HackerNews MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose: get_post retrieves specific post details, search_comments analyzes comment trees, search_posts filters posts by various criteria, search_trending finds trending topics, and search_user accesses user profiles. There is no overlap in functionality, making tool selection straightforward for an agent.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with 'get_' or 'search_' prefixes, using snake_case throughout. This predictability enhances readability and reduces cognitive load when navigating the tool set.
Tool Count5/5
With 5 tools, the server is well-scoped for HackerNews interactions, covering key operations like retrieving posts, searching content, analyzing trends, and accessing user data. Each tool earns its place without feeling excessive or insufficient for the domain.
Completeness4/5
The tool set provides comprehensive coverage for reading and analyzing HackerNews data, including posts, comments, trends, and users. A minor gap exists in write operations (e.g., posting or voting), but this is acceptable for a read-focused server, and agents can still perform core workflows effectively.
Average 2.9/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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.
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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 carries the full burden of behavioral disclosure. It mentions retrieving 'comprehensive details' and 'comments', which hints at read-only behavior, but doesn't explicitly state if this is a safe read operation, what permissions are needed, or how it handles errors (e.g., invalid IDs). 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, efficient sentence that front-loads the key action ('Get comprehensive details') and resource ('HackerNews post'). It wastes no words and clearly communicates the core functionality without unnecessary elaboration.
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 (a read operation with 2 parameters), no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain return values, error handling, or parameter details, leaving the agent with insufficient context to use the tool effectively beyond a basic understanding of its purpose.
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%, meaning parameters are undocumented in the schema. The description doesn't add any meaning beyond what the schema provides—it doesn't explain what 'id' represents (e.g., a HackerNews post ID) or what 'includeComments' does (e.g., toggles comment retrieval). With 2 parameters and low coverage, the description fails to compensate for the schema's lack of 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 with specific verbs ('Get comprehensive details') and resources ('about a HackerNews post'), including what information is retrieved ('metadata and comments'). It distinguishes from siblings like search_posts by focusing on retrieving details for a specific post rather than searching. However, it doesn't explicitly mention the sibling differentiation in the description text itself.
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_posts or search_comments. It doesn't mention prerequisites (e.g., needing a post ID) or exclusions (e.g., not for searching). The implied usage is for retrieving details of a known post, but this isn't 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'current trending topics' and 'top HackerNews posts,' implying real-time data and a focus on popularity, but lacks details on rate limits, data freshness, authentication needs, or what constitutes 'trending.' This is a significant gap 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 a single, efficient sentence: 'Find current trending topics and keywords from top HackerNews posts.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence adds value.
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 (external data source, trending analysis), lack of annotations, no output schema, and 0% schema coverage, the description is incomplete. It doesn't cover parameter meanings, behavioral traits like rate limits, or output details (e.g., format of returned topics). This makes it inadequate for effective agent use.
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?
The schema has 0% description coverage, so the description must compensate for both parameters. It doesn't mention minWordLength or postCount at all, failing to explain their roles (e.g., filtering keywords by length or limiting posts analyzed). This leaves the agent guessing about parameter meanings beyond the schema's basic constraints.
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: 'Find current trending topics and keywords from top HackerNews posts.' It specifies the verb ('Find'), resource ('trending topics and keywords'), and source ('top HackerNews posts'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like search_posts or search_comments, 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 when it's appropriate (e.g., for trend analysis vs. specific post searches) or when not to use it, nor does it reference sibling tools like search_posts or search_comments for comparison. This leaves the agent without context for 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 mentions analyzing 'engagement patterns and statistics,' which implies read-only behavior, but doesn't specify whether this requires authentication, has rate limits, or what the output format looks like (e.g., structured data or raw text). 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Analyze the comment tree of a post for engagement patterns and statistics.' It's front-loaded with the core action and outcome, with no wasted words. However, it could be slightly more structured by explicitly mentioning parameters or usage context to enhance clarity without losing conciseness.
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 analyzing comment trees (which may involve nested data), no annotations, no output schema, and low schema description coverage (0%), the description is incomplete. It doesn't address how results are returned, potential limitations (e.g., depth constraints implied by 'maxDepth'), or error conditions. For a tool with these gaps, more detail is needed to ensure the agent can use it 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?
The description doesn't explicitly mention any parameters, but it implies the need for a 'post' to analyze its comment tree, which aligns with the 'postId' parameter in the schema. With 0% schema description coverage, the description adds minimal value by hinting at the resource context. However, it doesn't explain 'maxDepth' or provide details beyond what the schema's structure suggests, resulting in a baseline score.
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 the comment tree of a post for engagement patterns and statistics.' It specifies the verb ('analyze'), resource ('comment tree of a post'), and outcome ('engagement patterns and statistics'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'search_posts' or 'search_user', which might also involve comment-related functionality.
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 a valid postId, or compare it to sibling tools like 'search_posts' (which might search posts rather than comments) or 'search_user' (which might involve user-specific data). This lack of context leaves the agent to infer usage scenarios independently.
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 full burden but offers minimal behavioral insight. It mentions filtering capabilities but doesn't disclose whether this is a read-only operation, if it requires authentication, rate limits, pagination behavior (beyond the 'limit' parameter in schema), or what the output format looks like. For a search tool with 6 parameters, this leaves significant gaps.
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 functionality ('search and filter HackerNews posts') followed by the key filter criteria. Every word earns its place with no redundancy or fluff.
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?
For a search tool with 6 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'keywords' means versus the 'query' parameter, how date ranges work (Unix timestamps?), what 'score' represents, or what the tool returns. The agent lacks critical 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?
The description lists the filterable fields (keywords, author, score, date range), which maps to 4 of the 6 parameters (query, author, minScore, startTime/endTime). However, with 0% schema description coverage, the 'limit' parameter remains undocumented in both schema and description. The description adds some value but doesn't fully compensate for the coverage gap.
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 ('search and filter') and resource ('HackerNews posts'), making the purpose immediately understandable. It distinguishes from siblings like 'get_post' (single post retrieval) and 'search_comments' (different resource type), though it doesn't explicitly contrast with 'search_trending' or 'search_user'.
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 about when to use this tool versus alternatives. While the description implies it's for searching posts, it doesn't mention when to choose this over 'search_trending' (which might find trending posts) or 'get_post' (for retrieving specific posts by ID). The agent must infer usage from tool names alone.
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 describes what data is retrieved but lacks details on permissions, rate limits, error handling, or response format. For a read operation tool, 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.
Conciseness4/5
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 details. It could be slightly improved by structuring usage hints, but it avoids redundancy and waste, making it appropriately concise for its content.
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 retrieving user data with no annotations, no output schema, and low schema description coverage, the description is incomplete. It doesn't cover behavioral aspects like authentication needs, data freshness, or what 'contribution patterns' entail, leaving the agent with insufficient context for reliable 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 specific meaning beyond the input schema, which has 0% description coverage. It doesn't explain what 'includeRecentItems' does or provide context for the 'username' parameter. However, with only 2 parameters and one being optional with a default, the baseline is 3 as the schema provides minimal but adequate structure.
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 specific verbs ('Get') and resources ('HackerNews user's profile, activity, statistics, and contribution patterns'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'get_post' or 'search_comments', which might also retrieve user-related data indirectly.
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 any prerequisites, exclusions, or comparisons to sibling tools like 'search_posts' or 'search_comments', leaving the agent to infer usage based on the name 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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