slightly more structured (e.g., bullet points for parameter details).
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 (1 parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the parameter but lacks behavioral context and usage guidelines. Without annotations or output schema, it should do more to compensate, making it adequate but with clear room for improvement.
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 documents the single parameter 'asset_type' and provides its possible values (hdris, textures, models, all), which adds meaning beyond the schema's minimal title. However, it doesn't explain the semantics of 'all' or default behavior, keeping it at a baseline 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('list of categories for a specific asset type on Polyhaven'), making the purpose understandable. It distinguishes from siblings like 'search_polyhaven_assets' by focusing on categories rather than assets themselves. However, it doesn't explicitly differentiate from other category-related tools if any existed, keeping it at 4 instead of 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, context for selecting asset types, or compare with sibling tools like 'search_polyhaven_assets' which might overlap in functionality. This leaves the agent without explicit usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions returns a list with basic information but lacks details on pagination, rate limits, authentication needs, or error handling. For a search tool with no annotation coverage, this is a significant gap in behavioral disclosure.
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: purpose, parameters, and returns. It's front-loaded with the main function. Some minor redundancy exists (e.g., 'optional filtering' could be inferred from parameters), but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, 0% schema coverage, and no output schema, the description is incomplete. It covers basic purpose and parameters but lacks details on return structure, error cases, or integration with siblings like download_polyhaven_asset. For a search tool in this context, more guidance is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains asset_type options (hdris, textures, models, all) and categories as comma-separated list, adding meaning beyond the schema's generic titles. However, it doesn't clarify format for categories or default behaviors, leaving some gaps.
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 (search) and resource (assets). It distinguishes from siblings like download_polyhaven_asset (which retrieves) and search_sketchfab_models (which searches a different platform). However, it doesn't explicitly contrast with get_polyhaven_categories, which might provide related 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 no guidance on when to use this tool versus alternatives. It mentions optional filtering but doesn't specify scenarios for using search_polyhaven_assets over get_polyhaven_categories for category info or download_polyhaven_asset for retrieval. 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 the full burden of behavioral disclosure. It states the tool checks integration status and returns a message, but lacks details on permissions, rate limits, error handling, or what the message format entails. The cryptic note about 'Don't emphasize the key type...' adds confusion rather than clarity, failing to compensate for the missing annotations.
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 relatively concise with two main sentences, but the second sentence about 'Don't emphasize the key type...' is vague and doesn't add value, reducing efficiency. It could be more front-loaded by starting with the core purpose without the cryptic note.
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 the return type (a message), but lacks context on integration specifics or error cases. Without annotations or output schema, it should provide more behavioral detail to be fully 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?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info, which is appropriate, earning a baseline 4 for not introducing unnecessary details. It avoids redundancy with the schema.
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, context, or exclusions, such as whether it should be used before other Hyper3D-related tools or in specific Blender states. With no usage instructions, it relies on the agent to infer from the purpose alone.
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 'downloads and imports' and 'returns a message indicating success or failure', but fails to cover critical aspects like whether this is a read-only or destructive operation, potential rate limits, authentication needs, or how errors are handled. This leaves significant gaps for a tool that interacts with external 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, starting with a clear purpose statement followed by a structured parameter list. Every sentence adds value, with no wasted words, though the return statement could be slightly more informative.
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 of downloading and importing external assets, no annotations, and no output schema, the description is moderately complete. It covers the basic purpose and parameters but lacks details on behavioral traits, error handling, and integration with Blender, which are crucial for safe and effective use.
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 effectively adds meaning by explaining each parameter's purpose, providing examples for 'resolution' and 'file_format', and noting that 'file_format' is optional. This clarifies semantics beyond the bare schema, though it could benefit from more detail on 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 tool's purpose with specific verbs ('download and import') and resources ('Polyhaven asset into Blender'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'import_generated_asset' or 'download_sketchfab_model', which handle similar operations for different sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'import_generated_asset' or 'download_sketchfab_model'. It also lacks information about prerequisites, such as whether Blender needs to be running or if specific permissions are required, leaving the agent with no 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, so the description carries the full burden of behavioral disclosure. It mentions executing code 'in Blender' and advises step-by-step chunking, but lacks critical details: it doesn't specify execution context (e.g., in Blender's Python environment), safety considerations (e.g., potential for destructive operations), permissions needed, rate limits, or error handling. 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 concise and front-loaded: the first sentence states the purpose, followed by usage advice and parameter details. It avoids redundancy, but the parameter section could be integrated more smoothly. 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.
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), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It misses critical context: execution environment details, safety warnings, return values, or error behavior. This leaves significant gaps for an AI agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It lists the parameter 'code' and explains it as 'The Python code to execute,' adding basic meaning beyond the schema's title 'Code.' However, it doesn't provide details on code format, constraints, or examples. With only one parameter, this is minimally adequate but leaves gaps.
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 distinct from sibling tools that handle downloads, searches, imports, or status checks. However, it doesn't explicitly differentiate from potential code-related siblings, though none are listed.
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.' This implies a best practice for using the tool but doesn't specify when to use it versus alternatives (e.g., for automation vs. other Blender operations) or any prerequisites. No explicit alternatives or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states it retrieves 'detailed information' without specifying what that includes (e.g., scene name, objects, render settings). It lacks behavioral details like whether it's read-only (implied but not stated), if it requires Blender to be active, or what format the information is returned in.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is front-loaded with the core action and resource, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and no parameters, the description is insufficient. It doesn't explain what 'detailed information' entails, leaving the agent uncertain about the return value's structure or content, which is critical for effective tool use in this 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 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's function, which aligns with the baseline expectation for zero-parameter tools.
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'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_object_info' or 'get_hyper3d_status', which also retrieve information but for different resources.
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 what specific information is returned, whether it's for scene properties, objects, or settings, nor does it reference sibling tools that might serve similar purposes in different contexts.
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 that the tool 'Returns a formatted list of matching models' which gives some output information, but doesn't describe important behaviors like pagination, rate limits, authentication requirements, error conditions, or what 'formatted' means. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
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 list. Each bullet point is concise and informative. The final sentence about return values is appropriately brief. There's no wasted text, though the structure could be slightly more front-loaded with key behavioral 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 complexity of a search tool with 4 parameters, no annotations, and no output schema, the description is moderately complete. It covers parameters well but lacks important context about search behavior, result formatting, limitations, and integration with sibling tools. The absence of output schema means the description should ideally say more about what 'formatted list' contains, but it provides the minimum for basic usage.
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 compensates well by providing clear explanations for all 4 parameters. It explains what each parameter controls (search text, categories filter, result count limit, downloadable filter), specifies defaults for 'count' and 'downloadable', and clarifies that 'categories' is comma-separated. This adds substantial value beyond the bare schema.
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 as 'Search for models on Sketchfab with optional filtering.' This specifies the verb ('search'), resource ('models on Sketchfab'), and scope ('with optional filtering'). It distinguishes from obvious siblings like 'download_sketchfab_model' but doesn't explicitly differentiate from 'search_polyhaven_assets' beyond the platform name.
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 search tool should be preferred over other search tools like 'search_polyhaven_assets' or when to transition from search results to download actions. There's no context about prerequisites, limitations, or typical use cases.
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 access rights and downloadability as prerequisites, which is helpful, but fails to describe key behaviors such as what 'import' entails (e.g., file format, destination), error handling, or rate limits. This leaves significant gaps 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 appropriately sized with three sentences: the first states the purpose, the second lists parameters, and the third covers returns and prerequisites. It is front-loaded with the main action, though the parameter listing could be integrated more smoothly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a download/import operation, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on what 'import' means, the output format beyond a success/failure message, error conditions, or integration with sibling tools, 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.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It adds meaning by explaining that 'uid' is 'the unique identifier of the Sketchfab model', which clarifies the parameter's purpose beyond the schema's basic type. However, it does not provide examples or format details, such as what a valid UID looks like.
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 does not explicitly differentiate from sibling tools like 'search_sketchfab_models' or 'import_generated_asset', which would require more detailed comparison.
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 'model must be downloadable and you must have proper access rights', which provides some context for prerequisites. However, it lacks explicit guidance on when to use this tool versus alternatives like 'search_sketchfab_models' or 'import_generated_asset', leaving the agent to infer based on the action needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves 'detailed information' but doesn't specify what that includes (e.g., object type, location, materials), whether it's read-only, potential errors (e.g., if the object doesn't exist), or performance considerations. This leaves significant gaps for a tool that presumably queries scene data.
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 clearly states the purpose, followed by a concise parameter explanation. There's no redundant or verbose language, and every sentence adds value. The structure is logical and 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 the tool's moderate complexity (querying a specific object) and lack of annotations/output schema, the description is minimally adequate. It covers the basic purpose and parameter but misses behavioral details like return format, error handling, and differentiation from siblings. Without output schema, it should ideally hint at what information is returned.
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: The name of the object to get information about.' Since schema description coverage is 0% (the schema has no descriptions), this compensates well by explaining what the parameter represents. However, it doesn't provide format examples or 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: 'Get detailed information about a specific object in the Blender scene.' It uses a specific verb ('Get') and resource ('object in the Blender scene'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_scene_info' beyond the object-specific focus.
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 might provide broader scene information) or other object-related operations. There's no context about prerequisites, limitations, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Returns a message indicating whether PolyHaven features are available,' which implies a read-only, non-destructive operation, but doesn't specify details like error handling, latency, or authentication needs. For a tool with zero 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences that are front-loaded with the core purpose ('Check if PolyHaven integration is enabled in Blender') followed by the return behavior. Every word earns its place, with no redundancy or fluff, making it highly efficient and 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 the tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and return value, but lacks context on integration specifics, error cases, or how it fits with siblings. For a status-check tool, more details on what 'enabled' entails would improve completeness, but it meets the baseline for simple 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?
The tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The description appropriately doesn't discuss parameters, which is efficient. A baseline of 4 is given for zero-parameter tools when the schema is fully covered, as it avoids unnecessary repetition.
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 what the tool does ('Check if PolyHaven integration is enabled in Blender') with a specific verb ('Check') and resource ('PolyHaven integration'), and distinguishes it from siblings like 'get_hyper3d_status' or 'get_sketchfab_status' by specifying the PolyHaven context. However, it doesn't fully differentiate from all siblings (e.g., 'get_object_info' or 'get_scene_info' might also check statuses), 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), exclusions, or comparisons to similar tools like 'get_hyper3d_status' or 'get_sketchfab_status'. The agent must infer usage from the purpose alone, which is insufficient for clear decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Returns a message indicating whether Sketchfab features are available,' which implies a read-only, non-destructive operation. However, it lacks details on error handling, performance characteristics (e.g., latency), or any side effects (e.g., whether it triggers network calls or logs data). For a tool with zero annotation coverage, this is minimal but not entirely absent.
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 front-loaded: two sentences that directly state the purpose and return value without any fluff. Every sentence earns its place by providing essential information, 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.
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 adequate but has gaps. It explains what the tool does and what it returns, but lacks context on integration details (e.g., what 'enabled' means technically) or error scenarios. For a status-checking tool, this is minimally viable but could be more informative about the return message format or conditions.
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 has 100% description coverage (though empty). The description doesn't need to explain parameters, so it appropriately avoids redundancy. A baseline of 4 is applied as per the rules for zero-parameter tools, since no parameter information is 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 tool's purpose: 'Check if Sketchfab integration is enabled in Blender.' This is a specific verb ('Check') and resource ('Sketchfab integration'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_hyper3d_status' or 'get_polyhaven_status', which have similar checking purposes for 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 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), exclusions, or how it relates to sibling tools like 'download_sketchfab_model' or 'search_sketchfab_models'. The agent must infer usage from the purpose alone.
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 the tool 'Returns a message indicating success or failure,' which adds some context about output behavior. However, it lacks details on permissions needed, whether the operation is reversible, potential side effects on the object, or error conditions beyond failure messages, leaving significant gaps 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.
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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a mutation tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is moderately complete. It covers the basic purpose, parameters, and return type, but lacks details on behavioral traits like error handling, dependencies, or integration with sibling tools, which would be helpful for an AI 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%, so the description must compensate. It adds meaningful semantics: object_name is 'Name of the object to apply the texture to,' and texture_id is 'ID of the Polyhaven texture to apply (must be downloaded first).' This clarifies purpose and prerequisites beyond the bare schema, though it doesn't specify format constraints (e.g., string patterns) or examples.
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 ('Apply') and target ('a previously downloaded Polyhaven texture to an object'), distinguishing it from sibling tools like download_polyhaven_asset or search_polyhaven_assets. However, it doesn't explicitly differentiate from tools like import_generated_asset or execute_blender_code that might also affect objects, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage 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 texture must be 'downloaded first' (referencing download_polyhaven_asset), but it doesn't explicitly state when to use this tool versus alternatives like execute_blender_code for texture application or get_object_info for checking object details. No exclusions or clear alternatives are provided.
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 full burden. It discloses the core behavior (capturing viewport screenshot) and return type (Image), but lacks details about permissions needed, whether it pauses rendering, rate limits, or what happens if viewport is empty. It provides basic behavioral context but misses operational details.
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 perfectly front-loaded with the core purpose in the first sentence, followed by clean parameter documentation. Every sentence earns its place with no redundant information. The structure with clear parameter listing is efficient and scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and low schema coverage, the description provides adequate basics but lacks completeness. It explains what the tool does and the parameter meaning, but doesn't cover return format details (image type, dimensions), error conditions, or integration context with Blender's state.
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 and only one parameter, the description adds significant value by explaining what max_size means ('Maximum size in pixels for the largest dimension') and providing the default value (800). This compensates well for the schema's lack of descriptions, though it doesn't explain constraints like minimum/maximum values.
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 target resource ('current Blender 3D viewport'), distinguishing it from sibling tools like get_scene_info or get_object_info. It uses precise technical terminology that identifies the exact functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (when working with Blender viewports) but provides no explicit guidance on when to use this tool versus alternatives like get_scene_info. There's no mention of prerequisites, limitations, or comparison to other screenshot-related tools that might exist.
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 discloses the tool's behavior as an import operation with a success/failure return ('Return if the asset has been imported successfully'), but lacks details on error conditions, permissions needed, or what 'imported' entails (e.g., file location, scene integration). This is adequate but leaves gaps 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by a clear parameter list and usage rules. Every sentence adds value, with no redundancy, though it could be slightly more structured (e.g., bullet points for parameters).
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 a fair job: it covers purpose, parameters, and usage guidelines. However, as a mutation tool ('Import'), it lacks details on behavioral aspects like error handling, side effects, or return format beyond success indication, leaving room for improvement in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates well by explaining all three parameters: 'name' as the object name in scene, and 'task_uuid' and 'request_id' as IDs from specific generation steps with mode-based usage rules. It adds meaningful context beyond the schema's basic titles, though it doesn't specify formats or constraints for the parameters.
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 after the generation task is completed'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'download_polyhaven_asset' or 'download_sketchfab_model' which also import assets but from different sources.
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: it specifies when to use this tool ('after the generation task is completed') and includes critical conditional logic ('Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!'), which helps the agent choose correctly between parameters and avoid misuse.
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 full burden. It discloses that the tool generates assets with built-in materials, normalizes size (requiring potential rescaling), and imports into Blender. However, it lacks details on permissions, rate limits, error conditions, or what 'success or failure' entails. The description adds some behavioral context but leaves gaps 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 appropriately sized with four sentences and a parameters section. It is front-loaded with the core purpose, followed by key behavioral details and parameter explanations. Some redundancy exists (e.g., '3D asset' repeated), but overall it is 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, no output schema, and 0% schema coverage, the description provides adequate basics: purpose, key behaviors, and parameter meanings. However, for a tool that generates and imports 3D assets (a complex mutation), it lacks details on output format, error handling, prerequisites, or integration with other tools like 'get_hyper3d_status'. It is minimally viable 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?
Schema description coverage is 0%, so the description must compensate. It explains that 'text_prompt' is a short English description of the desired model and 'bbox_condition' (optional) controls length/width/height ratio as a list of 3 floats. This adds meaningful semantics beyond the bare schema, though it could specify format examples or constraints more precisely.
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 ('Generate 3D asset using Hyper3D'), resource ('3D asset'), and scope ('by giving description of the desired asset, and import the asset into Blender'). It distinguishes from siblings like 'generate_hyper3d_model_via_images' by specifying text-based generation and from 'import_generated_asset' by combining generation with import.
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: for generating 3D assets from text descriptions and importing them into Blender. It implicitly distinguishes from image-based generation via sibling 'generate_hyper3d_model_via_images', but does not explicitly state when not to use it or mention alternatives like downloading existing models from Polyhaven or Sketchfab.
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 that the tool generates AND imports, mentions built-in materials, normalized size requiring rescaling, and success/failure messaging. It doesn't cover rate limits, authentication needs, or error specifics, but provides substantial operational context beyond basic functionality.
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: purpose statement first, key behavioral notes (materials, scaling), then parameter details with clear formatting. Every sentence adds value—no redundancy or fluff. The parameter section uses bold and lists for readability while maintaining brevity.
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 no annotations, 0% schema coverage, and no output schema, the description does an excellent job covering purpose, behavior, and parameters. It mentions the return is a success/failure message. Minor gaps: no explicit error handling, no details on Blender import specifics (e.g., scene placement), and sibling context could be more explicit. Still, highly complete for the complexity.
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: it clarifies the mutual exclusivity of input_image_paths vs input_image_urls based on Hyper3D Rodin mode, specifies absolute path requirement and list wrapping, and details bbox_condition as optional 3-int list controlling [Length, Width, Height] ratio. This adds crucial meaning not in the bare 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 specific action: 'Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.' It distinguishes from sibling tools like 'generate_hyper3d_model_via_text' by specifying image-based generation, and from 'import_generated_asset' by including the generation step. The verb+resource+destination combination is precise and unambiguous.
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: for generating 3D assets from images via Hyper3D and importing to Blender. It implicitly distinguishes from text-based generation (sibling tool 'generate_hyper3d_model_via_text') and from standalone import ('import_generated_asset'). However, it doesn't explicitly state when NOT to use it or mention all relevant alternatives like downloading existing models from Polyhaven/Sketchfab.
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 the full burden of behavioral disclosure. It effectively describes the tool as a polling API, explains that it returns status lists or single statuses depending on the mode, defines completion criteria ('Done' or 'COMPLETED'), failure conditions, and advises to proceed only after final determination. It lacks details on rate limits or error handling, but covers core behavior well.
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 for different modes, using bullet-like formatting for parameters and status interpretations. It is appropriately sized for the complexity, with no redundant sentences. However, it could be slightly more front-loaded by stating the dual-mode nature earlier, but overall it's efficient and informative.
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 (two operational modes with different parameters and statuses), no annotations, 0% schema coverage, and no output schema, the description does an excellent job of providing necessary context. It explains parameters, return values, completion criteria, and usage instructions. It slightly lacks details on error responses or polling intervals, but is largely complete for effective use.
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 does so by clearly explaining the two parameters: 'subscription_key' for MAIN_SITE mode and 'request_id' for FAL_AI mode, linking them to previous steps ('given in the generate model step'). This adds essential meaning beyond the bare schema, making the parameters understandable and actionable.
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.' It specifies the verb ('check') and resource ('Hyper3D Rodin generation task'), and distinguishes it from siblings like 'get_hyper3d_status' by focusing on polling for completion rather than general status retrieval.
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 guidelines: it specifies when to use the tool (to poll for task completion), when not to proceed (until status is 'finally determined'), and distinguishes between two modes (MAIN_SITE and FAL_AI) with different parameters and status interpretations, offering clear alternatives within the tool itself.
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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