Which integrations are available for this server?

Provides web search capabilities through the Brave Search API, including web search with filtering options (safe search, country, freshness), local POI lookups, and rich result fetching.

How do I use Search MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Search MCP find recent articles about AI advancements in healthcare" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Search MCP

by nanameru

Overview Schema Related Servers Score Discussions

TypeScript

Remote

Search MCP

The Universal MCP Server exposes tools for your workflows and is designed for prompt-first usage in MCP-compatible clients.

Installation

Prerequisites

Node.js 18+
Set SEARCH_MCP_... in your environment

Get an API key

If your tools require an external API, obtain a key from the provider’s docs/console.
Otherwise, you can skip this step.

Build locally

cd /path/to/search-mcp
npm i
npm run build

Related MCP server: Brave Search MCP

Setup: Claude Code (CLI)

Use this one-liner (replace with your real values):

claude mcp add URL-Context-MCP -s user -e SEARCH_MCP_API_KEY="sk-your-real-key" -- npx @taiyokimura/url-context-mcp@latest

To remove:

claude mcp remove Search MCP

Setup: Cursor

Create .cursor/mcp.json in your client (do not commit it here):

{
  "mcpServers": {
    "url-context-mcp": {
      "command": "npx",
      "args": ["@taiyokimura/url-context-mcp@latest"],
      "env": { "SEARCH_MCP_API_KEY": "sk-your-real-key" },
      "autoStart": true
    }
  }
}

Other Clients and Agents

Install via URI or CLI:

code --add-mcp '{"name":"url-context-mcp","command":"npx","args":["@taiyokimura/url-context-mcp@latest"],"env":{"SEARCH_MCP_API_KEY":"sk-your-real-key"}}'

Follow the MCP install guide and reuse the standard config above.

Command: npx
Args: ["@taiyokimura/url-context-mcp@latest"]
Env: SEARCH_MCP_API_KEY=sk-your-real-key

Type: STDIO
Command: npx
Args: @taiyokimura/url-context-mcp@latest
Enabled: true

Example ~/.config/opencode/opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "url-context-mcp": {
      "type": "local",
      "command": ["npx", "@taiyokimura/url-context-mcp@latest"],
      "enabled": true
    }
  }
}

Add a new MCP and paste the standard JSON config.

See docs and reuse the standard config above.

Setup: Codex (TOML)

Example (Serena):

[mcp_servers.serena]
command = "uvx"
args = ["--from", "git+https://github.com/oraios/serena", "serena", "start-mcp-server", "--context", "codex"]

This server (minimal):

[mcp_servers.url-context-mcp]
command = "npx"
args = ["@taiyokimura/url-context-mcp@latest"]
# Optional:
# SEARCH_MCP_API_KEY = "sk-your-real-key"
# MCP_NAME = "url-context-mcp"

Configuration (Env)

SEARCH_MCP_API_KEY: Your API key (if applicable)
MCP_NAME: Server name override (default: url-context-mcp)

Available Tools

web_search
- inputs: object { query: string (required), count?: number, offset?: number, safeSearch?: 'off'|'moderate'|'strict', country?: string, freshness?: 'pd'|'pw'|'pm'|'py', enableRichCallback?: boolean }
- outputs: object (Brave Web Search API JSON)
local_pois
- inputs: object { ids: string[] (1-20) }
- outputs: object (Local POI API JSON)
local_descriptions
- inputs: object { ids: string[] (1-20) }
- outputs: object (Local descriptions API JSON)
rich_fetch
- inputs: object { callback_key: string }
- outputs: object (Rich results JSON)

Example invocation (MCP tool call)

{
  "tool": "web_search",
  "inputs": {
    "query": "weather in munich",
    "enableRichCallback": true
  }
}

Troubleshooting

401 auth errors: check SEARCH_MCP_API_KEY
Ensure Node 18+
Local runs: npx search-mcp after npm run build
Inspect publish artifacts: npm pack --dry-run

References

MCP SDK: https://modelcontextprotocol.io/docs/sdks
Architecture: https://modelcontextprotocol.io/docs/learn/architecture
Server Concepts: https://modelcontextprotocol.io/docs/learn/server-concepts
Specification: https://modelcontextprotocol.io/specification/2025-06-18/server/index
Brave Search API: https://api.search.brave.com/app/documentation

Name Consistency & Troubleshooting

Always use CANONICAL_ID (url-context-mcp) for identifiers and keys.
Use CANONICAL_DISPLAY (URL-Context MCP) only for UI labels.
Do not mix legacy keys after registration.

Consistency Matrix:

npm package name → @taiyokimura/url-context-mcp
Binary name → url-context-mcp
MCP server name (SDK metadata) → url-context-mcp
Env default MCP_NAME → url-context-mcp
Client registry key → url-context-mcp
UI label → URL-Context MCP

Conflict Cleanup:

Remove any stale keys (e.g., old display names) and re-add with url-context-mcp only.
Cursor: configure in the UI; this project intentionally omits .cursor/mcp.json.

Available Tools

4 tools

local_descriptionsC

Fetch AI-generated descriptions for locations using Brave Local Search API

ParametersJSON Schema

Name	Required	Description	Default
`ids`	Yes

TDQS

C2.8/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions fetching via an external API, which implies network usage and potential rate limits, but doesn't specify authentication needs, error handling, or what 'AI-generated' entails. It lacks details on response format, pagination, or data freshness.

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, efficient sentence with zero waste. It's front-loaded with the core purpose and includes the API source, making it easy to parse quickly without unnecessary elaboration.

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?

Given no annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't cover parameter semantics, behavioral traits like rate limits or errors, or return values. For a tool with external API dependencies and one required parameter, this leaves significant gaps for the agent.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It doesn't explain the 'ids' parameter—what these IDs represent, their format, or how to obtain them. The description adds no meaning beyond the bare schema, leaving the agent guessing about input requirements.

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?

The description clearly states the action ('Fetch AI-generated descriptions') and resource ('for locations'), specifying it uses the Brave Local Search API. It distinguishes from 'local_pois' (likely points of interest) and 'web_search' (general search) by focusing on location descriptions, but doesn't explicitly differentiate from 'rich_fetch' (which might be similar).

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?

No guidance is provided on when to use this tool versus alternatives like 'local_pois' or 'rich_fetch'. The description implies it's for location descriptions, but doesn't specify use cases, prerequisites, or exclusions, leaving the agent to infer context from tool names alone.

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

local_poisC

Fetch extra information for locations using Brave Local Search API

ParametersJSON Schema

Name	Required	Description	Default
`ids`	Yes

TDQS

C2.4/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the API source ('Brave Local Search API') but fails to describe key traits like whether it's read-only or mutative, rate limits, authentication needs, error handling, or what happens if invalid IDs are provided. This leaves significant gaps in understanding the tool's behavior.

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?

The description is a single, efficient sentence that front-loads the core action ('Fetch extra information for locations') and specifies the API. It avoids redundancy and waste, making it appropriately concise, though it could be more structured with additional details if needed for clarity.

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?

Given the tool's complexity (fetching location data via an external API), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't cover behavioral aspects, parameter details, or return values, making it inadequate for an agent to reliably use the tool without further context.

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

Parameters2/5

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

The input schema has 0% description coverage, with one parameter 'ids' of type array of strings. The description does not explain what these IDs represent (e.g., location identifiers, coordinates), their format, or constraints, failing to compensate for the lack of schema documentation and leaving the parameter's meaning unclear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'Fetch[es] extra information for locations' using a specific API, which provides a general purpose. However, it lacks specificity about what type of 'extra information' (e.g., reviews, hours, contact details) and doesn't clearly differentiate from sibling tools like 'local_descriptions' or 'rich_fetch', leaving ambiguity about its unique role.

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?

No explicit guidance is provided on when to use this tool versus alternatives such as 'local_descriptions' or 'web_search'. The description implies usage for location-related data but offers no context on prerequisites, exclusions, or comparative scenarios, leaving the agent to infer usage without clear direction.

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

rich_fetchC

Fetch rich results using the callback_key from web_search

ParametersJSON Schema

Name	Required	Description	Default
`callback_key`	Yes	callback_key from web_search.rich.hint.callback_key

TDQS

C2.6/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions fetching 'rich results' but doesn't disclose behavioral traits such as what the results include, whether it's a read-only operation, error handling, rate limits, or authentication needs. This leaves significant gaps in understanding the tool's behavior.

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?

The description is a single, efficient sentence that directly states the tool's function. It is appropriately sized and front-loaded, with no wasted words, though it could be slightly more informative without losing conciseness.

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?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'rich results' are, the return format, or any behavioral context needed for a tool with no structured data support, making it inadequate for proper agent usage.

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?

The schema description coverage is 100%, with the parameter callback_key fully documented in the schema. The description adds minimal value by referencing it comes from web_search.rich.hint.callback_key, but doesn't provide additional semantics beyond what the schema already states, aligning with the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool fetches rich results using a callback_key from web_search, which provides a general purpose. However, it lacks specificity about what 'rich results' entail and doesn't clearly distinguish this tool from its sibling web_search tool, making it somewhat vague.

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?

The description implies usage by referencing a callback_key from web_search, suggesting it should be used after web_search. However, it provides no explicit guidance on when to use this tool versus alternatives like web_search or other siblings, nor any prerequisites or exclusions.

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

web_searchC

Search the web using Brave Web Search API

ParametersJSON Schema

Name	Required	Description
`query`	Yes	Search query
`count`	No	Results count (1-20)
`offset`	No	Results offset
`safeSearch`	No
`country`	No
`freshness`	No
`enableRichCallback`	No	Include rich callback hint

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden but only mentions the API used ('Brave Web Search API'). It lacks details on behavioral traits such as rate limits, authentication needs, error handling, or what the search results include, which are critical for a web search tool.

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, efficient sentence with zero waste, clearly front-loaded with the core purpose. It's appropriately sized for a tool with multiple parameters, making it easy to parse quickly.

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?

Given the complexity of a web search tool with 7 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain return values, error cases, or important constraints like the 'count' range (1-20), leaving significant gaps for the agent.

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 57% (4 out of 7 parameters have descriptions), so the baseline is 3. The description adds no additional parameter semantics beyond the schema, such as explaining enum values (e.g., 'freshness' options) or default behaviors, but doesn't compensate for the coverage gap.

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?

The description clearly states the verb ('Search') and resource ('the web'), specifying it uses the 'Brave Web Search API'. However, it doesn't differentiate from sibling tools like 'local_descriptions' or 'local_pois', which might also search but in different contexts, so it's not a perfect 5.

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?

No guidance is provided on when to use this tool versus alternatives like 'local_descriptions' or 'rich_fetch'. The description only states what it does without context, prerequisites, or exclusions, leaving the agent to infer usage.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

4 tool updatesv1.0.0Jan 10, 2026
- First observedlocal_descriptions
- First observedlocal_pois
- First observedrich_fetch
- First observedweb_search

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3/5.0

Disambiguation3/5

The tools have some overlap in purpose, particularly between local_descriptions and local_pois, which both fetch location information using the same API and could be confused. However, web_search and rich_fetch are distinct in targeting general web search and rich results respectively, and the descriptions help clarify the differences.

Naming Consistency4/5

The naming is mostly consistent with a clear pattern of using descriptive terms like 'local', 'web', and 'rich', all in snake_case. The only minor deviation is that rich_fetch uses 'fetch' while others use more specific verbs like 'search' or 'descriptions', but overall the pattern is readable and predictable.

Tool Count5/5

With 4 tools, this server is well-scoped for a search-focused purpose, covering local descriptions, local points of interest, rich results, and general web search. Each tool earns its place without feeling excessive or insufficient for the domain.

Completeness4/5

The tool surface covers key search operations for both web and local domains, with minor gaps such as no explicit tool for image or news search, but agents can likely work around this using the provided tools. The inclusion of rich_fetch as a callback from web_search adds useful functionality.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

Need Help?

Reddit Discussion

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Search MCP

Installation

Prerequisites

Get an API key

Build locally

Setup: Claude Code (CLI)

Setup: Cursor

Other Clients and Agents

Setup: Codex (TOML)

Configuration (Env)

Available Tools

Example invocation (MCP tool call)

Troubleshooting

References

Name Consistency & Troubleshooting

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Brave Search MCP Serverofficial

Latest Blog Posts

MCP directory API