Skip to main content
Glama
sunwu51

ORZ MCP

by sunwu51
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: web_fetch retrieves and processes the content of a specific URL, while web_search queries multiple search engines to find relevant web pages. There is no overlap in functionality, making it easy for an agent to choose the right tool for the task.

    Naming Consistency5/5

    Both tools follow a consistent 'web_' prefix and snake_case naming pattern (web_fetch and web_search). The naming is predictable and aligns with their web-related functions, providing clear and uniform conventions.

    Tool Count2/5

    With only two tools, the server feels thin for a web-related domain. While the tools cover fetching and searching, there are obvious gaps such as navigation, form handling, or API interactions that could enhance completeness, making the count insufficient for robust web operations.

    Completeness2/5

    The tool set is severely incomplete for web-related tasks. It lacks essential operations like navigating between pages, handling cookies or sessions, interacting with forms, or accessing APIs. This will likely cause agent failures when trying to perform common web automation or data extraction workflows.

  • Average 4.3/5 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • 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 and does well by disclosing key behavioral traits: it uses multiple search engines (Brave, Sogou, DuckDuckGo), performs deduplication, filters out ads, and describes the return format. However, it doesn't mention rate limits, authentication needs, or error handling.

    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 and front-loaded: two sentences that efficiently cover purpose, implementation details, and output without any wasted words. Every sentence adds essential value to the tool's 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, no annotations, and no output schema, the description is mostly complete—it covers what the tool does, how it works, and what it returns. However, it lacks details on error cases, rate limits, or example usage, which would enhance completeness for a search tool.

    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 100%, so the input schema already fully documents both parameters ('query' and 'num_results'). The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced coverage.

    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 ('Search the web') with precise details on implementation ('using multiple search engines simultaneously'), resource scope ('the web'), and output format ('array of search results'). It distinguishes from the sibling tool 'web_fetch' by focusing on search rather than fetching specific URLs.

    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 (web searching with deduplication and ad filtering), but it doesn't explicitly state when not to use it or mention the sibling tool 'web_fetch' as an alternative for specific use cases. The guidance is implicit rather than explicit.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden and discloses key behavioral traits: it describes the transformation behavior (removing useless HTML tags, converting to Markdown when simplify is enabled), timeout constraint (10 seconds), and default settings (simplify enabled by default). This provides essential operational context beyond basic functionality.

    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 states the core purpose, and subsequent sentences efficiently add crucial behavioral details (simplification behavior, timeout). Every sentence earns its place with no wasted words or redundancy.

    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 (3 parameters, no output schema, no annotations), the description is largely complete: it covers purpose, key behavior, and constraints. However, it doesn't describe the return format (e.g., structure of the content) or error handling, which would be helpful given the lack of output schema.

    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 thoroughly. The description adds minimal value beyond the schema by mentioning the simplify parameter's effect, but doesn't provide additional syntax, format details, or usage examples. This meets the baseline for high schema coverage.

    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 ('fetch a web page', 'return its content') and distinguishes it from sibling tools by focusing on direct URL fetching rather than searching. It specifies the resource (web page) and transformation behavior (simplification to Markdown).

    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 (fetching web pages with optional simplification) but does not explicitly mention when not to use it or name alternatives. It implies usage for direct URL access rather than search-based retrieval, though it doesn't contrast with the sibling 'web_search' tool by name.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

orz-mcp MCP server

Copy to your README.md:

Score Badge

orz-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sunwu51/orz-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server