superhighway-mcp
Server Quality Checklist
Latest release: v1.2.0
- Disambiguation5/5
Each tool targets a distinct source or operation: images, news, web search, combined search+scrape, and raw scraping. The descriptions clearly differentiate their use cases, so an agent can easily select the right one.
Naming Consistency4/5Tool names mostly follow a verb_noun pattern (e.g., web_search, news_search, image_search), but 'scrape' is a bare verb and 'research' is a noun, creating a minor inconsistency. Overall still readable and predictable.
Tool Count5/5Five tools cover the essential web research functionality without redundancy or bloat. Each tool serves a clear purpose, and the count feels well-scoped for the stated domain.
Completeness5/5The set covers image, news, and general web search, plus page scraping and a combined research operation. This spans the full typical workflow for web-based information retrieval, leaving no significant gaps.
Average 4.3/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 29 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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description discloses payment model (paid per call in USDC via x402, no signup, no API key) and return format (JSON). Lacks details on rate limits or error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with clear structure: defines tool, then explains payment and use cases. No superfluous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers return format and payment. No output schema, but description lists fields. Adequate for a simple two-parameter tool; missing only minor details like rate limits.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. Description does not add meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it is a 'Real-time news search' and specifies output fields (title, url, snippet, published date). Distinguishes from sibling tools like web_search and image_search by focusing on news.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists use cases (current events, breaking news, monitoring, time-sensitive facts) but does not mention when not to use or suggest alternatives among siblings.
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 full burden. It discloses the pricing model (paid per call via x402, no signup, no API key) and the multi-engine metasearch nature. It does not mention rate limits or caching, but for a search tool, the transparency is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences: functional output, pricing model, and use cases. No wasted words; critical information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given only 2 parameters and no output schema, the description covers the essentials: what it searches, what it returns (image URLs, source pages, thumbnails, JSON), cost model, and use cases. It is complete enough for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% both parameters have descriptions. The description adds context about the return format but does not add meaning beyond what the schema already provides for the parameters. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Image search' and enumerates the outputs (direct image URLs, source pages, thumbnails as JSON). This verb+resource combination is specific and easily distinguishes it from sibling tools like web_search, news_search, and research.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists use cases: 'visual grounding, finding images for content generation, and multimodal research.' It does not explicitly state when not to use or alternatives, but the sibling tool names imply the context well enough for an AI agent.
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, so description carries full burden. Discloses multi-engine metasearch, paid per call via x402, no signup/key needed. Describes output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences. First sentence covers purpose and output. Second sentence adds pricing and use cases. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description explains return fields (title, url, snippet). Covers purpose, pricing, use cases. Parameters documented. Missing pagination details, but acceptable for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters with descriptions. Description adds default for limit (5) and range (1-20), but schema already specifies these. Minimal extra value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'real-time web search' with specific output format (JSON, ranked, with title/url/snippet). Distinguishes from sibling tools by implying general web results.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit use cases: fresh facts, research, fact-checking, grounding/RAG. Pricing model noted. No direct alternatives named, but sibling list implies scope.
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 core actions (search+scrape), output format (clean markdown, content not links), and cost. Missing failure modes or rate limits, but sufficient for basic use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no waste. The first sentence front-loads the core functionality and unique value. Every part serves a purpose, including pricing and usage guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the combined search+scrape behavior, but lacks details on the return structure (e.g., how results are ranked, text format). However, for a tool with no output schema and two simple params, it provides adequate context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both parameters described. The description reinforces they exist but adds only marginal context (e.g., 'top results', 'clean markdown'). No new constraints or clarifications beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'searches the live web AND reads the top result pages as clean markdown', specifying both the verb and resource. It distinguishes from siblings like web_search and scrape by combining search and scrape in one call.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use to answer questions from fresh sources in a single tool call instead of search-then-scrape round-trips', providing clear when-to-use and an alternative to sibling tools. Also mentions pricing and no signup.
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, the description carries full burden. It discloses the payment model (paid per call via x402, no signup), the output format (title, markdown, plain text), and that it's a read-only operation. It lacks details on rate limits or error handling, but for a simple tool this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core purpose. Each sentence adds value: purpose, input/output, and use case/payment model. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema, no annotations), the description covers all necessary information: what it does, how to use it, what to expect, and special conditions (payment). It is fully sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% of parameters with a basic description. The tool's description adds meaning by explaining what happens with the URL ('Get back the page title, readable markdown, and plain text') and the expected input (a URL). This goes beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads web pages and returns clean text and markdown. It specifies the action (read, fetch, scrape) and the resource (web pages). The sibling tools are search-oriented, so this tool's distinct purpose is well-defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes explicit use cases: 'Use to let the agent read pages, fetch articles/docs it can't access, scrape content, and feed RAG.' While it doesn't explicitly state when not to use, the context of siblings implies this is for direct URL access, not search.
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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- Evaluate tool definition quality.
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