Internetsearch-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. The tool has a single, clear purpose: performing internet searches based on a query.
Naming Consistency5/5Since there is only one tool, naming consistency is inherently perfect. The tool name 'InternetSearch' follows a clear and descriptive pattern, though no comparison to other tools is possible.
Tool Count2/5A single tool is too few for a server named 'Internetsearch-mcp-server', which suggests a broader scope for internet-related operations. This minimal set limits functionality and may cause agent failures due to lack of complementary tools like filtering, pagination, or advanced search options.
Completeness2/5The tool surface is severely incomplete for internet search functionality. While it covers basic search, it lacks essential operations such as filtering results, handling pagination, specifying search domains, or retrieving detailed page content, which are common in search APIs.
Average 2.9/5 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
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
- 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 the tool performs internet searches but doesn't describe how it works (e.g., search engine used, rate limits, authentication needs, privacy implications, or what happens on failure). For a tool that interacts with external resources, this lack of transparency is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the main purpose in the first sentence. The Args section is structured but could be more integrated. There's no wasted text, though it could benefit from slightly more detail without losing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (internet search with external interactions), lack of annotations, no output schema, and low parameter coverage, the description is incomplete. It doesn't address behavioral aspects like error handling, result format, or limitations, which are crucial for an AI agent to use it effectively.
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 description adds minimal parameter semantics: it explains that 'query' is '需要联网搜索的问题' (the question that needs internet search). With schema description coverage at 0% and 2 parameters (query and txt_count), the description only covers one parameter partially. It doesn't explain txt_count at all, leaving it undocumented. Baseline is 3 due to some value added but incomplete coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as '联网搜索对应问题的答案' (search the internet for answers to corresponding questions), which is a specific verb+resource combination. It distinguishes itself as an internet search tool, though with no sibling tools mentioned, differentiation isn't applicable. The purpose is unambiguous but could be more precise about what type of answers it provides.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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, prerequisites, or context for usage. It simply states what the tool does without indicating scenarios where it's appropriate or inappropriate. With no sibling tools, this is less critical, but still a gap in usage context.
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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