qsearch
Server Details
Multi-engine search for AI agents. Trust scoring, local corpus, MCP-native. Self-hostable, BYOK.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- theYahia/qsearch
- GitHub Stars
- 0
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Tool Definition Quality
Average 4.3/5 across 3 of 3 tools scored.
Each tool targets a distinct search domain: context_search for enriched depth, news_search for recent events, and web_search for general queries. There is no overlap in their purposes, making selection unambiguous.
All tools follow a consistent verb_noun pattern with snake_case (context_search, news_search, web_search), making the naming predictable and easy to navigate.
With only 3 tools, the set is modest but well-scoped for a search-focused server. It covers the primary search modalities without unnecessary duplicates, though a few more (e.g., image search) could enhance completeness.
The tool surface covers core search needs (web, news, deep context), but lacks features like filtering, pagination, or result customization. For a minimal set, it is functional but may leave users wanting more control.
Available Tools
3 toolscontext_searchContext Search (qsearch)ARead-onlyInspect
Retrieve enriched page content via Brave LLM Context API with local QVAC cleaning. Returns 2-28 text snippets per source (vs 1 snippet in web_search). Use when depth matters over breadth.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query for deep page content extraction | |
| n_results | No | Number of sources (1-2 max — each source has 2-28 snippets, all get cleaned. CPU-bound, ~25s/source. Default 1.) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, openWorldHint) are supplemented with details on local QVAC cleaning, snippet range, CPU-bound nature, and timing (~25s/source for n_results >1). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, efficient and no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description mentions return structure (snippets per source). Lacks explanation of QVAC cleaning, but overall adequate for a simple retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage, baseline 3. Description does not add new parameter info beyond schema, which already provides good semantics for both query and n_results (including default, max, cost).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves enriched page content via Brave LLM Context API with local cleaning, and distinguishes from sibling web_search by noting snippet count (2-28 vs 1).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises 'Use when depth matters over breadth' and contrasts snippet count with web_search, providing clear context for selection. No explicit when-not, but implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
news_searchNews Search (qsearch)ARead-onlyInspect
Search recent news via Brave News API with local QVAC LLM cleaning. Defaults to past week. Use for current events, market news, and time-sensitive queries.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | News search query | |
| freshness | No | Time filter: pd (past day), pw (past week, default), pm (past month) | pw |
| n_results | No | Number of results (1-3 max — cleaning is CPU-bound, each result adds ~25s. Default 1.) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and openWorldHint. The description adds 'local QVAC LLM cleaning' and default time range, providing some extra context beyond annotations, but no significant behavioral details like rate limits or data source specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no redundant words. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with good schema and annotations, the description adequately covers purpose, usage context, and primary behavior. Lacks mention of output format but is still sufficient for selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with complete parameter descriptions. The description does not add new parameter-level information; it only restates defaults already apparent from schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies 'Search recent news via Brave News API' with a clear verb and resource, and distinguishes from sibling tools (context_search, web_search) by focusing on news for current events, market news, and time-sensitive queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Directly states 'Use for current events, market news, and time-sensitive queries' providing clear when-to-use guidance. Does not explicitly exclude alternatives but context is sufficient given sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
web_searchWeb Search (qsearch)ARead-onlyInspect
Search the web via Brave Search API with local QVAC LLM cleaning. Returns cleaned markdown summaries. Use for general web research, factual lookups, and topic exploration.
| Name | Required | Description | Default |
|---|---|---|---|
| clean | No | Run on-device QVAC LLM cleaning over results (Qwen3-0.6B Q4). Adds ~25s per result on CPU; seconds on mobile with QVAC acceleration. Default false — returns raw Brave snippets (title, description, extra snippets) immediately. | |
| query | Yes | Search query | |
| country | No | Country code, e.g. "us", "ru" | |
| freshness | No | Time filter: pd (past day), pw (past week), pm (past month), py (past year), or YYYY-MM-DDtoYYYY-MM-DD | |
| n_results | No | Number of results (1-3). Default 2. | |
| search_lang | No | Language code, e.g. "en", "ru" |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond annotations, detailing the latency impact of the 'clean' parameter ('adds ~25s per result on CPU') and the difference between raw and cleaned output. Annotations already indicate read-only and open-world nature, and description complements without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tightly-packed sentences convey purpose, mechanism, and usage without superfluous words. No content is wasted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters and no output schema, the description covers the main purpose, performance trade-off, and usage. However, it lacks details on result formatting or pagination, which are partially in schema but not synthesized.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds no per-parameter meaning beyond what the schema provides, though it does link the output format to the 'clean' parameter indirectly via 'Returns cleaned markdown summaries'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search the web via Brave Search API with local QVAC LLM cleaning', specifying the verb and resource uniquely. It distinguishes from siblings by emphasizing the general web research scope, unlike context_search or news_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly recommends use for 'general web research, factual lookups, and topic exploration', providing clear context. However, it does not mention when not to use this tool or contrast with sibling tools.
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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