Smart Prompts MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, such as check_github_status for GitHub connectivity, compose_prompts for combining prompts, and search_prompts for searching. However, get_prompt and search_prompts could be slightly confused since both retrieve prompts, but descriptions clarify that search_prompts is for discovery and get_prompt is for exact retrieval by name.
Naming Consistency4/5
Tool names follow a consistent verb_noun pattern with snake_case throughout, like check_github_status and search_prompts. The only minor deviation is prompts_help, which uses a noun_verb structure, but it still fits the overall naming style and is readable.
Tool Count5/5
With 7 tools, the count is well-scoped for a prompt management server. Each tool serves a clear role, from checking GitHub status to creating, searching, and managing prompts, without feeling bloated or insufficient for the domain.
Completeness4/5
The tool set covers core CRUD operations for prompts, including create_github_prompt, get_prompt, and search_prompts, with additional utilities like compose_prompts and list_prompt_categories. A minor gap is the lack of update or delete tools for prompts, but agents can work around this by creating new prompts as needed.
Average 4/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool 'Returns guidance on tool usage, examples, and best practices,' which gives some behavioral context about output content. However, it doesn't cover aspects like response format, potential errors, or performance characteristics (e.g., if it's a fast lookup or requires network 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 concise and front-loaded, consisting of two clear sentences that directly state the purpose and output. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 optional parameter, no output schema, no annotations), the description is moderately complete. It covers the core purpose and output type but lacks details on error handling, response structure, or integration with sibling tools. For a help tool, more context on when and how to use it would improve completeness.
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 input schema has 100% description coverage, with the 'topic' parameter well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what the schema provides (e.g., it doesn't elaborate on topic examples or constraints). With high schema coverage, the baseline score of 3 is appropriate.
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 help understanding how to use the Smart Prompts tools effectively.' It specifies the verb ('Get help') and resource ('Smart Prompts tools'), but doesn't explicitly differentiate from siblings like 'check_github_status' or 'compose_prompts', which are action tools rather than help tools. The purpose is clear but lacks sibling distinction.
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 ('how to use the Smart Prompts tools effectively') but doesn't provide explicit guidance on when to use this tool versus alternatives. For example, it doesn't specify if this should be used before attempting other tools or as a fallback for errors. The guidance is 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 provided, so the description carries the full burden. It mentions checking 'connection status and write access,' which implies it performs read operations to verify permissions, but it doesn't disclose behavioral traits like what happens on failure, whether it requires authentication, rate limits, or what the output looks like. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with zero waste. The first sentence states the purpose, and the second provides usage guidance, making it front-loaded and efficient. Every sentence earns its place by adding clear value.
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 somewhat complete but lacks depth. It explains what the tool does and when to use it, but without annotations or output schema, it should ideally disclose more about behavior (e.g., what 'write access' means, response format). It's adequate but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for 0 parameters is 4, as the description doesn't need to compensate for any missing schema information, and it appropriately avoids unnecessary parameter discussion.
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 GitHub connection status and write access.' It specifies both the verb ('Check') and the resources ('GitHub connection status and write access'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools, which are mostly prompt-related, so it doesn't reach a 5.
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: 'Use this to verify GitHub operations are available.' This gives a specific scenario (verifying availability before operations) and implies it's a prerequisite check. It doesn't mention when not to use it or name alternatives, so it's not a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool's function as listing categories with counts, which is straightforward, but doesn't mention potential behavioral traits like pagination, rate limits, authentication needs, or error handling. For a read-only listing tool with no annotations, this is adequate but lacks depth, justifying a score of 3.
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: it starts with an overview, states the purpose, and provides usage guidelines in two sentences. Every sentence adds value without redundancy, and it's front-loaded with key information. This earns a score of 5 for efficiency.
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 reasonably complete for its purpose. It explains what the tool does and when to use it. However, without annotations or an output schema, it could benefit from more details on behavioral aspects or return format, leaving some gaps. This results in a score of 3.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameters need documentation. The description doesn't add parameter information, which is fine in this case. Since there are no parameters, the baseline is 4, as the description doesn't need to compensate for any gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List all available prompt categories with prompt counts.' It specifies the verb ('list') and resource ('prompt categories'), and includes the additional detail of 'prompt counts.' However, it doesn't explicitly differentiate this from sibling tools like 'search_prompts' or 'get_prompt,' which prevents a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: 'Use this to explore the library structure and see what categories exist before searching or creating prompts. Great for discovering new areas.' This gives guidance on when to use the tool (for exploration and discovery) and implies alternatives (searching or creating prompts). However, it doesn't explicitly state when not to use it or name specific sibling alternatives, so it falls short of a 5.
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 of behavioral disclosure. It mentions saving 'directly to the GitHub repository' and the duplication-avoidance workflow, which adds useful context beyond basic creation. However, it doesn't cover potential side effects (e.g., GitHub API rate limits, authentication requirements, or error handling), leaving gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and follows with a clear workflow guideline. Both sentences earn their place by providing essential information. The emojis are slightly decorative but don't significantly impact clarity. It could be slightly more concise by integrating the emojis more seamlessly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (10 parameters, mutation operation) and lack of annotations/output schema, the description does well by specifying the GitHub integration and workflow prerequisites. However, it doesn't fully address behavioral aspects like error conditions or response format, leaving some gaps for a creation 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?
The schema description coverage is 100%, so the schema already documents all 10 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema. This meets the baseline of 3 when the schema does the heavy lifting, but doesn't provide additional semantic context.
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 ('Create New Prompt') and resource ('save it directly to the GitHub repository'), distinguishing it from sibling tools like 'search_prompts' or 'get_prompt' which retrieve rather than create. The emojis add emphasis but don't obscure the core purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives: 'Always use search_prompts first to check if a similar prompt already exists. Only create new prompts when needed to avoid duplicates.' This clearly directs the agent to a specific workflow and names the alternative tool.
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 of behavioral disclosure. It states this is a search operation that returns metadata, implying it's read-only and non-destructive, which is adequate. However, it lacks details on pagination, rate limits, error handling, or response format, leaving gaps for an AI agent to infer 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 front-loaded with key information ('ALWAYS START HERE') and uses only three sentences, each earning its place by explaining purpose, usage guidelines, and benefits. There is no wasted text, and the structure flows logically from action to context to rationale.
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 moderate complexity (search with three optional parameters), no annotations, and no output schema, the description is mostly complete. It covers purpose and usage well but lacks details on behavioral aspects like response structure or limitations. It compensates somewhat with strong guidance, but could be more comprehensive for full agent 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 description coverage is 100%, so the schema already documents all three parameters (category, query, tags) with examples and constraints. The description adds no additional parameter semantics beyond implying these are search filters, which the schema covers. 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.
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 with specific verbs ('search for prompts') and resources ('by keyword, category, or tags'), distinguishing it from siblings like get_prompt (retrieves specific prompts) and create_github_prompt (creates new prompts). It explicitly mentions returning 'matching prompts with their metadata,' providing a complete picture of the operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'ALWAYS START HERE' and 'recommended first step before using get_prompt or creating new prompts.' It also explains why: 'Helps avoid duplicates and find exactly what you need,' effectively positioning it as a discovery tool versus retrieval or creation 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?
No annotations are provided, so the description carries the full burden. It describes the tool's behavior (combining prompts in order with a separator) and hints at workflow integration, but lacks details on error handling, output format, or limitations (e.g., maximum number of prompts). It doesn't contradict annotations, but could be more comprehensive for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by usage guidelines. It uses two sentences efficiently, though the emojis and formatting (e.g., '📋 WORKFLOW:') add minor clutter. Overall, it's concise and well-structured for quick comprehension.
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 moderate complexity (combining prompts), no annotations, and no output schema, the description does a good job covering purpose and usage. However, it lacks details on the output (e.g., what the combined prompt looks like) and potential constraints, leaving some gaps for an agent to infer behavior.
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 100%, so the schema already documents both parameters thoroughly. The description adds context by mentioning 'prompt names' and referencing search_prompts, but doesn't provide additional semantic details beyond what's in the schema. Since parameters are well-covered, a baseline of 3 is appropriate, but the workflow reference slightly enhances understanding.
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 ('Combine multiple existing prompts into a single prompt') and resource ('prompts'), distinguishing it from siblings like search_prompts (which finds prompts) or get_prompt (which retrieves a single prompt). The emoji and title-like phrasing reinforce the purpose without being tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use this tool ('Perfect for creating complex multi-step workflows') and provides a clear workflow instruction: 'Use search_prompts to find prompt names first, then compose them.' This directly addresses when to use it versus alternatives like search_prompts, offering practical 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return value ('complete prompt content with metadata and template variables') and includes a caution note ('⚠️ IMPORTANT') about the exact name requirement, adding useful context beyond basic functionality. However, it doesn't mention potential errors or rate limits.
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 important usage guidance and return details. Every sentence adds value with no wasted words, and the emoji and formatting enhance readability without sacrificing clarity.
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 retrieval tool with one well-documented parameter and no output schema, the description is largely complete. It explains the purpose, usage workflow, and return content. The main gap is the lack of error handling or edge case information, but overall it provides sufficient context 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?
Schema description coverage is 100%, so the schema already documents the single parameter thoroughly. The description reinforces the 'exact name' requirement and references 'search_prompts results,' adding some context but not significant new semantic information 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 verb ('Retrieve') and resource ('a specific prompt by its exact name'), making the purpose specific and actionable. It distinguishes from sibling tools like 'search_prompts' by focusing on retrieval of a single prompt rather than searching or listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage guidance: 'Use search_prompts first to find the correct prompt name, then use this tool.' This specifies a clear workflow and alternative tool, helping the agent understand when and how to use this tool effectively.
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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