Skip to main content
Glama
zhsama

DuckDuckGo MCP Server

by zhsama

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as performing web searches using DuckDuckGo.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'duckduckgo_web_search' follows a clear verb_noun pattern that would be appropriate if more tools were added.

    Tool Count2/5

    A single tool for a web search server is too minimal for the apparent scope. While the tool covers basic search functionality, a more complete surface would likely include additional tools such as for image search, news search, or advanced query options, making this feel thin and incomplete.

    Completeness2/5

    The tool surface is severely incomplete for a DuckDuckGo server. It only provides general web search, missing obvious functionalities like image search, video search, instant answers, or autocomplete, which are core features of DuckDuckGo. This will limit agents to basic queries without specialized capabilities.

  • Average 3.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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.

  • Tools from this server were used 4 times in the last 30 days.

  • 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

  • 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 adds useful context like 'maximum 20 results per request' and mentions content filtering and region-specific searches, but does not cover aspects like rate limits, authentication needs, or error handling, leaving gaps in behavioral understanding.

    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 appropriately sized with three sentences that efficiently cover purpose, usage, and constraints. It is front-loaded with the core function and avoids unnecessary repetition, though minor improvements in flow could elevate it to a 5.

    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 moderate complexity (3 parameters, no output schema, no annotations), the description provides a basic overview but lacks details on return values, error cases, or advanced usage. It is adequate for a simple search tool but incomplete for robust agent interaction.

    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 fully documents all parameters. The description does not add any parameter-specific details beyond what the schema provides, such as explaining the impact of 'safeSearch' levels or query formatting. Baseline 3 is appropriate as the schema handles the heavy lifting.

    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 'performs a web search using DuckDuckGo' with specific use cases like 'general queries, news, articles, and online content.' It distinguishes itself as a general-purpose search tool, though without sibling tools to differentiate from, it cannot achieve a perfect 5.

    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 provides implied usage guidance with phrases like 'ideal for general queries' and 'use this for broad information gathering,' but lacks explicit when-not-to-use scenarios or alternatives. Without sibling tools, it cannot offer comparative guidance, limiting its score.

    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

duckduckgo-mcp-server MCP server

Copy to your README.md:

Score Badge

duckduckgo-mcp-server 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/zhsama/duckduckgo-mcp-server'

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