SkillHub MCP

by artuntan

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Server Quality Checklist

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Latest release: v0.6.0
Disambiguation5/5
Each tool has a clear, distinct purpose: analyze_stack for stack analysis, recommend for general recommendations, search for database queries, get_resource for details, and get_setup_guide for installation instructions. There is no overlap, and agents can easily distinguish them.
Naming Consistency4/5
Most tools follow a verb_noun pattern (analyze_stack, get_resource, get_setup_guide), but recommend and search are single verbs. This is a minor inconsistency but still predictable and readable.
Tool Count5/5
With 5 tools, the set is well-scoped for a resource discovery and recommendation server. Each tool serves a necessary function without redundancy or bloat.
Completeness4/5
The tools cover the core workflow: stack analysis, general recommendation, search, resource details, and setup guides. Minor gaps like user feedback or comparison features exist but do not hinder the primary purpose.
Average 3.9/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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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  ]
}
```
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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?
No annotations are provided, so the description must cover behavioral traits. It does not mention read-only nature, rate limits, or behavior on empty results. The focus is on purpose, not behavior, leaving significant gaps for agent decision-making.
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 that are front-loaded with the action and scope. No fluff or redundancy.
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?
No output schema exists, yet the description does not mention return format, pagination, or error handling. It also contains an inaccuracy about tags. For a 4-parameter search tool with no annotations, this is insufficient.
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 coverage is 100%, so baseline is 3. However, the description claims filtering by 'tags' which is not a parameter in the schema, causing potential confusion. It adds little beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search' and the resource 'SkillHub database (20,000+ AI resources)' with specific filtering dimensions (text query, type, ecosystem, or tags). It also explains the usage scenarios (lookups, comparisons, browsing), distinguishing it from sibling tools like get_resource or recommend.
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 gives clear context for when to use the tool ('targeted lookups', 'compares options', 'browse resources in a specific category'). However, it does not explicitly mention when not to use it or provide alternatives, but the guidance is sufficient.
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 must disclose behavioral traits. It says 'Get full details' but does not explain what 'full details' includes, whether it's read-only, error handling for invalid identifiers, or any side effects. For a simple lookup, it's adequate but not thorough.
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: first states what the tool does, second states when to use it. No wasted words; front-loaded with essential information. Excellent conciseness.
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 low complexity (1 parameter, no output schema, no annotations), the description is mostly complete. It covers purpose and usage context. However, it omits any detail about return format or constraints, which prevents a perfect score.
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 covers the single parameter 'identifier' with a description ('Resource ID or exact title to look up') at 100% coverage. The description adds no new semantic value beyond restating the parameter's role, so baseline 3 applies.
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?
Description clearly states 'Get full details about a specific AI resource' with a specific verb and resource. It indicates lookup by ID or exact name. While it doesn't explicitly differentiate from siblings like search or recommend, the purpose is clearly about retrieving details of a known resource.
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 explicitly states 'Use this when the user wants more information about a previously recommended resource,' providing clear context. It could be improved by mentioning when not to use (e.g., if the resource is already fully known or for other operations), but the guidance is direct and helpful.
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 discloses that the tool recommends specific categories (AI tools, skills, MCP servers, rules), which is adequate. However, it does not mention any behavioral traits like rate limits, authentication, or side effects, leaving gaps 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 two sentences, front-loading the action and then providing usage context. Every sentence adds value with zero 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?
Given no output schema and simple parameters, the description covers the main purpose and usage. It does not detail the return format, but for a recommendation tool, the context is reasonably complete. A slightly higher score would require output structure hints.
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 100%, so the baseline is 3. The description does not add significant meaning beyond the schema; it mentions the categories of recommendations which align with the 'focus' parameter enum but does not explain parameters in more detail.
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 analyzes a technology stack and recommends complementary AI tools, skills, MCP servers, and rules. This distinguishes it from siblings like 'recommend' which may be broader, and 'search' which is for searching resources.
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 explicitly says 'Use this when the user describes their project, tech stack, or development environment and could benefit from AI-powered tools.' This provides clear context but does not specify when not to use or mention alternatives, so it misses the full guidance for 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool as returning instructions ('get'), implying a safe read operation, but does not explicitly state idempotency, side effects, or auth requirements. It lacks detail on whether the tool alone provides complete setup guidance.
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 and zero wasted words. The first sentence fronts the core purpose, and the second provides immediate usage context. Highly 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?
For a simple tool with one parameter and no output schema, the description adequately covers purpose and usage. It could optionally describe the return format, but this is not critical given the tool's simplicity and lack of output schema.
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 one parameter 'identifier' with a clear description. The tool description adds usage context but no additional semantic meaning beyond what the schema already provides. With 100% schema coverage, a score of 3 is appropriate as per guidelines.
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 begins with a specific verb-resource pair: 'Get installation and setup instructions for a specific AI resource from SkillHub.' It clearly distinguishes from sibling tools like 'get_resource' (which likely returns the resource itself) and 'recommend' (for recommendations), as it provides installation context.
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 explicitly states when to use this tool: 'Use this after recommending a resource to help the user actually install and configure it.' This gives clear contextual guidance, although it does not mention specific situations where the tool should not be used.
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 mentions that results are ranked with relevance scores and install guidance, but does not disclose any potential side effects, authentication needs, or limitations. This is adequate but not comprehensive.
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 three sentences: purpose with scope, usage guidance, and output format. It is front-loaded with essential information and contains no unnecessary words.
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 recommendation nature, 4 parameters, and no output schema, the description sufficiently covers what the tool does and what it returns. It could mention pagination or sorting behavior, but the current description is adequate for 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 explains all parameters. The description adds some nuance to the 'task' parameter (e.g., natural language, technical question, prompt), which is helpful but not essential. Overall, the description adds marginal value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool recommends AI tools, skills, MCP servers, agents, rules, and resources from the SkillHub ecosystem based on user's task. It distinguishes itself from sibling tools like search and get_resource by focusing on discovery and ranking.
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 explicitly states when to use: when the user could benefit from discovering relevant AI tools, needs help finding the right framework/library, or is working on a task improvable with AI resources. It does not explicitly state when not to use, but the context is clear enough.
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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