pruna-mcp-server

Server Quality Checklist

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Latest release: v0.2.0
Disambiguation5/5
Each tool has a clearly distinct purpose: image generation, image editing, video generation, video transformation, try-on, upscaling, model listing, and file upload. No two tools overlap in function.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., generate_image, edit_image, list_models) using snake_case, making them predictable and easy to understand.
Tool Count5/5
With 8 tools, the server is well-scoped for its purpose—covering core media generation and editing tasks without being overly sparse or cluttered.
Completeness4/5
The set covers major operations: generation, editing, transformation, upscaling, try-on, model listing, and file upload. A minor gap is the lack of a text-based video editing tool analogous to edit_image, but overall it's quite complete.
Average 4.1/5 across 8 of 8 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate non-read-only, open-world, non-idempotent, non-destructive behavior, but the description adds no behavioral context beyond listing parameters. There is no mention of costs, generation time, or error conditions.
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 with the purpose, followed by a clear parameter list. However, it could be better structured with explicit defaults or grouped 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?
The description lacks mention of prerequisites (e.g., uploading image/audio files) and does not clarify behavior with multiple inputs. Since output schema exists, return values are not expected, but the tool's complexity warrants more 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 adds meaning by listing parameters and brief explanations (e.g., 'prompt: Text prompt for video generation'), but it does not provide constraints or allowed values for parameters like model or resolution.
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 'Generate a video from text, image, or audio using Pruna AI,' specifying the verb and resource, and implicitly distinguishes from sibling tools like generate_image and transform_video.
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 explicit guidance on when to use this tool vs alternatives such as generate_image or transform_video. The description lists inputs but does not provide decision criteria or mention prerequisites like file uploads.
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?
Annotations already provide readOnlyHint=false (mutation), destructiveHint=false, and idempotentHint=true. The description adds that it upsamples and enhances details/realism but does not reveal additional behavioral traits like file size limits or processing time. It meets the baseline but adds minimal value beyond annotations.
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?
Extremely concise: one-line purpose followed by a clear bullet list of parameters. Every sentence is necessary and front-loaded, with no redundancy.
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?
The description covers all input parameters but omits details about the output (e.g., returned image URL). Given the tool's moderate complexity and lack of output schema, describing the return value would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully explains each parameter: image source, target megapixels with range, output format options, and boolean enhancements. This adds crucial meaning beyond the schema's type/defaults, enabling correct usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action: 'Upscale an image using Pruna AI.' This directly conveys the purpose, distinguishing it from sibling tools like edit_image and generate_image.
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 explicit guidance on when to use this tool versus alternatives. It does not mention any prerequisites or situations where the tool is inappropriate, leaving the agent to infer usage from the parameter descriptions.
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?
Annotations already declare readOnlyHint=false, destructiveHint=false, so the description's addition of constraints (e.g., dimension multiples) and seed for reproducibility adds some value. However, it does not disclose other behavioral traits like rate limits, authentication, or what happens on failure.
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, using a docstring format with a clear one-line summary followed by an organized Args list. Every sentence adds value—no redundancy or irrelevant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains input parameters well but does not describe the output format (e.g., image URL, base64). Given no output schema, this omission could leave an agent uncertain about what is returned. Additionally, no usage context or error handling is mentioned.
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 explains all six parameters. It defines defaults (model: p-image, aspect_ratio: 16:9), lists allowed aspect ratios, and specifies valid ranges and conditions for width/height. This provides essential meaning beyond the schema's type and title.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Generate an image from a text prompt' and specifies the tool (Pruna AI). The verb and resource are specific, and it naturally distinguishes from sibling tools like 'edit_image' and 'generate_video'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit guidance on when to use this tool versus alternatives (e.g., edit_image for modifications). It is implied that this is for generating new images from text, but no when-not-to-use or exclusion criteria are given.
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?
Annotations already indicate the tool is modifiable (readOnlyHint=false) and not destructive. The description adds that it uses 'Pruna AI' (external dependency) but does not elaborate on side effects, rate limits, or required permissions. Some value added beyond annotations.
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, starting with a clear purpose sentence followed by a parameter list. Every sentence adds value, and the structure is easy to scan. No redundant or unnecessary 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?
The description covers the core functionality and parameters but omits details like valid model options, aspect ratio formats, error handling, output format, and prerequisites. Given the complexity and lack of output schema, more completeness would help an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With schema description coverage at 0%, the description lists each parameter with a brief explanation (e.g., 'prompt: Edit instruction describing the desired changes'). This adds meaning beyond the schema's bare titles, though some params like aspect_ratio receive minimal clarification.
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 edits one or more images using text instructions, specifying the verb (edit) and resource (images) distinctly. This differentiates it from sibling tools like generate_image or generate_video, which create new content.
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 implies use when an existing image needs modification via a text prompt, and provides parameter docs like prompt and images. However, it does not explicitly state when not to use it or mention alternative tools for similar tasks.
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?
Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds that the tool returns 'capabilities and pricing' and supports filtering. It does not describe pagination or rate limits, but for a simple list operation this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with a clear main sentence and a docstring-style args section. It could be slightly more structured, but it efficiently conveys the purpose and filter option.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter, output schema exists), the description covers its purpose and filter. It does not mention pagination or output structure, but the output schema likely handles that. It is complete enough for a listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning to the 'category' parameter by listing the allowed values: image, editing, try-on, upscale, video, video-edit. The input schema only specifies type string/null with no enums, so the description compensates for low schema coverage (0%).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'list' and resource 'Pruna AI models', and specifies it returns 'capabilities and pricing'. It distinguishes from sibling tools that perform actions (edit, generate, etc.).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives. The description only mentions filtering by category, but does not discuss when listing is appropriate or when to prefer other tools. Sibling tools are all action-oriented, so the distinction is implicit.
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?
Annotations provide minimal info (readOnlyHint=false, destructiveHint=false), so the description carries the burden. It reveals that motion, timing, etc. are preserved and that turbo mode offers faster generation at lower quality. However, it lacks disclosure of limitations (e.g., maximum video length, supported input formats) or side effects beyond the stated transformations.
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-organized with a lead sentence, model breakdown, and a bullet list of parameters. It is slightly verbose but every sentence adds value. Could be condensed slightly, but overall efficient.
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 10 parameters, no nested objects, and an existing output schema, the description covers all parameters and model usage. It lacks mention of prerequisites (e.g., pre-uploaded files), potential error conditions, or integration with sibling tools like upload_file. Nearly complete, with minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, leaving all parameter meaning to the description. The description provides thorough inline explanations for all 10 parameters, including details on reference count constraints per model and optional instruction prompt. This fully compensates for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies it is a video-to-video transform using reference images. It distinguishes two models (p-video-animate and p-video-replace) with different use cases, which differentiates it from sibling tools like generate_video or edit_image.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use each model based on number of references and what is preserved (motion, timing, etc.). However, it does not explicitly state when not to use this tool or mention alternatives for other scenarios, which would strengthen guidance.
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?
Annotations provide no behavioral hints (readOnlyHint=false, destructiveHint=false), so the description carries full burden. It discloses recommendations (up to 6 garments, turbo not for >4), experimental flags (prompt, reference_pose), and defaults, but omits details on failure modes, rate limits, or exact output 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 brief and well-structured: a one-sentence summary followed by a bullet-like list of parameters. Every sentence adds value, with no redundancy or fluff.
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?
Despite 10 parameters and no output schema, the description explains each parameter well and offers usage tips. However, it lacks an explicit description of what the tool returns (e.g., an image URL) and does not specify input format requirements (e.g., valid file types).
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 fully explain parameters. It does so with clear explanations for all 10 parameters, including defaults, recommendations, and experimental notes—adding significant meaning beyond raw schema property names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it virtually fits garments onto a person's photo using Pruna AI. The verb 'fit' and resource 'garments onto person's photo' are specific, and the tool is distinct from siblings like edit_image or generate_image due to its focused try-on 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 implicitly defines usage for virtual try-on but does not explicitly state when to use this tool over alternatives or when not to use it. Sibling tools like edit_image or generate_image are not mentioned, so an agent lacks guidance on trade-offs.
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?
The description adds a key behavioral constraint (max 20MB) beyond what annotations provide. It aligns with annotations (idempotentHint, non-destructive). However, it could disclose whether duplicate file paths overwrite or create new versions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise: two sentences plus an args section. It is front-loaded with the primary purpose and the args section is clear and efficient. No extraneous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with an output schema, the description covers the essential behavior. It could be improved by indicating what the tool returns (e.g., file ID) or prerequisites (file existence), but it is largely adequate.
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 the 20MB size limit and clarifies that file_path is a local path. Since the schema has 0% description coverage, this is a valuable addition. It could specify allowed file types or formats.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (upload), the target (Pruna AI), and the context (editing/video workflows). It effectively distinguishes from sibling tools that perform other operations like editing or generating.
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 context on when to use this tool (for editing/video workflows) but does not explicitly mention when not to use it or suggest alternatives. The sibling tools list implies related use cases.
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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