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Johnhong9527

bocha-mcp

by Johnhong9527
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation3/5

    Both tools are search-based and can return similar result types, creating potential ambiguity. The AI search tool explicitly notes it should only be used when requested, which helps, but the boundary between general web search and AI-enhanced search is not always obvious.

    Naming Consistency5/5

    Both tool names follow a consistent pattern: a type prefix followed by 'search' (web-search, ai-search). This makes the naming predictable and easy to understand.

    Tool Count4/5

    With only two tools, the server is on the lean side, but for a focused search API server, this is reasonable. Each tool serves a distinct search mode, so the count feels appropriate for the scope.

    Completeness5/5

    The tool surface covers general web search and AI-powered search, including images, videos, and multimodal cards. No obvious missing functionality for the stated purpose of a search API.

  • Average 4/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 3 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

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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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?

    With no annotations, the description carries the full burden of disclosing behavior. It explains what is returned and supported filters, but it does not disclose rate limits, potential side effects, or limitations. This is adequate but not outstanding.

    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 concise and front-loaded, consisting of two sentences that clearly state the purpose and key capabilities without unnecessary detail. Every word earns its place.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given that there is no output schema and no annotations, the description must explain return values and use context. It does so reasonably by listing returned content types and filters, but it could be improved by mentioning pagination or edge cases. Overall, it is mostly complete for a web search tool.

    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?

    Schema coverage is 100%, so the baseline is 3. The description adds value by explaining natural language queries, time range filtering, and site-specific search, which clarifies how the parameters are intended to be used beyond the schema's individual descriptions.

    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 identifies the tool as a web search via Bocha API, specifying the types of results returned. However, it does not distinguish this tool from the sibling 'ai-search' tool, so it does not achieve the highest 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?

    There is no guidance on when to use this tool versus the sibling 'ai-search' tool, nor any exclusions or alternative suggestions. The description only lists features and leaves the agent to infer appropriate usage.

    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 the full burden of disclosing behavior. It reveals the tool returns diverse result types and supports streaming, which goes beyond a generic search description. It does not mention limitations or error conditions, but for a read-only search tool the core behavior is well-covered.

    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 compact and front-loaded: it starts with the core action, then enumerates outputs, mentions streaming, and ends with a usage constraint. Each sentence contributes distinct, useful information with no repetition or filler.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool has 5 parameters all documented in the schema and no output schema, the description sufficiently explains the return types and adds usage context. It could have mentioned the freshness filter or count behavior, but those are already in the schema, so the description is adequate for a search tool.

    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?

    Schema coverage is 100% with parameter descriptions, so baseline is 3. The description adds meaning by explaining that 'AI-generated answers' and 'follow-up questions' are part of the output, which elaborates the 'answer' parameter's effect. It also mentions streaming, reinforcing the 'stream' parameter usage.

    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 performs a search via Bocha AI Search API, a specific resource distinct from the sibling 'web-search'. It also lists concrete output types (web results, images, multimodal cards, AI answers, follow-up questions), 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 Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides an explicit usage rule: 'Use only when specifically requested by the user.' This is a clear contextual guideline, though it does not directly name alternatives or contrast with the sibling tool. The rule effectively narrows the appropriate use case.

    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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