SearXNG Server
Server Quality Checklist
Latest release: v1.0.4
- Disambiguation5/5
The two tools have completely distinct purposes: one performs web searches via SearXNG, the other reads content from a specific URL. There is no overlap or ambiguity between them.
Naming Consistency5/5Both tool names follow a consistent pattern: [prefix]_[object]_[verb]. They use snake_case and the verb is the last component ('search' and 'read'). The naming is predictable and clear.
Tool Count3/5With only 2 tools, the server is minimal but reasonable for a focused web search utility. It covers the essential actions of searching and reading results, though a few more tools (e.g., for settings) could be expected.
Completeness4/5The server provides a complete basic workflow: search the web and then read the content of result URLs. No obvious gaps for a simple search-and-retrieve use case, though advanced search features (e.g., image search) are missing.
Average 3.9/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 16 of 20 issues responded to in the last 6 months
- No commit activity data available
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and openWorldHint, but the description adds no extra behavioral context (e.g., error handling, timeouts, content format). It does not contradict annotations.
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?
Two sentences with front-loaded purpose. The second sentence is slightly redundant ('for further information retrieving') but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 6 optional parameters and no output schema, the description is too brief. It does not describe the return format, how optional parameters affect behavior, or what constitutes a successful read. Incomplete for a complex tool.
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?
Schema description coverage is 100%, so the description adds no additional meaning beyond the schema. It does not explain parameter interplay or provide usage examples.
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 clearly states 'Read the content from an URL' (specific verb+resource) and distinguishes from sibling searxng_web_search by framing this as for 'further information retrieving' after search.
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 implies usage after search by saying 'Use this for further information retrieving'. It provides clear context against the sibling tool, but lacks explicit exclusions or when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds no behavioral traits beyond what annotations already provide (readOnlyHint and openWorldHint). It correctly identifies that the tool searches the web, but does not elaborate on behavior like result format or pagination. Annotations already cover safety.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the purpose and then a critical note. Every sentence serves a purpose, with no fluff or repetition.
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?
Given no output schema, a brief description of return values could be helpful but is not strictly necessary. The tool is straightforward, and the description covers the essential invocation detail. It is complete enough for selection and use.
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?
Schema description coverage is 100%, so the schema already documents all parameters. The description only reinforces the exact parameter name 'query', which adds emphasis but no new semantic meaning. Baseline score of 3 is appropriate.
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 clearly states it 'Searches the web using SearXNG', which is a specific verb and resource. It distinguishes from the sibling 'web_url_read' which reads a specific URL, making the purpose unambiguous.
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 provides a critical usage guideline about the exact parameter name, which is important for correct invocation. However, it does not explicitly state when to use this tool versus the sibling, though the purpose implies it for general web search.
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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