open-webSearch

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: four tools fetch content from specific platforms (CSDN, GitHub, Juejin, Linux.do), while the search tool performs a general web search across multiple engines. There is no overlap in functionality, making tool selection straightforward.
Naming Consistency4/5
The naming is mostly consistent with a 'fetchX' pattern for four tools (fetchCsdnArticle, fetchGithubReadme, fetchJuejinArticle, fetchLinuxDoArticle), but the search tool deviates by using a simple verb without a noun. All tools use camelCase, so the convention is uniform, but the pattern is not fully applied across all tools.
Tool Count5/5
With 5 tools, the count is well-scoped for the server's purpose of open web search and content fetching. Each tool serves a specific, useful function without redundancy, making the set efficient and manageable for agents.
Completeness3/5
The tool surface covers fetching content from specific platforms and general web search, but there are notable gaps. For example, there is no tool to fetch content from other common sources (e.g., news sites, blogs beyond the listed ones), and no advanced search capabilities like filtering or pagination, which could limit agent effectiveness in broader web tasks.
Average 3.2/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 11 of 20 community issues answered or closed in the last 6 months
- 9 commits in the last 12 weeks
- Last stable release on April 30, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't mention error handling (e.g., invalid URLs, private repos), rate limits, authentication needs, or output format. This leaves significant gaps in understanding how the tool behaves beyond the basic fetch operation.
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, clear sentence with zero wasted words. It's front-loaded with the core purpose and efficiently communicates the essential information without unnecessary elaboration.
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 no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't address error cases, return format, or behavioral constraints that an agent would need to use this tool effectively in real-world scenarios.
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 description adds minimal semantic context beyond the schema: it implies the 'url' parameter should be a GitHub repository URL. However, with 0% schema description coverage and only one parameter, this provides some value but doesn't fully compensate for the lack of schema documentation (e.g., URL format expectations).
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 action ('Fetch') and resource ('README content from a GitHub repository URL'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'fetchCsdnArticle' or 'fetchJuejinArticle' beyond specifying GitHub as the source, which is a minor gap.
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 'search' or other fetch tools. It mentions GitHub specifically, but doesn't explain when to prefer this over general search or other content-fetching tools, leaving usage context implied at best.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool fetches content but lacks details on error handling, rate limits, authentication needs, or what 'full article content' entails (e.g., text format, metadata). This is a significant gap for a tool with zero 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 with zero waste. It front-loads the key action and resource, making it easy to parse quickly. Every word earns its place without redundancy.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'full article content' returns (e.g., plain text, HTML, structured data) or address potential behavioral aspects like errors or limitations. For a tool with no structured support, this leaves critical gaps for an AI agent.
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 input schema has 1 parameter with 0% description coverage, so the description must compensate. It implies the 'url' parameter should be a linux.do post URL, adding some context beyond the schema's generic URI format. However, it doesn't specify URL format details or validation rules, leaving gaps in parameter understanding.
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 action ('fetch full article content') and the resource ('from a linux.do post URL'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like fetchCsdnArticle or fetchGithubReadme beyond the domain name, missing a direct comparison that would earn a 5.
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 when to choose fetchLinuxDoArticle over other fetch tools (e.g., for linux.do posts specifically) or over the search tool, leaving the agent to infer usage from the name alone.
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 states the action ('fetch full article content') but doesn't describe what 'full article content' includes (e.g., text, images, metadata), potential errors (e.g., invalid URLs, network issues), or any constraints (e.g., rate limits, authentication needs). This leaves significant gaps for an agent to understand how the tool behaves beyond the basic operation.
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 action ('fetch full article content') and resource ('from a csdn post URL'). There is no wasted language, and it directly communicates the essential information without 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 low complexity (one parameter, no annotations, no output schema), the description is minimally complete. It covers the basic purpose but lacks details on usage guidelines, behavioral traits, and output specifics. For a simple fetch operation, this might be adequate, but it doesn't provide enough context for an agent to handle edge cases or alternatives effectively.
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 description implies the 'url' parameter must be a csdn post URL, which adds meaning beyond the schema's generic URI format. However, with 0% schema description coverage and only one parameter, the baseline is 4 for zero parameters, but here one parameter is partially clarified. The description doesn't specify URL format details (e.g., must include 'csdn.net'), so it compensates somewhat but not fully.
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 verb 'fetch' and the resource 'full article content from a csdn post URL', making the purpose immediately understandable. It distinguishes from siblings like fetchGithubReadme by specifying the source (csdn) and content type (article), though it doesn't explicitly contrast with other article-fetching siblings like fetchJuejinArticle or fetchLinuxDoArticle.
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 when to choose fetchCsdnArticle over fetchJuejinArticle or fetchLinuxDoArticle, nor does it indicate any prerequisites or exclusions. The only implied usage is for csdn URLs, but this is already covered in the purpose.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'no API key required' which is useful context about authentication needs, but fails to describe critical behaviors like rate limits, result format, pagination, or whether this is a read-only operation. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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 perfectly concise - a single sentence that efficiently communicates the core functionality and key feature (no API key). Every word earns its place with no redundancy or unnecessary elaboration.
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?
For a search tool with 3 parameters, 0% schema description coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what results look like, how they're structured, whether there are usage limits, or provide sufficient parameter guidance. The 'no API key required' is helpful but doesn't compensate for the overall lack of context.
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 description coverage is 0%, so the description must compensate for parameter documentation. It mentions 'multiple engines' and lists examples that correspond to the 'engines' parameter enum values, adding some semantic context. However, it doesn't explain the 'query' or 'limit' parameters at all, leaving half the parameters undocumented in both schema and description.
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 using multiple engines' with specific examples provided (Baidu, Bing, etc.). It distinguishes itself from sibling tools (which fetch specific articles from single sources) by offering multi-engine web search. However, it doesn't specify the exact verb+resource combination (e.g., 'retrieve web search results') with complete precision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning 'no API key required,' suggesting this tool is accessible without authentication. However, it provides no explicit guidance on when to use this tool versus the sibling article-fetching tools, nor does it mention any exclusions or alternatives for different search scenarios.
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 'fetch full article content,' which implies a read-only operation, but does not disclose other traits such as authentication needs, rate limits, error handling, or what 'full content' entails (e.g., includes images, metadata). This leaves significant gaps for an agent to understand the tool's 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 is front-loaded with the core purpose. There is no wasted text, and every word contributes directly to understanding the tool's function, making it highly concise and well-structured.
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 low complexity (one parameter, no output schema, no annotations), the description is minimally complete. It covers the basic purpose and parameter semantics but lacks details on behavioral aspects like output format, error cases, or usage constraints, which could hinder an agent's ability to use it correctly in varied contexts.
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 description adds meaning beyond the input schema by specifying that the 'url' parameter must be a 'Juejin post URL,' which clarifies the expected content type. With schema description coverage at 0% and only one parameter, this compensation is effective, though it could further detail URL format or validation rules.
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 specific action ('fetch full article content') and resource ('from a Juejin post URL'), distinguishing it from sibling tools like fetchCsdnArticle or fetchGithubReadme by specifying the Juejin platform. It uses precise verbs and identifies the exact resource type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'Juejin post URL,' suggesting it should be used for articles from that platform. However, it does not explicitly state when to use this tool versus alternatives like search or other fetch tools, nor does it provide exclusions or prerequisites for usage.
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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