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LangGPT

Context MCP Server

by LangGPT
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation2/5

    The two tools have significant functional overlap. Both fetch URLs from the internet and handle content extraction, with fetch_and_save essentially being an enhanced version of fetch that adds file saving functionality. An agent would struggle to determine when to use fetch versus fetch_and_save since the core fetching behavior is duplicated.

    Naming Consistency4/5

    The naming follows a consistent snake_case pattern with clear verb-action structure. Both tools start with 'fetch' as the primary action, and fetch_and_save accurately describes the additional functionality. The minor deviation is that fetch doesn't specify its optional markdown extraction in the name, but this is reasonable.

    Tool Count3/5

    With only 2 tools, the server feels thin for a 'Context MCP Server' which suggests broader contextual capabilities. While internet fetching is a valuable function, a server with this name might be expected to offer additional context-related operations beyond just URL fetching and saving.

    Completeness2/5

    For a server named 'Context MCP Server', the toolset is severely incomplete. There are no tools for managing, searching, or analyzing fetched content, no context storage or retrieval mechanisms, and no integration with other context sources. The server essentially provides only basic URL fetching with file saving as an afterthought.

  • 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
    • 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.

  • 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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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 full burden. It discloses that the tool grants internet access and can fetch current information, which is valuable behavioral context. However, it doesn't mention rate limits, authentication needs, error handling, or what happens with truncated content beyond the parameters.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is appropriately sized but not optimally structured. The first sentence efficiently states the core functionality. However, the second paragraph contains historical context that could be condensed or omitted, making it less front-loaded than ideal.

    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 4 parameters with 100% schema coverage but no annotations or output schema, the description provides adequate context about the tool's internet access capability and general purpose. However, for a tool with potential complexity around web fetching, it lacks details about response format, error cases, or performance characteristics that would make it more complete.

    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?

    Schema description coverage is 100%, providing solid baseline documentation for all 4 parameters. The description adds minimal parameter semantics by mentioning 'optionally extracts its contents as markdown' which relates to the 'raw' parameter, but doesn't provide additional meaning beyond what the schema already documents.

    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's purpose with specific verbs ('fetches', 'extracts') and resources ('URL from the internet', 'contents as markdown'). It distinguishes from sibling tool 'fetch_and_save' by focusing on retrieval and optional extraction rather than saving.

    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 clear context about when to use this tool (to get up-to-date information from the internet) and mentions the historical limitation of no internet access. However, it doesn't explicitly state when NOT to use it or provide specific alternatives to 'fetch_and_save' beyond the general purpose difference.

    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: the two-step fetch process (Jina Reader API with fallback to standard fetch), file saving location (configured working directory), and automatic filename generation. However, it does not mention error handling, rate limits, authentication needs, or file format details, leaving some behavioral aspects uncovered.

    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 appropriately sized and front-loaded: the first sentence clearly states the core functionality, followed by details on the fetch process and file handling. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

    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's moderate complexity (fetching and saving with fallback logic), no annotations, and no output schema, the description does a good job covering the main operations and parameters. However, it lacks details on error responses, output format, or potential side effects, which would enhance completeness for a tool with mutation (file creation) and external dependencies.

    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?

    Schema description coverage is 100%, so the schema already documents all parameters. The description adds marginal value by explaining the fallback mechanism and auto-generation of filenames, but does not provide additional semantic details beyond what the schema specifies for 'url', 'file_path', or 'raw'. The baseline score of 3 is appropriate as the schema does the heavy lifting.

    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's purpose: 'Fetches a URL from the internet... and saves the content to a file.' It specifies the verb (fetch and save), resource (URL content), and distinguishes from the sibling 'fetch' tool by explicitly mentioning the saving functionality. The description is specific and avoids tautology.

    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 clear context for when to use this tool: for fetching URLs and saving content to files. It implicitly contrasts with the sibling 'fetch' tool by emphasizing the saving aspect, but does not explicitly state when to choose one over the other or mention any exclusions. The guidance is useful but lacks explicit alternatives or when-not-to-use details.

    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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