searxng-mcp

by TadMSTR

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v3.18.0
Disambiguation5/5
Each tool has a distinct purpose: clearing cache, crawling a site, querying domain stats, fetching a single URL, searching, searching+fetching, and searching+summarizing. No two tools are easily confused.
Naming Consistency5/5
All tool names follow a consistent snake_case verb_noun pattern (e.g., crawl_site, fetch_url, search_and_fetch). No mixing of conventions.
Tool Count5/5
With 7 tools, the server is well-scoped for a search/crawl service. Each tool provides a necessary function, and the count is neither too few nor too many.
Completeness5/5
The tool set covers the full workflow: searching, fetching, crawling, cache management, domain statistics, and even search+fetch and search+summarize combinations. There are no obvious gaps.
Average 4.3/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 36 commits in the last 12 weeks
- Last stable release on August 19, 2026
- 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.
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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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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?
With no annotations, the description correctly indicates a destructive action ('purge') and specifies the cache targets. However, it lacks details on rate limits, auth requirements, or side effects of clearing different caches.
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?
Two sentences, both relevant. No fluff. Slightly more structured formatting (e.g., bullet points) could improve, but it's already efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 param, no output schema), the description adequately covers purpose and typical scenario. Could mention that clearing cache is immediate or note persistence, but not necessary.
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 the parameter 'target' is fully described in the schema with enum and default. The description adds no additional meaning beyond the schema.
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?
Clearly states the tool purges search and/or fetch result cache, distinguishing it from sibling tools that perform searches, fetches, or crawls. The verb 'purge' and specific resources are explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a specific use case: 'researching fast-moving topics where cached results from the past hour may be stale.' Does not explicitly mention when not to use, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description carries full burden. Discloses caching behavior and crawl strategy (Firecrawl, sitemap-first, BFS). Could mention rate limits or error handling, but sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with core purpose, each sentence adds value. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers caching, strategy, and relationship to fetch_url. Lacks detailed return structure of manifest, but adequate for a tool with no output schema and good parameter descriptions.
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%, baseline 3. Description adds no extra meaning beyond parameter names and default values in schema.
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?
Description clearly states 'Crawl a site and return a manifest of pages' (specific verb+resource+output). Distinguishes from sibling fetch_url by noting it caches full page content for later retrieval.
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?
Implied guidance: use fetch_url after crawling for full text. No explicit when-to-use or when-not-to-use compared to search or other siblings. Lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses caching, blocked domain filtering, a fetch cascade (Firecrawl → Crawl4AI → raw HTTP), and GitHub API handling. It also mentions reranking. It does not mention rate limits, auth needs, or side effects, but given no structural annotations, coverage is strong.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-front-loaded paragraph of about 80 words. It states the core action first, then details special cases (GitHub, caching, blocked domains, fetch cascade). Every sentence adds value with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters, no output schema, and moderate complexity, the description covers the process (search, rerank, fetch), caching, domain filtering, and special handling. It also states the return format. It could elaborate on reranking or result list structure, but overall it is reasonably complete for agent invocation.
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?
Schema coverage is 100%, so baseline is 3. The description adds significant value beyond schemas: explains 'site' as best-effort with site: operator, 'expand' as local LLM variants with ~3s latency, 'engines' as verbatim with degraded fallback, and 'domain_profile' as named filters (homelab, dev). This extra context elevates the score.
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's purpose: 'Search the web, rerank results, then fetch the full content of the top result(s).' This verb+resource combination is specific and distinguishes it from siblings like 'search' (plain search) and 'fetch_url' (single URL fetch). The mention of GitHub URL special handling further clarifies its scope.
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 implies usage for combined search and fetch, but does not explicitly state when to use this tool over alternatives like 'search' followed by 'fetch_url' or 'search_and_summarize'. No exclusions or prerequisites are provided, so usage guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It details the fetch cascade, token budget, caching (24 hours), and refusal of blocked/internal domains. It does not mention rate limits or error handling, but the main behaviors are well covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the primary action. The second sentence provides essential details without unnecessary words. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains return is clean markdown and trimmed to token budget. It covers caching and refusal behavior. It could mention return format more explicitly but is adequate for a fetch tool with moderate complexity.
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?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining the default token budget (2000 tokens / 8000 chars) and how max_tokens relates to characters. It also clarifies behavior for domain_profile, target_selector, and wait_for_selector, which goes beyond the schema descriptions.
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 fetches and extracts readable content from any URL, with specific handling for GitHub URLs and a cascade of backends. It distinguishes itself from siblings like crawl_site (multi-page) and search (search) by focusing on single URL fetch and extraction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states what the tool does and provides context for when to use it (fetching any URL). It mentions blocked domains and private addresses are refused, giving a clear exclusion criterion. However, it does not explicitly contrast with siblings like crawl_site or search_and_fetch, which could be used for similar tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the use of a local LLM (qwen3:14b), fallback to raw content if Ollama is unavailable, and a structured answer with citations. Omits details like rate limits or external request behavior, but covers key aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences that front-load the core action and fallback, then mention return format and usage guidance. No wasted words; each sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters with 100% coverage and no output schema, the description explains the workflow (search, rerank, fetch, synthesize), fallback, and output structure. Lacks detail on 'rerank' and exact output schema, but provides enough for competent use.
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?
Schema description coverage is 100%, so baseline 3. The description adds useful context beyond schema: site restriction is 'best-effort', engines 'forwarded verbatim with graceful degradation', fetch_count defaults and max, etc. Enhances parameter understanding.
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 explicitly states the tool's purpose: search, rerank, fetch top results, and synthesize a summary using a local LLM. It distinguishes from siblings like 'search' (raw results) and 'search_and_fetch' (fetch without synthesis) by emphasizing the synthesis and citation component.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly advises use for 'deep research where you want pre-digested synthesis rather than raw pages,' implying a contrast with sibling tools. It does not explicitly list when not to use, but the guidance is sufficient for appropriate selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description fully discloses read-only nature, internal scan cap, and truncated flag. Gives confidence about non-destructive operation and limitations.
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 clear separation of single vs. aggregate behavior. Slightly verbose but every sentence adds information; no fluff.
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?
With one optional parameter and an output schema, the description explains return fields and truncation behavior completely. No gaps for agent to navigate.
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?
Schema coverage is 100%, description adds value by clarifying that omitting hostname triggers aggregate mode and providing an example. Baseline 3, extra context earns 4.
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?
Clear verb ('Read') and specific resource ('domain capability database') with detailed output fields. Distinguishes from sibling tools (e.g., fetch_url, crawl_site) as a read-only statistics tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit instructions for single-domain vs. aggregate usage, including the behavior when hostname is omitted. Lacks explicit exclusion of when not to use, but context from siblings makes purpose clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavioral traits: uses SearXNG, reranking with an ML model, 1-hour cache, filtered blocked/boosted domains, surface of native features, and expand option latency. This is comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured paragraph of 5 sentences. It is front-loaded with the core purpose and every sentence provides essential information without redundancy.
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?
Given 9 parameters and an output schema (not shown), the description covers the process, caching, reranking, domain features, and optional behaviors. It is complete for an agent to select and invoke the tool correctly.
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?
Schema coverage is 100% with decent descriptions, so baseline is 3. The description adds value by explaining the expand option's latency impact and domain filter behavior, going beyond the schema.
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 verb 'search' and resource 'web via local SearXNG instance with reranking', and explicitly distinguishes from the built-in WebSearch tool. It is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises preferring this over the built-in WebSearch tool, implying its primary use case. However, it does not explicitly state when not to use it or contrast with other sibling tools like crawl_site or fetch_url, leaving some ambiguity.
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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