DuckDuckGo MCP Server
Server Quality Checklist
Latest release: v1.0.3
- Disambiguation5/5
With only a single tool, there is no possibility of ambiguity or misselection. The tool's purpose is clearly defined as fetching DuckDuckGo search results, making it trivially distinct.
Naming Consistency5/5The single tool name follows a consistent pattern using underscores and includes a clear verb (getSearchResults) and resource hierarchy (duckduckgo_serp). While not a classic verb_noun structure, it is internally consistent and descriptive within its own scope.
Tool Count3/5The server exposes only one tool, which is below the typical 3-15 tool range for a fully featured server. However, given the focused purpose of a DuckDuckGo SERP API, a single comprehensive endpoint is arguably sufficient, earning a borderline score.
Completeness4/5The tool covers the core search lifecycle: querying with pagination, region targeting, safesearch, and device options, plus returning organic results, ads, and AI answers. Minor gaps exist (e.g., no dedicated news or image search), but the primary search functionality is complete for typical use cases.
Average 4.1/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
Tools from this server were used 2 times in the last 30 days.
This repository includes a glama.json configuration file.
This server has been verified by its author.
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?
No annotations are provided, so the description carries the full disclosure burden. It discloses the nature of the operation ('Fetches ... SERPs'), the return payload (organic results, ads, Search Assist AI answer), and critical pagination behavior (nextPageToken is pre-signed, session-bound, must be used as-is, cannot be constructed). This goes beyond a basic GET. However, it does not mention rate limits, cost, or auth requirements, which are common for SERP APIs, so it is not a 5.
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 compact and well-structured. The first sentence nails the purpose, the next expands on functionality and return types, and the final sentence lists use cases. It is slightly longer than necessary but every sentence adds value. No fluff 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?
For a 7-parameter tool with no output schema and no annotations, the description provides enough for an agent to call it correctly: it explains the parameter combinations (kl vs cc+setLang, precedence), pagination mechanics, and what the response includes. It lacks error-handling details and rate-limit guidance, but these are not essential for a basic call. Completeness is above average.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with detailed descriptions, including the precedence rule ('Ignored if kl is set') and the required/exclusive nature of q vs nextPageToken. The description repeats some of this (region targeting with kl or cc+setLang, pagination) but does not add substantive new meaning beyond the schema. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Get DuckDuckGo Search Results' — a specific verb and resource — and then elaborates on fetching SERPs with region targeting, safesearch, device type, and pagination. It also lists the return content (organic results, ads, Search Assist AI) and explicit use cases (SEO rank tracking, SERP monitoring, visibility audits), making the tool's purpose unambiguous and differentiating it from generic search tools.
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 explicitly states when to use this tool: 'Use for SEO rank tracking, SERP feature monitoring, DuckDuckGo-specific visibility audits, and training/eval data for search agents.' It provides clear contexts but does not mention when NOT to use it or any alternative tools (though there are no siblings). This is adequate guidance for an agent.
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
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/HasData/duckduckgo-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server