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maccam912

searxng-mcp-server

by maccam912
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: get_available_engines retrieves metadata about search engines, while search performs actual searches. There is no overlap or ambiguity between these functions.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (get_available_engines and search). The naming is straightforward and predictable across the toolset.

    Tool Count2/5

    With only two tools, the server feels under-scoped for a search functionality domain. While the core search operation is present, typical search-related operations like managing preferences, saving searches, or advanced filtering are missing, making the toolset feel thin.

    Completeness2/5

    The toolset is severely incomplete for a search server. It lacks essential operations such as configuring search settings, handling search history, or integrating with other data sources. The surface is minimal and does not cover the expected lifecycle of search interactions.

  • Average 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

  • 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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  • 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

  • Behavior2/5

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

    No annotations provided, so description carries full burden. Fails to disclose what SearXNG aggregation entails, return value structure (despite output schema existing, behavioral traits like rate limits or latency are absent), or mutation effects.

    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?

    Well-structured with purpose front-loaded in the first line followed by clear Args section. No redundant prose. The Args list is necessary given zero schema coverage, so it earns its place effectively.

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

    Completeness2/5

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

    Despite 7 parameters and 0% schema coverage, description lacks crucial domain context. Misses the critical relationship to `get_available_engines` for valid engine names, doesn't explain SearXNG's meta-search nature, and omits behavioral constraints. Output schema presence excuses return value documentation, but operational context is insufficient.

    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?

    With 0% schema description coverage, the Args section compensates by documenting all 7 parameters. Adds valuable structural hints ('comma-separated', specific enum values for time_range/day|month|year and safe_search/0,1,2) not present in raw schema. However, lacks semantic detail on valid category strings or that engines must come from sibling tool.

    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?

    States specific verb (Search) and resource (SearXNG), clearly identifying this as a web search operation. Distinguishes implicitly from sibling `get_available_engines` (this performs search vs listing engines), though explicit differentiation would strengthen it further.

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

    Usage Guidelines2/5

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

    Provides no guidance on when to use versus alternatives, prerequisites, or when not to use. Critically misses the relationship to `get_available_engines` for populating the `engines` parameter, leaving agents to guess valid values.

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

  • Behavior2/5

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

    No annotations provided, so description carries full disclosure burden. While 'Get' implies read-only behavior, description omits details on caching, authentication requirements, whether results are user-specific or global, and what the output structure contains (though output schema exists).

    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?

    Single 6-word sentence with no redundancy. Efficiently front-loaded but arguably too minimal given lack of annotations and contextual guidelines that could have been included without sacrificing clarity.

    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?

    Adequate for a simple discovery tool with existing output schema (which handles return value documentation). However, misses opportunity to explain relationship to 'search' sibling or define what makes an engine 'available'. Minimal viable description.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Zero input parameters with 100% schema coverage (empty object). Per rubric, baseline score is 4 for zero-parameter tools since there are no semantics to clarify beyond the schema.

    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?

    States specific verb ('Get') and resource ('available search engines'), distinguishing it from sibling 'search' (which performs searches vs. this which discovers them). However, 'information' is vague regarding what specific data is returned (names, capabilities, endpoints?).

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

    Usage Guidelines2/5

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

    Provides no guidance on when to use this tool versus the 'search' sibling, nor prerequisites for calling it. Description states what it does but not why or when an agent should invoke it.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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