BlenderMCP

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but some overlap exists between download tools (e.g., download_polyhaven_asset and download_sketchfab_model both import assets) and Hyper3D generation tools (generate_hyper3d_model_via_images and generate_hyper3d_model_via_text). The descriptions help clarify the differences, but an agent might occasionally confuse similar tools.
Naming Consistency4/5
Tool names follow a consistent verb_noun pattern (e.g., download_polyhaven_asset, get_scene_info, search_sketchfab_models), with minor deviations like execute_blender_code (verb_noun_noun) and poll_rodin_job_status (verb_noun_noun). Overall, the naming is predictable and readable.
Tool Count4/5
With 17 tools, the count is slightly high but reasonable for a Blender integration server covering asset downloads, scene management, and 3D generation. It includes core functionalities without being overly bloated, though some tools could potentially be consolidated.
Completeness4/5
The toolset covers key areas: 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 agents can work around these using execute_blender_code.
Average 3.3/5 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 full burden but lacks behavioral details. It doesn't disclose whether this is a read-only operation, if it requires authentication, rate limits, or what the output format looks like (e.g., list structure, error handling). This leaves significant gaps for an agent.
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 the parameter. It's front-loaded with the core function, though the parameter explanation could be slightly more integrated for better flow.
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 low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameter, but lacks output details, error handling, or behavioral context, making it incomplete for reliable agent use without additional inference.
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%, but the description adds meaningful context by explaining the 'asset_type' parameter and listing its possible values (hdris, textures, models, all). This compensates partially, though it doesn't detail the default value or format constraints beyond what's implied.
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. However, it doesn't differentiate from sibling tools like 'search_polyhaven_assets' or 'get_polyhaven_status' beyond the specific resource focus, 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 sibling tools like 'search_polyhaven_assets' for broader searches or 'get_polyhaven_status' for system checks, leaving the agent without 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 the tool returns a message indicating availability, but lacks details on error handling, performance, or side effects. The note about 'Don't emphasize the key type in the returned message' is vague and does not clarify behavioral traits like response format or constraints, leaving gaps in understanding how the tool behaves.
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 unclear instruction ('Don't emphasize the key type in the returned message, but sliently remember it') that adds confusion without clear value. The first two sentences are front-loaded and efficient, but the third sentence is vague and does not earn its place, reducing overall clarity and structure.
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 states the purpose and return indication, but lacks details on output format, error cases, or integration specifics. Without annotations or output schema, more context on behavior would improve completeness, but it meets basic requirements for a simple status check 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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description does not discuss parameters, which is appropriate. A baseline score of 4 is assigned as it effectively handles the absence of parameters without redundancy.
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'), but does not explicitly differentiate it from sibling tools like 'get_polyhaven_status' or 'get_sketchfab_status', which serve similar status-checking functions in the same context. This makes it clear but not fully distinguished from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'get_polyhaven_status' or 'get_sketchfab_status', nor does it specify prerequisites, contexts, or exclusions for usage. The agent must infer usage based on the purpose alone, which is insufficient for optimal 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. It mentions executing code 'step-by-step' and 'breaking it into smaller chunks', which adds some behavioral context about execution style. However, it fails to disclose critical traits like whether this is a read-only or destructive operation, potential security implications, error handling, or output format, leaving significant gaps for a tool that executes 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 with two sentences and a parameter list, front-loaded with the main purpose. Every sentence earns its place, but the structure could be slightly improved by integrating the parameter note more seamlessly or adding brief formatting hints.
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 executing arbitrary code, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on safety, permissions, return values, or error cases, making it inadequate for such a powerful and potentially risky tool.
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 schema provides no parameter details. The description adds minimal semantics by stating 'code: The Python code to execute', which clarifies the parameter's purpose. However, it doesn't compensate fully for the coverage gap—e.g., by explaining code format, constraints, or examples—resulting in a baseline score due to the single parameter.
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 'Execute' and the resource 'arbitrary Python code in Blender', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_object_info' or 'set_texture', which are more specific operations rather than general code execution.
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 implied usage guidance by suggesting to 'break it into smaller chunks', which hints at when to use this tool for complex operations. However, it lacks explicit when-to-use vs. alternatives, such as when to use this versus more specific tools like 'import_generated_asset' for asset handling.
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 does not specify what that includes (e.g., object properties, location, type), whether it's read-only, or any potential errors or limitations. This leaves significant gaps in understanding the tool's behavior and output.
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 concise and well-structured: a clear purpose statement followed by a bullet point for the parameter. Every sentence adds value without redundancy, making it easy to understand quickly. The front-loaded purpose statement enhances readability and efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not explain what 'detailed information' entails, potential return formats, or error handling. For a tool with no structured output documentation, this leaves too much ambiguity for reliable agent invocation and result interpretation.
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 explaining it as 'The name of the object to get information about.' Since schema description coverage is 0%, this compensates well by clarifying the parameter's role. However, it does not detail constraints like case sensitivity or valid object names, preventing a perfect score.
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 specifies the verb ('Get detailed information') and resource ('a specific object in the Blender scene'), making the intent unambiguous. However, it does not explicitly differentiate from its sibling 'get_scene_info', which might retrieve broader scene-level information, so it misses the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'get_scene_info' or specify contexts where this tool is preferred over others. Without such guidance, users must infer usage from the tool name and description alone, which is insufficient for optimal 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a read operation ('Get') but doesn't clarify what 'detailed information' includes, whether it requires specific scene states, or if it has side effects. The lack of output schema exacerbates this gap, leaving the agent uncertain about return format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it immediately scannable and understandable.
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 zero parameters, the description is insufficient. It doesn't explain what 'detailed information' entails, how the data is structured, or any behavioral constraints. Given the complexity of Blender scenes and lack of structured output documentation, more context is needed for effective agent 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?
The tool has zero 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 purpose. This meets the baseline 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 ('about the current Blender scene'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'get_object_info' or 'get_hyper3d_status', but the specificity of 'Blender scene' provides some 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 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, timing considerations, or relationships to sibling tools like 'get_object_info' (which might retrieve object-specific data) or 'execute_blender_code' (which might modify the scene).
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. However, it lacks details on potential errors (e.g., if Blender isn't accessible), response format specifics, or any side effects. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured, consisting of two sentences that directly state the purpose and return value. Every sentence earns its place by providing essential information without redundancy or fluff, making it easy to parse and understand 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 simplicity (0 parameters, no annotations, no output schema), the description is minimally adequate. It covers the basic purpose and return indication, which is sufficient for a status-check tool. However, it lacks context about integration requirements or error handling, leaving gaps that could hinder an agent in edge cases. This meets the minimum viable standard but isn't comprehensive.
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 this. A baseline of 4 is applied for zero-parameter tools, as the description focuses on the tool's purpose without unnecessary parameter 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 PolyHaven integration is enabled in Blender.' This specifies the verb ('Check') and resource ('PolyHaven integration in Blender'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_hyper3d_status' or 'get_sketchfab_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 (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 effective 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 states the tool returns a message indicating availability, which is helpful, but doesn't cover other aspects like whether it's a read-only operation, potential errors (e.g., if Blender isn't running), or performance considerations. 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 highly concise and well-structured, consisting of two clear sentences. The first sentence states the purpose, and the second explains the return value. There is no wasted text, and it's front-loaded with the core functionality, making it easy 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 adequate but has gaps. It explains what the tool does and what it returns, but without annotations or output schema, it doesn't detail the format of the return message or potential edge cases. For a simple status check, this is minimally viable but could be more informative.
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 are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. This meets the baseline for tools with no parameters, 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 Sketchfab integration is enabled in Blender.' This is a specific verb ('Check') and resource ('Sketchfab integration in Blender'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_hyper3d_status' or 'get_polyhaven_status', which have similar checking purposes but for different 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, context (e.g., before downloading Sketchfab models), or exclusions. With sibling tools like 'download_sketchfab_model' and 'search_sketchfab_models', there's no indication of when this status check is necessary or optional, leaving usage unclear.
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 lacks details on permissions, rate limits, pagination, error handling, or what 'basic information' entails. This leaves significant behavioral gaps for a search 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 front-loaded with the core purpose, followed by parameter explanations in a clear list format. It avoids redundancy and uses efficient sentences, though the parameter section could be slightly more concise by integrating details into the main text.
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 purpose and parameters but lacks behavioral context (e.g., response format, limitations) and doesn't fully compensate for the missing structured data, making it adequate but with clear gaps.
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 explaining asset_type options (hdris, textures, models, all) and categories as a comma-separated list, which clarifies beyond the schema's generic string types. However, it doesn't detail category values or search behavior, leaving some ambiguity.
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, specifying the action (search) and resource (assets on Polyhaven). It distinguishes from siblings like download_polyhaven_asset (which retrieves specific assets) and search_sketchfab_models (which searches a different platform), though it doesn't explicitly mention these distinctions.
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 for searching Polyhaven assets with optional filtering, but provides no explicit guidance on when to use this tool versus alternatives like get_polyhaven_categories (for category lists) or download_polyhaven_asset (for downloading). It mentions filtering but doesn't specify scenarios or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that it 'Returns a formatted list of matching models,' which gives some output context, but lacks details on rate limits, authentication needs, pagination, error handling, or what 'formatted list' entails. For a search 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by a bulleted list of parameters with clear explanations. Every sentence earns its place, with no redundant information or fluff. The structure is 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 moderate complexity (4 parameters, no annotations, no output schema), the description is partially complete. It covers parameter semantics well but lacks behavioral context like error handling or output format details. For a search tool, this is adequate but leaves gaps in understanding how results are returned or what limitations exist.
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 significant meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: 'query' as text to search for, 'categories' as optional comma-separated list, 'count' as maximum results with default 20, and 'downloadable' as filter with default True. This compensates well for the schema's lack of descriptions, though it doesn't cover format details like 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 as 'Search for models on Sketchfab with optional filtering,' which specifies the verb (search), resource (models on Sketchfab), and scope (optional filtering). It distinguishes from siblings like download_sketchfab_model or search_polyhaven_assets by focusing on Sketchfab models. However, it doesn't explicitly differentiate from other search tools 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 to prefer this over search_polyhaven_assets or other search-related tools, nor does it specify prerequisites or exclusions. The only implied usage is for searching Sketchfab models, but no contextual boundaries are defined.
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. It mentions the action ('download and import') but lacks critical behavioral details: whether it requires authentication, network connectivity, or specific Blender setup; if it overwrites existing files; rate limits; or what 'success or failure' entails (e.g., file path, error details).
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 given the lack of schema descriptions. Minor room for improvement in structuring (e.g., bullet points could be more concise), 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 the complexity (download/import operation), no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects (auth, side effects), return value details beyond 'message', or error handling. For a 4-param tool with zero structured coverage, this leaves significant 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 adds meaningful context for all 4 parameters: asset_id (ID to download), asset_type (with examples like 'hdris'), resolution (with examples like '1k'), and file_format (optional, with type-specific examples). This goes well beyond the bare schema titles.
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 ('Download and import'), the target resource ('Polyhaven asset'), and the destination ('into Blender'). It distinguishes from siblings like 'search_polyhaven_assets' (which finds assets) and 'import_generated_asset' (which imports from other 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. It doesn't mention prerequisites (e.g., needing to search for assets first), exclusions (e.g., not for Sketchfab assets), or comparisons to siblings like 'download_sketchfab_model' or 'import_generated_asset'.
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 that the tool 'downloads and imports' and returns a success/failure message, which implies mutation and output behavior. However, it lacks details on side effects (e.g., where the model is stored, if it overwrites existing files), error handling, or performance aspects like rate limits. For a mutation 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by parameter details and prerequisites. It uses three sentences efficiently, with no wasted words. A minor deduction because the prerequisites could be integrated more seamlessly, but overall it's concise and clear.
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 complexity (a mutation operation with one parameter), no annotations, and no output schema, the description is adequate but has gaps. It covers the purpose, parameter meaning, and basic prerequisites, but lacks details on behavioral traits, output format beyond success/failure, and integration with siblings. It meets minimum viability but isn't fully comprehensive.
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: 'uid: The unique identifier of the Sketchfab model.' Since schema description coverage is 0% (the schema only provides title and type), this compensates well by explaining what the UID represents. However, it doesn't specify format or examples (e.g., if it's a numeric ID or alphanumeric string), keeping it from a perfect score.
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: 'Download and import a Sketchfab model by its UID.' It specifies the verb ('download and import'), resource ('Sketchfab model'), and key identifier ('by its UID'). However, it doesn't explicitly differentiate from siblings like 'search_sketchfab_models' or 'import_generated_asset', 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 implies usage by mentioning prerequisites: 'The model must be downloadable and you must have proper access rights.' This provides some context about when the tool will work, but it doesn't explicitly state when to use this tool versus alternatives like 'search_sketchfab_models' or 'import_generated_asset', nor does it outline exclusions or specific scenarios.
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 the action and return outcome ('Return if the asset has been imported successfully'). It doesn't disclose behavioral traits like required permissions, error conditions, whether it modifies existing assets, or what '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 appropriately sized and front-loaded with the core purpose. The parameter explanations are necessary given low schema coverage, and the warning about mode-based selection is critical. Minor redundancy exists in repeating 'Hyper3D Rodin'.
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 3-parameter mutation tool with no annotations and no output schema, the description is moderately complete. It covers parameters and basic purpose but lacks details on behavioral aspects (e.g., side effects, error handling) and output specifics. Given the complexity, it should provide more context about what 'import' entails operationally.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates well by explaining all 3 parameters: 'name' as object name in scene, and 'task_uuid'/'request_id' tied to specific Hyper3D Rodin modes with clear usage rules. It adds crucial semantic context beyond the bare schema, though it doesn't specify parameter formats 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 ('Import') and resource ('asset generated by Hyper3D Rodin'), making the purpose 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('after the generation task is completed') and provides parameter-specific guidance for Hyper3D Rodin modes, but lacks explicit when-to-use vs. alternatives like other import/download tools. No prerequisites or exclusions are mentioned beyond the mode-based parameter selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the texture must be downloaded first, adding some context about prerequisites, but doesn't cover other critical aspects like whether this operation is reversible, what permissions are needed, how failures are handled beyond a generic message, or any rate limits. For a mutation 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with three sentences: a clear purpose statement, parameter details, and return information. It's front-loaded with the main action and avoids unnecessary fluff, though the parameter section could be slightly more integrated into the flow rather than listed separately.
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 complexity (a mutation operation with 2 parameters), no annotations, and no output schema, the description is moderately complete. It covers the purpose and parameters adequately but lacks details on behavioral traits like error handling, side effects, or output specifics beyond a generic success/failure message. For a tool in this context, it should provide more operational guidance to be fully 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 description explicitly lists and describes both parameters ('object_name' and 'texture_id'), adding meaning beyond the input schema which has 0% description coverage. It clarifies that 'texture_id' refers to a Polyhaven texture and must be downloaded first, providing essential context not present in the schema. With 2 parameters and low schema coverage, this compensates well, though it could detail format or constraints 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 action ('Apply') and target ('a previously downloaded Polyhaven texture to an object'), specifying both the resource (texture) and the object it affects. It distinguishes from siblings like 'download_polyhaven_asset' by focusing on application rather than acquisition, though it doesn't explicitly contrast with all texture-related tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by noting the texture 'must be downloaded first', which suggests a prerequisite but doesn't explicitly state when to use this tool versus alternatives like 'import_generated_asset' or other texture-handling tools. It provides some context but lacks clear when/when-not guidance or named alternatives.
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 model has built-in materials, normalized size (requiring potential rescaling), and returns a success/failure message. However, it lacks details on permissions, rate limits, processing time, or error conditions, which are important for a generative 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 well-structured with a purpose statement, behavioral details, and a parameters section. It's front-loaded and avoids redundancy, though the parameters section could be more integrated into the flow rather than listed separately.
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 generative tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is moderately complete. It covers purpose, key behaviors, and parameter meanings, but lacks output details (beyond success/failure), error handling, or integration context with Blender or sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains 'text_prompt' as a short English description and 'bbox_condition' as an optional list of 3 floats controlling length, width, height ratios. This adds meaningful semantics beyond the bare schema, though it could specify format details like float ranges or units.
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 generates a 3D asset using Hyper3D based on a text description and imports it into Blender. It specifies the resource (3D asset) and verb (generate and import), but doesn't explicitly differentiate from sibling 'generate_hyper3d_model_via_images' beyond the 'via_text' naming.
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 for generating 3D assets from text prompts, with optional bbox conditioning. However, it doesn't explicitly state when to use this vs. alternatives like 'generate_hyper3d_model_via_images' or other asset download tools, 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.
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 ('Capture') and return type ('Returns the screenshot as an Image'), but lacks details on permissions needed, potential side effects (e.g., if it pauses rendering), rate limits, error conditions, or what 'Image' entails (e.g., format, size constraints beyond max_size). This is inadequate for a tool with mutation-like behavior (capturing).
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, followed by a clear parameter section and return statement. Every sentence earns its place with no redundant or verbose language, 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 moderate complexity (capturing a screenshot with one parameter), no annotations, and no output schema, the description is minimally adequate. It covers the purpose, parameter semantics, and return type, but gaps remain in behavioral details (e.g., how the image is formatted, error handling) and usage guidelines, making it incomplete for confident agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaningful context for the single parameter 'max_size' by explaining it as 'Maximum size in pixels for the largest dimension' and providing the default value (800), which clarifies its role beyond the schema's basic type and title. However, it doesn't detail units or constraints (e.g., 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 all sibling tools which involve downloading assets, executing code, getting status/info, or setting textures. No tautology or vagueness is 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?
No guidance is provided on when to use this tool versus alternatives. While the purpose is clear, there is no mention of prerequisites (e.g., Blender must be running), exclusions, or comparisons to other screenshot or image-related tools, leaving usage context implicit at best.
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 key traits: it's a polling API, explains what constitutes completion ('Done' or 'COMPLETED'), failure conditions ('Failed' or other statuses), and progress states ('IN_PROGRESS'). It also clarifies that users should wait for final states before proceeding. This covers essential behavioral aspects without contradictions, though it lacks details on rate limits or authentication needs.
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 appropriately sized but could be more front-loaded. It starts with a clear purpose, but the detailed breakdown into two modes (MAIN_SITE and FAL_AI) is somewhat repetitive and could be streamlined. Every sentence adds value, but the structure feels slightly verbose, with redundant phrases like 'This is a polling API' repeated for each mode.
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 complexity (two operational modes, no annotations, no output schema), the description is largely complete. It explains the tool's purpose, usage, parameters, and expected behaviors (polling, status interpretation). However, it doesn't detail the return format (e.g., structure of the 'list of status' or specific failed states), which could be helpful since there's no output schema. This minor gap prevents a perfect score.
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 semantics of both 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 crucial meaning beyond the bare schema, ensuring users understand when and how to use each parameter effectively.
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 the Hyper3D Rodin generation task is completed.' It specifies the verb ('check') and resource ('Hyper3D Rodin generation task'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_hyper3d_status', which might serve a similar purpose, preventing 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 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: it's a polling API to check job status, and it advises to 'only proceed if the status are finally determined.' It also implicitly distinguishes usage between two modes (MAIN_SITE and FAL_AI) based on parameters. However, it doesn't explicitly mention when not to use it or name alternatives among siblings, such as 'get_hyper3d_status', which could be a related tool.
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. It discloses several behavioral traits: the generated asset has built-in materials, has normalized size requiring potential rescaling, returns success/failure messages, and has mode-dependent parameter requirements (MAIN_SITE vs FAL_AI). It doesn't cover permissions, rate limits, or error details, but provides substantial operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and well-structured: purpose statement first, then behavioral details, then parameter explanations. Every sentence adds value. Minor improvement could be front-loading the parameter exclusivity rule earlier, 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters with 0% schema coverage, no annotations, and no output schema, the description provides substantial context: purpose, behavioral traits, detailed parameter semantics, and return indication. It doesn't explain the import process details or Blender integration specifics, but covers the core functionality adequately for a complex generation tool.
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 that 'input_image_paths' requires absolute paths and is for MAIN_SITE mode, 'input_image_urls' is for FAL_AI mode, both must be lists even for single images, and only one should be used. It explains 'bbox_condition' as optional ratio control for [Length, Width, Height]. This adds crucial meaning beyond 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 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), resource (3D asset), technology (Hyper3D), input method (images), and destination (Blender). It distinguishes from sibling tools like 'generate_hyper3d_model_via_text' by specifying image-based generation.
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 distinguishes from 'generate_hyper3d_model_via_text' by specifying image input. However, it doesn't explicitly state when NOT to use it or mention all alternatives like 'download_polyhaven_asset' or 'download_sketchfab_model' for pre-existing assets.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![blender-mcp MCP server](https://glama.ai/mcp/servers/mikeysrecipes/blender-mcp/badges/card.svg)](https://glama.ai/mcp/servers/mikeysrecipes/blender-mcp)

Score Badge

Copy to your README.md:

[![blender-mcp MCP server](https://glama.ai/mcp/servers/mikeysrecipes/blender-mcp/badges/score.svg)](https://glama.ai/mcp/servers/mikeysrecipes/blender-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mikeysrecipes/blender-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server