Brave Search With Proxy
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have clearly distinct purposes: brave_local_search targets local businesses and physical locations with 'near me' queries, while brave_web_search handles general web searches for information, news, and online content. There is no overlap in functionality, as each tool is specialized for different query types and data sources, making it easy for an agent to choose the correct one based on the user's intent.
Naming Consistency5/5Both tool names follow a consistent pattern: 'brave_' prefix followed by a descriptive term ('local_search' and 'web_search') using snake_case. This naming convention clearly indicates the server domain (Brave Search) and the specific search type, making the tools predictable and easy to understand without any deviations or mixed styles.
Tool Count3/5With only 2 tools, the server feels thin for a search domain, as it might benefit from additional tools like image search, news search, or advanced filtering options. However, the tools cover the core local and web search functionalities adequately, so it's borderline but not severely lacking. A typical search server would have more tools to handle diverse query types and result formats.
Completeness4/5The server covers the essential search operations for local and web queries, with each tool providing detailed capabilities like pagination, filtering, and fallback mechanisms. Minor gaps exist, such as no dedicated tools for image or video searches, but agents can work around this using the web search tool. The surface is reasonably complete for basic search needs without significant dead ends.
Average 4.3/5 across 2 of 2 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.
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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, so description carries full burden. Mentions pagination, content filtering, freshness controls, and maximum results (20). However, does not explicitly state it is a read-only operation or discuss rate limits/auth. 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences: first states purpose, second provides usage scenarios, third mentions capabilities. No fluff, well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has 3 params and no output schema. Description covers purpose, usage, and constraints but lacks explanation of return value format or structure. Could be more complete for agent usage.
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%; baseline 3. Description adds slight value by reiterating max 20 results and offset usage, but no new parameter details beyond schema. Content filtering and freshness controls are mentioned but are not parameters.
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 'Performs a web search using the Brave Search API' with specific verb+resource. Distinguishes from siblings like brave_answers and brave_local_search by stating 'ideal for general queries, news, articles, and online content.'
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?
Provides explicit usage context: 'ideal for general queries, news, articles, and online content. Use this for broad information gathering, recent events, or when you need diverse web sources.' Lacks explicit when-not-to-use or alternatives, but context is clear.
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 the description carries full burden. It discloses automatic fallback to web search and lists returned information (names, ratings, hours). It lacks details on rate limits or authorization, but the key behavior is transparent.
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 a concise paragraph with bullet points. It front-loads the purpose, then gives usage context, return info, and a fallback note. No unnecessary words, well-organized.
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 details (business names, addresses, ratings, etc.) and fallback behavior. It misses error handling or pagination, but for a local search tool with two simple params, it is sufficiently complete.
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?
Schema coverage is 100% (both params described), baseline 3. The description adds meaning by specifying query examples ('pizza near Central Park') and default count behavior, plus clarifies what the return values include. This goes 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 the tool searches for local businesses and places using Brave's Local Search API. It distinguishes from siblings like brave_web_search by specifying physical locations and business entities, and notes a fallback to web search.
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 says 'Best for queries related to physical locations' and 'Use this when the query implies "near me" or mentions specific locations,' providing clear when-to-use guidance. It does not explicitly state when not to use, but the context is strong.
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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