mcp-server-requests
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The three tools have clearly distinct purposes with no overlap. 'fetch' retrieves web content for immediate use, 'fetch_to_file' saves web content to a file, and 'http_request' handles general HTTP requests with full control over methods and parameters. Each tool serves a unique function in the web request workflow.
Naming Consistency5/5All tools follow a consistent snake_case naming pattern with clear verb-action structure. 'fetch', 'fetch_to_file', and 'http_request' all use descriptive verbs that accurately reflect their functionality, maintaining excellent naming consistency throughout the toolset.
Tool Count4/5Three tools is reasonable for a web requests server, though slightly minimal. The tools cover the core use cases well, but the count feels slightly lean for a server that could potentially benefit from additional specialized tools like websocket handling or streaming responses.
Completeness5/5The toolset provides comprehensive coverage for web request operations. It includes content fetching with processing options, file-based fetching, and a full-featured HTTP client supporting all major methods, headers, and data formats. No obvious gaps exist for a general-purpose web requests server.
Average 4.4/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed 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
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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
- Behavior3/5
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 clearly describes what the tool does (fetching and processing web content) and includes important details about the different processing formats available. However, it doesn't mention potential behavioral aspects like rate limits, authentication requirements, error handling, timeout behavior, or what happens with non-HTML content.
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 well-structured with clear sections (Function/Features, Args, Examples) and front-loads the core purpose. Every sentence adds value: the opening statement establishes purpose, the features section clarifies scope, the args section provides parameter context, and the examples demonstrate practical usage. There's no wasted text or redundancy.
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?
Given that an output schema exists (so return values don't need explanation in the description), the description provides good coverage of the tool's functionality. It explains what the tool does, documents the parameters meaningfully, and includes helpful examples. The main gap is the lack of behavioral context around error conditions, performance characteristics, or limitations that would be important for a web fetching tool.
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?
With 100% schema description coverage, the schema already documents both parameters thoroughly. The description adds meaningful context by explaining the semantic differences between the four 'return_content' options with clear definitions of what each format does, which goes beyond the enum values listed in the schema. This helps the agent understand when to choose each processing option.
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 specific action ('Fetch web page content') and resource ('from any HTTP/HTTPS URL'), making the purpose immediately apparent. It distinguishes itself from sibling tools like 'fetch_to_file' and 'http_request' by focusing specifically on retrieving and processing web content rather than saving to files or making general HTTP requests.
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 provides clear context about when to use this tool (retrieving web page content from URLs) and includes examples that demonstrate different use cases. However, it doesn't explicitly state when NOT to use this tool or provide direct comparisons with sibling alternatives like 'fetch_to_file' or 'http_request'.
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 provided, the description carries full burden and does so well. It discloses key behavioral traits: automatic directory creation, UTF-8 encoding, absolute path requirement, and content processing options. It doesn't mention error handling, rate limits, or authentication needs, but covers essential operational behavior adequately.
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 well-structured with clear sections (Function/Features, Notes, Args, Examples) and front-loaded with the core purpose. It's appropriately sized but could be slightly more concise by integrating some notes into the Args section. Every sentence adds value, though the formatting is slightly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, file operations), no annotations, but with a rich output schema (implied by 'Has output schema: true'), the description is complete. It covers purpose, usage, parameters, and examples thoroughly. The output schema handles return values, so the description appropriately focuses on input and behavior.
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 description coverage is 100%, so the baseline is 3. The description adds significant value by explaining the 'return_content' enum options in detail with practical semantics (e.g., 'removes non-displaying tags', 'converts to clean Markdown'), which goes beyond the schema's basic enum listing. It also emphasizes the 'absolute path' requirement for 'file_path'.
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 ('fetch web content', 'save it to a file') and resources ('any HTTP/HTTPS URL', 'workspace'). It distinguishes from sibling tools by emphasizing the file-saving aspect, unlike 'fetch' which might return content directly or 'http_request' which is more general.
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 implies usage context through examples and notes (e.g., 'absolute path', 'automatic directory creation'), but does not explicitly state when to use this tool versus alternatives like 'fetch' or 'http_request'. It provides clear operational guidance but lacks comparative decision-making advice.
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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: automatic URL-encoding of query parameters, automatic Content-Type setting for JSON, and the structure of the complete HTTP response (status, headers, body). However, it lacks details on error handling, timeouts, or authentication requirements, which are important for a general HTTP tool.
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 well-structured with clear sections (Function/Features, Notes, Args, Examples) and front-loads key information. However, it is somewhat lengthy due to extensive examples, which, while helpful, could be more concise. Most sentences earn their place by providing essential guidance, but some redundancy exists in explaining response details.
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?
For a complex tool with 6 parameters, low schema coverage, and no annotations, the description does a strong job of covering usage, parameters, and behaviors. The presence of an output schema means return values don't need explanation, but the description still clarifies response structure. Minor gaps remain in error handling and advanced HTTP features, but overall it's nearly complete for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given the low schema description coverage (33%), the description compensates excellently by adding detailed semantics for all parameters. It explains the purpose of 'url', 'method' with default, 'query' with encoding behavior, 'headers', and the critical distinction between 'data' and 'json' with Content-Type implications. The examples further clarify usage, adding significant value beyond the minimal schema descriptions.
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 as 'Execute an HTTP request with the specified method,' which is a specific verb+resource combination. It distinguishes itself from sibling tools 'fetch' and 'fetch_to_file' by emphasizing its general-purpose nature supporting multiple HTTP methods, custom headers, and complete response handling, unlike more specialized fetch tools.
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 provides explicit guidance on when to use specific parameters, such as the mutual exclusivity of 'data' and 'json' and when to set Content-Type headers manually. While it doesn't directly compare to sibling tools, the detailed parameter usage rules serve as clear alternatives within the tool itself, helping the agent choose between different request configurations.
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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