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applex250

websearch-deepseek

by applex250
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TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.1

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion between overlapping purposes. The tool's purpose is clear and unambiguous.

    Naming Consistency5/5

    The single tool name 'web_search' follows a clear verb_noun pattern, which is consistent and descriptive. No naming conflicts or mixed conventions exist.

    Tool Count5/5

    One tool is perfectly scoped for a web search server; additional tools would be redundant. The tool fully embodies the server's purpose without unnecessary bloat or thinness.

    Completeness5/5

    For a web search domain, the tool covers the complete expected functionality: fetching current, real-time, and factual information with sources. There are no obvious gaps or missing operations.

  • Average 4.4/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How to sync the server with GitHub?

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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

  • Behavior4/5

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

    With no annotations provided, the description carries the burden. It discloses that the tool returns a 'detailed AI-generated answer based on full page content, plus source URLs,' and that it is 'powered by DeepSeek's native web search API (server-side execution).' This adequately sets expectations, though it omits potential limitations like result filtering or rate limits.

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

    Conciseness4/5

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

    The description is concise in each language version, with the English text using four short sentences. Bilingual duplication doubles length, but it's necessary for multilingual support. Each sentence contributes meaningful information.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a simple 2-parameter tool with no output schema, the description covers what it does, when to use it, what it returns, and its backend. This is fully sufficient for an agent to invoke it correctly.

    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 coverage is 100%, and both parameters ('query' and 'explanation') have clear descriptions. The tool description itself adds no extra parameter-specific detail beyond the schema, so the baseline of 3 is appropriate.

    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 performs web searches for current, real-time, or factual information. The verb 'search' and resource 'the web' are explicit, and it distinguishes itself from knowledge within the training cutoff, making its purpose unmistakable.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    It provides explicit 'when to use' guidance: 'when you need information beyond your training cutoff — recent events, current data, documentation lookups, or fact-checking.' This implies the inverse (don't use for well-known static facts) and clearly frames the tool's role.

    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.

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