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Visit https://brave.com/search/api/ for a free API key. Search the web, local businesses, images,…

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Healthy
Last Tested
Transport
Streamable HTTP
URL
Repository
brave/brave-search-mcp-server
GitHub Stars
1,249
Server Listing
Brave Search MCP Server

Glama MCP Gateway

Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.

MCP client
Glama
MCP server

Full call logging

Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.

Tool access control

Enable or disable individual tools per connector, so you decide what your agents can and cannot do.

Managed credentials

Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.

Usage analytics

See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.

100% free. Your data is private.
Tool DescriptionsA

Average 4.1/5 across 8 of 8 tools scored. Lowest: 3.1/5.

Server CoherenceA
Disambiguation5/5

Each tool targets a distinct search modality (web, image, video, news, local, places) or processing step (summarizer, LLM context). Descriptions clearly differentiate purposes and chaining dependencies are explicitly noted.

Naming Consistency5/5

All tool names follow a consistent `brave_<descriptive_suffix>` pattern using snake_case. The suffixes are specific to the operation (e.g., `image_search`, `llm_context`, `local_search`), making the purpose clear and predictably structured.

Tool Count5/5

With 8 tools, the count is well-scoped for a search API covering multiple media types and AI enhancements. Each tool serves a distinct purpose without redundancy or unnecessary expansion.

Completeness4/5

The set covers all major search modalities and adds AI-oriented tools (summarizer, LLM context). Minor gaps exist (e.g., no direct location-to-POI search without a two-step flow), but the documented chaining addresses these adequately.

Available Tools

8 tools
brave_llm_contextLLM context retrieval (RAG)A
Read-only
Inspect

Retrieves pre-extracted, relevance-ranked web content using Brave's LLM Context API, optimized for AI agents, LLM grounding, and RAG pipelines. Unlike a traditional web search that returns links and short descriptions, this tool returns the actual substance of matching pages — text chunks, tables, code blocks, and structured data — so the model can reason over it directly. When relaying results in markdown-supporting environments, cite source URLs from the sources map.

ParametersJSON Schema
NameRequiredDescriptionDefault
countNoMaximum number of search results considered to select the LLM context data. Default 20, max 50.
queryYesThe user's search query. Max 400 characters and 50 words.
countryNo2-letter country code (ISO 3166-1 alpha-2). Defaults to US.
freshnessNoFilter results by recency. Use pd, pw, pm, py, or YYYY-MM-DDtoYYYY-MM-DD.
spellcheckNoWhether to spellcheck the query.
search_langNo2-letter language code for the search. Defaults to en.
enable_localNoWhether to enable local recall.
context_threshold_modeNoMode used to determine the inclusion threshold for content.
enable_source_metadataNoEnable source metadata enrichment (site_name, favicon) in the sources attribute.
maximum_number_of_urlsNoMaximum number of different URLs to include in LLM context.
maximum_number_of_tokensNoApproximate maximum number of tokens to include in context. Default 8192, max 32768.
maximum_number_of_snippetsNoMaximum number of snippets (chunks of text) to include in LLM context. Default 50, max 256.
maximum_number_of_tokens_per_urlNoMaximum number of tokens to include per URL. Default 4096, max 8192.
maximum_number_of_snippets_per_urlNoMaximum number of snippets to include per URL. Default 50, max 100.
Behavior4/5

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

Annotations already declare readOnlyHint=true. Description adds that it returns text chunks, tables, code blocks, and a sources map, providing useful behavioral context beyond annotations.

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 concise sentences, front-loaded with core functionality, each sentence adds distinct value (purpose, differentiation, usage guidance). No waste.

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 high schema coverage and read-only annotations, the description adequately covers tool behavior. Could mention response structure more, but sufficient for a RAG tool.

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 description coverage is 100%, so parameter semantics are well-documented in schema. Description does not add extra parameter-specific meaning beyond schema, earning baseline 3.

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 it retrieves pre-extracted, relevance-ranked web content using Brave's LLM Context API, optimized for AI agents and RAG, distinguishing it from traditional web search by returning actual content chunks.

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?

Explicitly contrasts with traditional web search, indicating when to use this tool over siblings like brave_web_search. Provides citation guidance but lacks explicit when-not usage instructions.

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

brave_summarizerAI-generated summaryA
Read-only
Inspect

Retrieves AI-generated summaries of web search results. Two-step flow: first call brave_web_search with summary=true to obtain summarizer.key, then pass it here. Pro AI tier required.

ParametersJSON Schema
NameRequiredDescriptionDefault
keyYesSummarizer key returned by a prior `brave_web_search` with `summary=true`.
entity_infoNoWhether to include extra entity-info fields with citation metadata.
Behavior5/5

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

Annotations indicate readOnlyHint=true and destructiveHint=false; description adds operational context (two-step flow, tier requirement) without contradicting annotations.

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, front-loaded with purpose, each sentence adds value with no fluff.

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?

No output schema exists; description lacks details about the summary output format. However, parameters are fully covered and operational flow is clear.

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%, so baseline is 3. Description mentions 'key' in context but adds no new semantic meaning beyond what the schema provides for either parameter.

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 'Retrieves AI-generated summaries of web search results' and distinguishes from sibling tools by explaining the two-step flow involving brave_web_search.

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

Usage Guidelines5/5

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

Explicitly describes the two-step flow and prerequisite ('first call brave_web_search with summary=true'), and states 'Pro AI tier required', providing clear usage guidance.

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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