community-prompt-voting-mcp-server

by AiAgentKarl

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Tools like upvote_prompt and downvote_prompt are clearly distinct. However, get_category_rankings, get_trending_prompts, and get_voting_info all provide listings of prompts with different sorting, which could cause some confusion, but descriptions clearly differentiate their purposes.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern in snake_case, with clear verbs (downvote, upvote, get, search, submit) and nouns (prompt, rankings, prompts, voting_info).
Tool Count5/5
Seven tools is appropriate for a community prompt voting system, covering submission, voting, and various listing views without being excessive.
Completeness3/5
The set covers submission and voting but lacks tools for updating or deleting prompts, and there is no tool to retrieve a single prompt by ID, which are basic operations for a prompt library. This leaves gaps in typical CRUD coverage.
Average 3.5/5 across 7 of 7 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.
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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 full responsibility for behavioral disclosure. It explains sorting and tag format but omits important traits such as authentication needs, rate limits, pagination behavior, or what happens with empty queries. 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 very concise at two sentences, front-loading the main purpose. While it is efficient, it slightly underspecifies details, but overall it earns its space with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 optional parameters) and the presence of an output schema, the description still lacks crucial context like default behavior when query is empty, result ordering details, or error handling. It feels incomplete for an agent to use effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning. It explains sort_by values and tags as comma-separated, but does not clarify query, category, or limit parameters beyond their defaults. This partial coverage results in a score of 3, as it adds some value but is incomplete.
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 prompts by keyword, category, or tags, which is a specific verb and resource. However, it does not differentiate from siblings like get_trending_prompts or get_category_rankings, which could cause confusion about when to use this tool.
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 gives some guidance on sort_by options and tag format, implying when to use certain parameters. However, it lacks explicit instructions on when to use this tool versus siblings or when not to use it, leaving usage context implied rather than explicit.
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 present, so the description bears full responsibility for behavioral disclosure. It accurately indicates a read-only operation but omits any information about permissions, rate limits, data freshness, or side effects. For a simple retrieval tool, this is minimal but acceptable.
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 short sentences, front-loaded with the main purpose. Every word is informative, 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?
Given the tool has no parameters and an output schema exists, the description provides sufficient high-level information. It could be enhanced by explicitly noting its relationship to similar sibling tools, but the current version is adequate for a simple retrieval operation.
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 coverage is trivially 100%. The description adds value by detailing the types of statistics returned (overall, top prompts, category distribution), which is not evident from the schema alone.
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 it retrieves community voting system statistics, listing specific outputs: overall stats, top prompts, and category distribution. It is not a tautology and distinguishes itself from action tools like upvote/downvote, but does not explicitly differentiate from similar retrieval tools like get_category_rankings or get_trending_prompts.
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 its siblings. An explicit note about its role as a general statistics overview compared to more specific retrieval tools would improve usability.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states the action and input format, but fails to disclose potential effects, authorization needs, or constraints like duplicate detection.
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?
Three succinct sentences front-load the purpose and provide necessary details on categories and tags, with no wasted words.
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?
Despite having an output schema, the description lacks crucial parameter semantics for title and prompt_text, and gives no behavioral context for a submission action. Incomplete for a 5-parameter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description explains the 'category' and 'tags' parameters with examples, but omits 'title', 'prompt_text', and 'author', leaving required fields undefined.
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 ('Einen neuen Prompt einreichen') and the resource ('Community-Bibliothek'). It distinguishes from sibling tools like downvote_prompt or search_prompts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when adding a new prompt but provides no explicit when-not or alternative guidance. Categories and tags are mentioned, but no context on when to use this versus other tools.
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 provided, and the description fails to disclose behavioral traits like authentication needs, rate limits, or output structure beyond 'top 10 prompts'. The read-only nature is implied but not confirmed.
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?
Two concise sentences with front-loaded purpose. Every sentence adds value; no wasted words.
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 that an output schema exists, the description does not need to detail return values. However, the description is minimal and omits error conditions or constraints. It is adequate for a simple tool but could be more 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?
With 0% schema description coverage, the description effectively explains the category parameter's effect: without it, an overview of all categories; with it, top 10 prompts of that category. This adds value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves rankings by category, distinguishing between with and without category. The verb 'abrufen' is specific, but it does not differentiate from sibling tools like get_trending_prompts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides conditional usage (with/without category) but lacks guidance on when not to use this tool or explicit alternatives such as get_trending_prompts for trending data.
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, and the description only mentions the action (downvote or withdraw) without disclosing behavioral traits such as required authentication, reversibility, or rate limits. The description fails to add sufficient behavioral context beyond the basic operation.
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 with two sentences, front-loading the main verb and purpose. No unnecessary words or redundant 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 covers the basic action and mentions the unique voter_id, but lacks details on how withdrawal works, idempotency, or any prerequisites. Given the existence of an output schema and sibling tools, more context would be beneficial for a deeper understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no parameter descriptions (0% coverage). The description adds value by explaining that 'voter_id' should be a unique identifier, but does not clarify 'prompt_id' or the withdrawal behavior. Thus it partially compensates for the lack of schema 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 explicitly states the tool's purpose: to downvote a prompt or withdraw a downvote ('niedrigbewerten' and 'Downvote geben oder zurueckziehen'). It clearly distinguishes itself from the sibling tool 'upvote_prompt' by being its opposite.
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 like 'upvote_prompt'. Usage is only implied by the tool name and the verb 'downvote'. No context about prerequisites or conditions is given.
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?
Given no annotations, the description adds important behavioral context: trend score combines votes with recency and favors newer prompts. This explains the algorithm behind trending, though it does not cover auth needs, rate limits, or potential side effects.
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?
Two sentences, front-loaded with the main purpose, no redundant words. Every sentence contributes meaningful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema and two simple parameters with defaults, the description is adequate but could be improved by mentioning parameter usage (e.g., how limit works) or clarifying that the output schema provides prompt details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention the two parameters (limit, time_window_days) at all. It fails to add any meaning beyond the bare schema, leaving the agent without guidance on how to use these parameters.
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 retrieves trending prompts of the community, specifying the resource (trending prompts) and the verb (abrufen/retrieve). It also explains how trending is determined (votes + recency), differentiating it from sibling tools like search_prompts or get_category_rankings.
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 getting currently trending prompts based on a trend score, but does not explicitly specify when to use this tool versus alternatives like search_prompts or get_category_rankings. No exclusions or prerequisites are provided.
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?
No annotations provided, so description carries full burden. It discloses toggle behavior (give or revoke) and the need for a unique voter_id to prevent multiple votes. This is good behavioral context beyond the schema.
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?
Two sentences, front-loaded with action, no wasted words. Efficiently communicates the core functionality.
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 toggle tool with an output schema, the description covers the action and key constraint (voter_id uniqueness). It doesn't mention the default voter_id or idempotency, but overall complete enough.
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 coverage is 0%, so description must compensate. It explains voter_id's purpose (unique identifier to prevent multiple votes), but does not describe prompt_id or the default value of voter_id. Adds value for one of two parameters.
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: upvoting a prompt with the ability to revoke (toggle). It specifies the resource (prompt) and the verb (upvote), distinguishing it from sibling downvote_prompt.
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 or when not. It implies use for upvoting, but doesn't contrast with downvote_prompt. Mentions voter_id uniqueness to prevent multiple voting, which is useful but not usage guidance.
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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