Claude Web Search MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap between tools, as there are no other tools to compare it against. The single tool's purpose is clearly defined and distinct by default.
Naming Consistency5/5The single tool name 'web_search' follows a consistent verb_noun pattern, and since there are no other tools, there is no inconsistency to evaluate. The naming is straightforward and appropriate for its function.
Tool Count2/5A single tool is generally too few for most server purposes, as it limits functionality and can feel thin or incomplete. For a web search server, while the core function is covered, additional tools like advanced search filters or result management might be expected to enhance utility.
Completeness3/5The server provides a basic search function, which covers the essential need for real-time information retrieval. However, there are notable gaps, such as lack of tools for refining searches, handling pagination, or accessing specific types of content, which could limit agent effectiveness in complex scenarios.
Average 4.1/5 across 1 of 1 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
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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 are provided, so the description carries the full burden. It mentions the tool's purpose and usage context but lacks details on behavioral traits such as rate limits, authentication needs, or specific output format. The description does not contradict any annotations, but it could be more informative about operational constraints.
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 front-loaded and concise, consisting of two sentences that directly address purpose and usage guidelines without unnecessary details. Every sentence earns its place by providing essential information efficiently.
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?
Given the tool's complexity (web search with 4 parameters) and no output schema, the description is adequate but could be more complete. It covers purpose and usage well but lacks details on behavioral aspects like result format or limitations. With no annotations, it should ideally provide more context to fully guide the agent.
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?
The input schema has 100% description coverage, so the schema already documents all parameters well. The description does not add any parameter-specific information beyond what the schema provides, such as examples or additional context. This meets the baseline for high schema coverage, but no extra value is added.
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's purpose with specific verbs ('search the web') and resources ('real-time information about any topic'). It distinguishes the tool's function from potential alternatives by emphasizing real-time, up-to-date information that might not be in training data, which is essential for a web search tool.
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?
The description explicitly states when to use this tool: 'when you need up-to-date information that might not be available in your training data, or when you need to verify current facts.' This provides clear context and guidance for the agent, even though no sibling tools are listed, making it comprehensive for decision-making.
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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