browse-ai

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: browse_answer handles full research pipelines, browse_compare compares answer types, browse_extract extracts from single pages, browse_open fetches/parses pages, and browse_search performs web searches. The descriptions clearly differentiate their scopes and workflows.
Naming Consistency5/5
All tools follow a consistent 'browse_verb' pattern (browse_answer, browse_compare, browse_extract, browse_open, browse_search), using snake_case uniformly. This predictable naming makes it easy for agents to understand and select tools based on their action verbs.
Tool Count5/5
With 5 tools, this server is well-scoped for web research and browsing tasks. Each tool earns its place by covering distinct aspects of the domain (searching, fetching, extracting, comparing, and full research), avoiding bloat while providing comprehensive functionality.
Completeness4/5
The tool set covers core web research workflows effectively, including search, page retrieval, extraction, and answer generation/comparison. A minor gap exists in operations like updating or managing saved data, but agents can work around this for most research tasks.
Average 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
- Last stable release on March 25, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
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 describes the tool's function but lacks details on how the comparison is performed, what the output format looks like, whether it requires specific data inputs beyond the query, or any rate limits or error conditions. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded in a single sentence, efficiently stating the tool's purpose without unnecessary words. However, it could be more structured by explicitly separating the function from usage context, but it earns its place by being clear and to the point.
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 comparing answers and the lack of annotations, output schema, and poor parameter documentation, the description is incomplete. It doesn't cover how the tool behaves, what inputs are needed beyond the query, or what results to expect, making it inadequate for effective agent use without additional context.
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 input schema has 1 parameter with 0% description coverage, and the tool description provides no information about the 'query' parameter. It doesn't explain what the query should contain, its format, or how it relates to the comparison process, failing to compensate for the lack of schema 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: comparing a raw LLM answer against an evidence-backed answer to show differences between hallucination-prone and grounded responses. It specifies the verb 'compare' and the resource 'answers', but doesn't differentiate from sibling tools like browse_answer or browse_search, which likely handle similar content.
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 browse_answer or browse_search. It mentions comparing two types of answers but doesn't specify prerequisites, context, or exclusions for usage, leaving the agent without clear selection criteria among siblings.
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 what is returned (URLs, titles, snippets, relevance scores) but lacks critical details: it doesn't specify rate limits, authentication needs, potential costs, or how results are sourced (e.g., search engine used). For a web search tool with zero 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 appropriately sized and front-loaded: it states the core purpose in the first sentence and adds return details in the second. Every sentence earns its place with no wasted words, making it efficient and easy to parse.
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 a web search tool, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on behavioral traits (e.g., rate limits), parameter usage, and how to interpret results (e.g., relevance scores). The description does not adequately compensate for the missing structured data.
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 description coverage is 0%, so the description must compensate for undocumented parameters. It adds no meaning beyond the schema: it doesn't explain what 'query' should contain (e.g., keywords, phrases) or 'limit' (e.g., max results, default value). With 2 parameters and no schema descriptions, the description fails to provide necessary semantic context.
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: 'Search the web for information on a topic.' It specifies the verb ('Search') and resource ('the web'), and mentions what information is returned. However, it doesn't explicitly differentiate from sibling tools like 'browse_answer' or 'browse_compare', which likely have related but distinct purposes.
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 its siblings (browse_answer, browse_compare, browse_extract, browse_open). It implies usage for general web searching but offers no explicit alternatives, exclusions, or context for selection among related tools.
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 AI-based extraction but doesn't cover critical aspects like rate limits, authentication needs, error handling, or what happens if extraction fails. For a tool with no annotations, 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 core purpose without unnecessary words. It directly states what the tool does, making it easy to parse and understand quickly.
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 AI-based extraction, no annotations, no output schema, and low parameter coverage, the description is incomplete. It lacks details on output format, error conditions, and behavioral constraints, making it inadequate for a tool with two parameters and no structured support.
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 0%, so the schema provides no parameter details. The description doesn't explain the parameters (url and query) beyond implying they relate to web page extraction. It adds minimal semantic value, failing to compensate for the low schema coverage, but at least hints at the tool's function.
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: extracting structured knowledge (claims, sources, confidence) from a single web page using AI. It specifies the verb 'extract' and resource 'structured knowledge from a single web page', but doesn't explicitly differentiate from sibling tools like browse_answer or browse_compare, which likely serve different purposes.
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 such as browse_answer or browse_search, leaving the agent to infer usage 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 outlines the pipeline steps, it fails to mention critical behavioral traits such as execution time, rate limits, authentication needs, error handling, or what happens if steps fail. For a complex multi-step tool with no annotations, 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 appropriately sized and front-loaded, listing key steps in a single sentence without unnecessary words. However, it could be more structured by separating steps with commas or bullet points for clarity, and some phrases like 'Full deep research pipeline' are slightly redundant with the tool name 'browse_answer'.
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 (multi-step pipeline), lack of annotations, no output schema, and minimal parameter guidance, the description is incomplete. It doesn't explain return values (e.g., format of the 'structured answer'), error conditions, or performance considerations, 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 schema has 1 parameter with 0% description coverage, so the description must compensate. It implies the 'query' parameter drives the research pipeline but doesn't add meaning beyond that (e.g., format expectations, length limits, or examples). Since there's only one parameter, the baseline is 4, but the description provides minimal semantic value, resulting in a score of 3.
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 the web, fetch pages, extract claims, build evidence graph, generate a structured answer') and resources ('with citations and confidence score'), distinguishing it from sibling tools like browse_search or browse_extract by describing a comprehensive multi-step pipeline rather than individual operations.
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 browse_search (for simple searches) or browse_compare (for comparisons). It implies usage for 'full deep research' but lacks explicit when/when-not instructions or prerequisites, 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.
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 behavior: fetching and parsing web pages, using Readability to strip ads, navigation, and boilerplate. However, it lacks details on error handling, rate limits, authentication needs, or output format (e.g., text structure). This is a moderate 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 a single, efficient sentence that front-loads the core purpose ('Fetch and parse a web page into clean text') and adds clarifying details ('using Readability. Strips ads, nav, and boilerplate.'). Every part earns its place by specifying the method and outcome without redundancy or unnecessary elaboration.
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 (web parsing with cleanup), no annotations, no output schema, and low schema coverage (0%), the description is incomplete. It covers the basic operation but omits critical details like output format, error cases, or performance considerations. For a tool with no structured support, this leaves significant gaps for an agent to use it effectively.
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 1 parameter (url) with 0% description coverage, so the schema provides no semantic context. The description doesn't explicitly mention parameters, but it implies the 'url' parameter by stating 'Fetch and parse a web page.' This adds minimal meaning beyond the schema. With 0 parameters documented in the schema, the baseline is 4, as the description compensates slightly by clarifying the resource type.
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: 'Fetch and parse a web page into clean text using Readability.' It specifies the verb (fetch and parse) and resource (web page), and mentions the technology (Readability) and outcome (clean text). However, it doesn't explicitly differentiate from sibling tools like browse_answer or browse_extract, which likely have related but distinct purposes.
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 its siblings (browse_answer, browse_compare, browse_extract, browse_search). It mentions stripping ads, nav, and boilerplate, which implies a use case for clean text extraction, but doesn't specify alternatives or exclusions. Without explicit comparisons, 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.

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