HN-MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: browse_stories for listing stories, get_story_details for fetching story content and comments, hn_explain for term explanations, search_hn for searching content, and user_analysis for user profiles. There is no overlap in functionality, making tool selection unambiguous.
Naming Consistency4/5
The naming follows a consistent verb_noun pattern with snake_case throughout, such as browse_stories and get_story_details. The only minor deviation is hn_explain, which uses a prefix 'hn_' instead of a verb, but it still fits the overall readable convention.
Tool Count5/5
With 5 tools, the server is well-scoped for interacting with Hacker News. Each tool serves a specific, non-trivial function, such as browsing, fetching details, explaining terms, searching, and analyzing users, making the count appropriate and efficient.
Completeness4/5
The tool set covers core Hacker News interactions well, including story listing, detail retrieval, searching, and user analysis. A minor gap is the lack of tools for posting or interacting with content, but for a read-focused server, the coverage is comprehensive and allows agents to handle most common tasks.
Average 3.3/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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 is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return format but does not cover important aspects such as whether this is a read-only operation, potential rate limits, authentication needs, or how results are paginated/ordered. This leaves significant gaps for a tool that fetches data.
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 and return value without any wasted words. It is appropriately sized for the tool's complexity and gets straight to the point.
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 (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return format but lacks details on behavioral traits and usage context, which are important for an agent to operate effectively without structured annotations.
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, clearly documenting both parameters with enums and constraints. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 without compensating 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 verb ('browse') and resource ('Hacker News stories by type'), and specifies what is returned ('story list with scores, comments, and metadata'). However, it does not explicitly differentiate from sibling tools like 'search_hn' or 'get_story_details', which would be needed for 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 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 'search_hn' or 'get_story_details'. It mentions the tool's function but lacks explicit context, exclusions, or comparisons with siblings, leaving the agent without clear usage 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Get explanations,' which implies a read-only operation, but doesn't clarify aspects like response format, potential rate limits, error handling, or whether it requires authentication. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior beyond the basic purpose.
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: 'Get explanations of Hacker News terms, culture, and conventions.' It is front-loaded with the core purpose, has no unnecessary words, and every part earns its place by specifying the action and scope. This makes it highly concise and well-structured for quick understanding.
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 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, output expectations, or integration with sibling tools. Without an output schema, it should ideally hint at what the explanations look like, but it doesn't, leaving some context incomplete for effective agent 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 input schema has 100% description coverage, with the 'term' parameter well-documented as 'HN term or concept (e.g., "karma", "flagged", "dupe", "Show HN", "Ask HN")'. The description adds no additional parameter semantics beyond what the schema provides, such as examples or usage tips. Since schema coverage is high, the baseline score of 3 is appropriate, as the schema adequately handles parameter documentation.
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 explanations of Hacker News terms, culture, and conventions.' It specifies the action ('Get explanations') and the resource ('Hacker News terms, culture, and conventions'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'user_analysis' or 'search_hn', which might also provide explanatory information in different contexts.
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 by mentioning 'terms, culture, and conventions,' suggesting it's for understanding Hacker News-specific concepts. However, it lacks explicit guidance on when to use this tool versus alternatives like 'search_hn' for general searches or 'user_analysis' for user-related insights. No exclusions or clear alternatives are provided, leaving some ambiguity in context.
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. While it mentions what gets analyzed (profile, karma, submissions), it doesn't describe key behavioral traits such as whether this is a read-only operation, what the output format looks like, potential rate limits, or authentication needs. For a tool with no annotation coverage, 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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence ('Analyze a Hacker News user's profile, karma, and recent submissions') directly contributes to understanding the tool's function, making it appropriately concise and well-structured.
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 (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral aspects like output format or operational constraints. Without annotations or an output schema, the description should do more to compensate, but it only meets the minimum viable threshold.
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 fully documents both parameters (username and submissionLimit). The description doesn't add any parameter-specific semantics beyond what's in the schema, such as explaining how submissionLimit affects the analysis or providing examples beyond the schema's descriptions. 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: 'Analyze a Hacker News user's profile, karma, and recent submissions.' It specifies the verb ('analyze'), resource ('Hacker News user'), and scope ('profile, karma, and recent submissions'). However, it doesn't explicitly differentiate from sibling tools like 'hn_explain' or 'search_hn' which might also involve user-related analysis, so it falls short of 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying what it analyzes (user data), but doesn't provide explicit guidance on when to use this tool versus alternatives like 'search_hn' for broader searches or 'hn_explain' for explanations. It lacks clear when/when-not statements or named alternatives, leaving usage somewhat ambiguous.
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 fetching 'full story content and comment threads', which suggests a read-only operation, but doesn't disclose behavioral traits like rate limits, authentication needs, error handling, or response format. For a tool with no annotation coverage, 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 two concise sentences that are front-loaded with the core purpose. Every sentence earns its place by specifying what is fetched and the scope (story and comments), with no redundant or vague language.
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 no annotations and no output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., structure of story and comments), error conditions, or other contextual details needed for effective use. For a tool fetching complex data like comment threads, this is inadequate.
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 doesn't add any meaning beyond what the schema provides (e.g., it doesn't explain how 'maxComments' or 'commentDepth' affect the output). 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'Hacker News story with its comments', specifying it fetches 'full story content and comment threads'. It distinguishes from siblings like 'browse_stories' (likely listing) and 'search_hn' (searching) by focusing on retrieving a specific story's details.
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 a specific story's details are needed, but doesn't explicitly state when to use this tool versus alternatives like 'browse_stories' or 'search_hn'. It provides some guidance through parameter descriptions (e.g., 'Change ONLY IF user specifies'), but lacks explicit when/when-not directives or named alternatives.
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 return type ('matching content with relevance scores'), which is helpful, but fails to address critical aspects like rate limits, authentication needs, error handling, or pagination behavior. For a search tool with 5 parameters, this leaves significant gaps in understanding how the tool behaves in practice.
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 ('Search Hacker News stories and comments') and immediately states the return value. Every word earns its place with zero redundancy or fluff, making it easy for an agent to parse 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 moderate complexity (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return type but lacks behavioral details (e.g., rate limits, errors) and output structure clarification. Without an output schema, the agent must infer the exact format of 'matching content with relevance scores'.
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%, meaning all parameters are well-documented in the schema itself. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain query syntax, tag combinations, or how relevance scores are calculated). 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.
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 ('Search Hacker News stories and comments') and resource ('Hacker News'), distinguishing it from siblings like 'browse_stories' (likely browsing without search) and 'get_story_details' (fetching specific story details). It explicitly mentions what it returns ('matching content with relevance scores'), making the purpose unambiguous.
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 search scenarios but provides no explicit guidance on when to use this tool versus alternatives like 'browse_stories' or 'hn_explain'. It lacks any 'when-not' or prerequisite information, leaving the agent to infer context from the tool name and description alone.
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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