promptpilot-mcp-server

Server Quality Checklist

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Latest release: v1.2.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: build_prompt constructs prompts, check_balance checks API credits, generate_* tools create different media types, and list_* tools provide metadata. The separation between generate_image, generate_batch, generate_video, and generate_audio is unambiguous, and list_models vs list_styles serve different informational needs.
Naming Consistency5/5
All tools follow a consistent verb_noun naming pattern with snake_case throughout. The verbs are descriptive and appropriate: build_, check_, generate_, and list_ are used consistently across related tools, making the set predictable and easy to understand at a glance.
Tool Count5/5
With 8 tools, this server is well-scoped for its purpose of interacting with the Pollinations API for media generation and prompt building. Each tool earns its place by covering distinct aspects: prompt construction, balance checking, media generation (with batch optimization), and model/style listing, without being overwhelming or insufficient.
Completeness4/5
The tool set covers core workflows comprehensively: prompt building, credit management, media generation (image, video, audio, batch), and resource listing. A minor gap is the lack of tools for managing generated content (e.g., delete or retrieve metadata), but agents can work around this using the provided URLs and API balance checks.
Average 3.2/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed 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.
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```
{
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  "maintainers": [
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  ]
}
```
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool returns a URL, which is useful, but lacks critical details such as rate limits, authentication requirements, cost implications, or error handling. For a generative tool with external API calls, 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 extremely concise with two sentences that directly state the tool's function and output. Every word earns its place, and it's front-loaded with the core purpose, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an audio generation tool with no annotations and no output schema, the description is incomplete. It lacks details on output format (e.g., audio file type, duration), error cases, or integration context, which are essential for effective tool use by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema, such as explaining prompt formatting or model selection criteria. This meets the baseline for high schema coverage but doesn't enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Generate audio'), the resource ('speech or music'), and the method ('using Pollinations API'). It distinguishes from siblings like generate_image and generate_video by specifying audio generation. However, it doesn't explicitly differentiate from generate_batch which might also handle audio, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like generate_batch or other audio-related tools. It mentions the API but doesn't specify use cases, prerequisites, or exclusions, leaving the agent with minimal contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the API (Pollinations) and return type (URL), but fails to disclose critical traits like rate limits, authentication needs, cost implications, error handling, or whether the operation is idempotent. This leaves significant gaps for a tool that likely involves external API calls.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—two sentences that efficiently state the tool's purpose and output. It is front-loaded with the core functionality and wastes no words, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an image generation tool with external API integration, no annotations, and no output schema, the description is insufficient. It omits details about behavioral traits (e.g., rate limits, costs), usage context compared to siblings, and output specifics beyond a URL, leaving the agent under-informed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter-specific information beyond what the input schema provides, which has 100% coverage with detailed descriptions for all 6 parameters. Since the schema fully documents parameters like prompt, model, and dimensions, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 ('Generate an image') and resource ('using Pollinations API'), and specifies the return value ('Returns a URL to the generated image'). It distinguishes from siblings like generate_audio and generate_video by focusing on image generation, though it doesn't explicitly contrast with generate_batch or other image-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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like generate_batch for multiple images or list_models for model selection. It lacks context about prerequisites, such as needing a prompt, or when not to use it, such as for audio or video generation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool generates a video and returns a URL, but lacks details on permissions, rate limits, costs, error handling, or processing time. This is inadequate for a tool that likely involves external API calls and resource consumption.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences that directly state the tool's function and output. It is front-loaded and wastes no words, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of video generation, no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like API dependencies, potential failures, or output details beyond a URL. For a tool with 5 parameters and external integration, more context is needed to guide effective use.
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 100%, so the schema fully documents all parameters. The description adds no additional meaning beyond what's in the schema, such as examples or constraints. Baseline 3 is appropriate as the schema handles parameter documentation, but the description doesn't enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate a video using Pollinations API' specifies the verb (generate) and resource (video), and 'Returns a URL to the generated video' indicates the output. However, it doesn't explicitly differentiate from sibling tools like generate_image or generate_audio beyond the resource type, missing specific distinctions about when to choose video over other generation tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage 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. The description mentions the Pollinations API but doesn't specify contexts, prerequisites, or exclusions. Sibling tools like generate_image or generate_audio are not referenced, leaving the agent without direction on 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 builds a prompt but doesn't explain what 'optimized' means, whether it's a read-only or mutation operation, if there are rate limits, authentication needs, or what the output format looks like. This leaves significant gaps for an AI agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly lists the input components, making it easy to parse and understand quickly. Every part of the sentence serves a clear informational purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (7 parameters, 1 required) and lack of annotations or output schema, the description is adequate but incomplete. It covers what the tool does but misses behavioral context, output details, and usage guidelines. Without an output schema, the agent doesn't know what the built prompt looks like, which is a notable gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with clear descriptions and enums for most parameters. The description adds minimal value beyond the schema by listing the parameter categories (subject, style, lighting, camera, mood, color, quality options), but doesn't provide additional syntax, format details, or usage examples. This meets the baseline for high schema coverage.
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: 'Build an optimized prompt from subject + style/lighting/camera/mood/color/quality options'. It specifies the verb ('Build'), resource ('optimized prompt'), and input components. However, it doesn't explicitly differentiate from sibling tools like 'generate_image' or 'list_styles', which might be related but serve different functions.
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, when not to use it, or how it relates to sibling tools such as 'generate_image' (which might use the built prompt) or 'list_styles'. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists models with type and pricing, implying a read-only operation, but doesn't clarify aspects like whether it requires authentication, has rate limits, returns paginated results, or includes metadata beyond type and pricing. 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 a single, well-structured sentence that directly conveys the tool's purpose without any wasted words. It is front-loaded with the core action ('List all available models') and includes essential details ('for image, video, and audio generation with their type and pricing'), making it highly efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks behavioral details like authentication needs or output format. Without annotations or an output schema, the description should ideally cover more about the return values and operational context, but it meets the basic requirement for a simple 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 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 avoids discussing inputs. A baseline of 4 is applied for zero-parameter tools, as it efficiently handles the lack of inputs without unnecessary detail.
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: 'List all available models for image, video, and audio generation with their type and pricing.' It specifies the verb ('List'), resource ('available models'), and scope ('image, video, and audio generation'). However, it doesn't explicitly differentiate from sibling tools like 'list_styles' or 'build_prompt,' which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing to check models before generating content, or compare it to siblings like 'list_styles' for style options. Without any usage context, the agent must infer when this tool is appropriate.
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 describes a read-only listing operation, which is clear, but lacks details on behavioral traits such as rate limits, authentication needs, response format, or any potential side effects. This leaves significant gaps for an agent to understand how to interact with it effectively.
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 front-loads the key action and resource without any wasted words. It directly conveys the tool's purpose in a structured manner, 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 complexity is low (0 parameters, no output schema), the description is adequate as a basic listing tool. However, without annotations or an output schema, it lacks completeness in detailing behavioral aspects like response format or usage constraints. It meets the minimum viable standard but has clear gaps in providing full context for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the 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. A baseline score of 4 is given since no parameters exist, and the description doesn't introduce confusion or 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 with a specific verb ('List') and resource ('all available styles, lighting, camera angles, moods, colors, and quality tags'), making it easy to understand what it does. However, it doesn't explicitly distinguish this tool from its sibling 'list_models', which might also list available resources, so it misses full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'for prompt building', which implies a context, but doesn't specify when to choose this over sibling tools like 'build_prompt' or 'list_models', nor does it outline any prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions efficiency but lacks critical details: it doesn't specify authentication needs, rate limits, cost implications, error handling, or what the output looks like (e.g., format, structure). For a batch generation tool with no annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with zero waste: the first states the purpose and constraint, the second provides usage guidance. It's appropriately sized and front-loaded, making every sentence earn its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a batch image generation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., authentication, rate limits), output format, and error handling. While the schema covers parameters well, the overall context for safe and effective use is insufficient.
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 100%, so the schema already documents the single parameter 'prompts' and its nested properties. The description adds minimal value beyond the schema by implying batch processing and efficiency, but doesn't provide additional syntax, format details, or constraints beyond what's in the schema. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate multiple images in one call (up to 10).' It specifies the verb ('generate'), resource ('images'), and scope ('multiple...up to 10'). However, it doesn't explicitly differentiate from its sibling 'generate_image' beyond mentioning efficiency, which is good but not a full distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: 'More efficient than calling generate_image repeatedly.' This implies an alternative (generate_image) and suggests usage for batch processing. However, it doesn't explicitly state when NOT to use it or other alternatives, which keeps it from a perfect score.
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 the tool's purpose (checking balance) and output type (remaining credits), but lacks details on behavioral traits like rate limits, authentication needs, error conditions, or whether it's read-only (implied but not stated). It adds basic context but misses operational specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Check your Pollinations API balance') and adds clarifying detail ('pollen', 'remaining credits for paid models'). Every word earns its place with zero waste, making it easy to parse quickly.
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 (0 parameters, no annotations, no output schema), the description is reasonably complete. It explains what the tool does and what information it provides. However, without an output schema, it could benefit from more detail on the return format (e.g., numeric value, JSON structure), slightly limiting completeness for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description adds no parameter information, which is appropriate here. Baseline is 4 for zero parameters, as no additional semantics are needed beyond what the schema provides.
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 ('Check'), resource ('Pollinations API balance'), and what it shows ('remaining credits for paid models'). It distinguishes itself from siblings like list_models or generate_image by focusing on account credits rather than content generation or listing resources.
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 ('paid models') but doesn't explicitly state when to use this tool versus alternatives. It doesn't provide guidance on prerequisites, frequency, or comparisons with other tools, leaving usage decisions to inference rather than explicit direction.
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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