これはLSD用のMCPサーバーの更新版です。この更新の目的は、 SDKを使用してトリップとして定義された動的ツールを効果的に活用することです。
Related MCP server: superhighway-mcp
これは、弊社の SDKを使用して、実行中の MCP サーバーをアカウントに接続することです。
userとpassword用語を使用する理由は、接続先がpostgres 互換データベースであるためです。
ホーム ディレクトリで、 .lsdという名前のファイルに、 user名とpasswordのプロパティ、メールアドレス、およびプロファイルのAPI キーを含む JSON を書き込みます。
{
"user": "<you@email.domain>",
"password": "<api_key>"
}あるいは、環境変数LSD_USERとLSD_PASSWORDを設定することもできます。
$ export LSD_USER='you@email.domain'
$ export LSD_PASSWORD='<api_key>'**重要:**この方法を採用したときにエラーが発生した場合は、設定された環境変数が PATH または MCP クライアントが呼び出しているプロセスからアクセス可能であることを確認してください。
https://www.pulsemcp.com/servers/lsd-so-internetdata
すぐにさらに追加されます。
このリポジトリをクローンする
$ git clone https://github.com/lsd-so/mcp.gitClaude デスクトップを使用している場合は、 claude_desktop_config.jsonファイルを更新します (作成ガイドはこちら)。
{
"mcpServers": {
// other MCP servers configured here...
"lsd": {
"command": "node",
"args": [
"/<path>/<to>/mcp/build/index.js"
]
}
}
}
MCP クライアントに「スキル」を教えたいシナリオでは、 LSD トリップ(それは何ですか? ) を使用して行うことができます。
「トリップ」とは、ローカル ブラウザーと対話することによって生成されたか、トリップを直接公開することによって生成された LSD プログラムで構成される公開モジュールです。
Bicycle ブラウザから、「クリック言語」を使用して LSD を派生できます。これは、右上のトランスクリプター アイコンをクリックしてアクティブ化できます。
または、 Command+k （Linux/Windowsの場合はCtrl+k ）を押します。これで、繰り返しコンテナや関心のあるフィールドをインタラクティブに「摘み取る」ことができます。
生成された LSD を使用すると、次のようにエイリアスを編集できます。
その後、言語を使用して公開できます。
ワークベンチから編集するだけで、旅行を公開できます。
ウェブとのブリッジに裏で使用されているinternetdata SDK をご確認ください。または、 create-your-internet ショートカットを使って始めることもできます。
$ yarn create your-internetまたは、npmを好む場合は
$ npm create your-internetSearch Brave for possibly relevant links to a query
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
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 tool searches for 'possibly relevant links,' which implies it's a read-only operation that returns search results, but doesn't disclose critical behaviors like rate limits, authentication needs, result format, pagination, or error handling. For a search tool with zero annotation coverage, this is insufficient.
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?
The description is extremely concise and front-loaded, consisting of a single sentence that directly states the tool's function. Every word earns its place with no wasted information, making it easy for an agent to parse quickly.
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?
Given the tool's complexity (a search operation with no output schema and no annotations), the description is incomplete. It lacks details on result structure, error cases, performance characteristics, or any behavioral context needed for effective use. The description alone doesn't provide enough information for reliable tool invocation.
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?
The description adds minimal meaning beyond the input schema. It implies the 'query' parameter is used to search Brave, but with 0% schema description coverage and only one parameter, the baseline is 4 for zero parameters; however, since there is one parameter and the description doesn't elaborate on its format, constraints, or examples, it doesn't fully compensate, warranting a score of 3.
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's purpose with a specific verb ('Search') and resource ('Brave'), and identifies the output ('links to a query'). It distinguishes the tool's function from its siblings (find_trips, run_lsd) by focusing on web search. However, it doesn't explicitly differentiate from potential alternative search tools, keeping it at a 4 rather than a 5.
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?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'possibly relevant links' but gives no context about when this search method is preferred over others, what types of queries it handles best, or any prerequisites for use. This leaves the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
brave_searchfind_tripsrun_lsdScored across 3 tools
The tools have unclear boundaries and overlapping purposes. 'brave_search' and 'find_trips' could both be interpreted as search tools, with the latter's lack of description making it ambiguous whether it searches for trips or performs another function. 'run_lsd' is completely opaque without a description, potentially overlapping with execution or processing roles.
The naming is mixed but readable. 'brave_search' and 'find_trips' follow a verb_noun pattern, while 'run_lsd' uses a verb_prefix style. There is no chaotic mixing of cases, but the conventions are not fully consistent across all tools.
With only 3 tools, the server feels thin for a general-purpose 'LSD MCP Server', suggesting an incomplete surface. The lack of descriptions for two tools makes it hard to assess scope, but the count is borderline too few for meaningful coverage of any substantial domain.
The server is severely incomplete. Based on the tool names, it appears to cover search and execution, but with no descriptions for 'find_trips' and 'run_lsd', there are significant gaps. It lacks any CRUD operations, lifecycle management, or clear domain coverage, making it impossible for agents to perform coherent workflows.
Driflyte MCP server which lets AI assistants query topic-specific knowledge from web and GitHub.
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
Hosted MCP server for live public-data APIs and Skills for AI agents.
