LLMが他のMCPサーバーを検査・テストできるようにする軽量なMCPサーバーです。外部CLIを呼び出すことなく、MCP SDKを直接使用する自己完結型の実装です。
SDKの直接統合: サーバーとクライアントの両方の操作に @modelcontextprotocol/sdk を直接使用
すべてのトランスポートタイプ: stdio、SSE、HTTP（ストリーミング可能）トランスポートをサポート
最小限のフットプリント: 単一の依存関係 (@modelcontextprotocol/sdk)
完全なMCP検査: ツールのリスト表示、ツールの呼び出し、リソースのリスト表示、リソースの読み取り、プロンプトのリスト表示、プロンプトの取得
セッション管理: 自動ガベージコレクションを備えた永続的な接続
イベントバッファリング: デバッグ用の通知、トラフィック、エラーのキャプチャ
Related MCP server: Mock MCP Server
npm install
npm run buildMCP設定に追加します。ハーネスによって多少の違いはありますが、一般的な形式は同じです：
{
"mcpServers": {
"mcp-inspector": {
"command": "node",
"args": ["/path/to/mcp-inspector-as-mcp-server/dist/server.js"]
}
}
}ツール | 説明 |
| MCPサーバーへの永続的な接続を確立します。 |
| 永続的なセッションを閉じ、リソースを解放します。 |
| ステータスとアイドル時間を含むすべてのアクティブなセッションをリスト表示します。 |
| セッションからバッファリングされたイベント（通知、トラフィック、エラー）を読み取ります。 |
| セッションのキューに人間の指示メッセージを注入します。 |
ツール | 説明 |
| MCPサーバーによって公開されているすべてのツールをリスト表示します |
| MCPサーバー上のツールを呼び出します |
| MCPサーバーによって公開されているすべてのリソースをリスト表示します |
| 特定のリソースを読み取ります |
| リソーステンプレートをリスト表示します |
| すべてのプロンプトをリスト表示します |
| 特定のプロンプトを取得します |
すべてのツールは以下の接続パラメータを受け入れます：
stdioトランスポート（ローカルコマンド）の場合：
command: 実行するコマンド (例: "node", "python")
args: 引数の配列 (例: ["path/to/server.js"])
SSE/HTTPトランスポート（リモートサーバー）の場合：
url: サーバーURL (例: "http://localhost:3000/sse")
headers: オプションのHTTPヘッダーオブジェクト
共通：
transport: トランスポートタイプを強制指定 ("stdio", "sse", または "http")。指定がない場合は自動検出されます。
session_id: (オプション) 一時的な接続を作成する代わりに、既存の永続的なセッションを使用します。
ステートフルなサーバーの動作をデバッグするには、永続的なセッションを使用します：
1. insp_connect → returns session_id
2. insp_tools_list (with session_id) → uses persistent connection
3. insp_tools_call (with session_id) → state is preserved
4. insp_read_events (with session_id) → see notifications
5. insp_disconnect (with session_id) → cleanupセッションは30分間操作がないと自動的に閉じられます。
このインスペクターは、LLM主導のMCPテストをリアルタイムで観察し、ガイドできるヒューマン・イン・ザ・ループワークフローを可能にします。
┌─────────────┐ MCP calls ┌─────────────────┐ forwards ┌─────────────┐
│ LLM Agent │ ◄────────────────► │ MCP Inspector │ ◄──────────────► │ Target MCP │
│ (Antigravity) │ (v2.0) │ │ Server │
└─────────────┘ └────────┬────────┘ └─────────────┘
│
Events logged to
session EventBuffer
│
┌───────────────────────┼───────────────────────┐
│ │ │
▼ ▼ ▼
insp_read_events HTTP :9847/api mcp-steer CLI
(LLM reads events) (external access) (human injection)LLM経由: エージェントは insp_read_events を呼び出して何が起きているかを確認できます：
{
"session_id": "sess_abc123",
"types": ["traffic_in", "traffic_out"],
"limit": 20
}HTTP経由: ステアリングAPIを直接クエリします：
curl http://127.0.0.1:9847/api/sessionsLLMの次のツール応答に表示されるガイダンスメッセージを注入します。
CLIを使用する場合:
./bin/mcp-steer.mjs "Focus on testing the error handling paths"
./bin/mcp-steer.mjs --session sess_abc123 "Try calling with invalid params"HTTPを使用する場合:
curl -X POST http://127.0.0.1:9847/api/steer \
-H "Content-Type: application/json" \
-d '{"message": "Check the authentication flow next"}'MCPツールを使用する場合:
{
"tool": "insp_inject_steering",
"arguments": {
"session_id": "sess_abc123",
"message": "Great progress! Now test edge cases."
}
}タイプ | 説明 |
| ターゲットサーバーへ送信されたメッセージ |
| ターゲットサーバーから受信したメッセージ |
| ターゲットサーバーからのMCP通知 |
| 通信中に発生したエラー |
| セッションに注入された人間の指示メッセージ |
LLMがセッションを作成: insp_connect → sess_abc123 を取得
LLMがテストを開始: session_id を指定して insp_tools_call を実行
人間が観察: curl http://127.0.0.1:9847/api/sessions
人間が誘導: ./bin/mcp-steer.mjs "バッチエンドポイントもテストして"
LLMが誘導を受信: 次のツール応答に ⚡ STEERING from human: ... が含まれる
LLMが適応: 人間のガイダンスを考慮に入れる
ローカルMCPサーバーからツールをリスト表示（一時的）:
{
"command": "node",
"args": ["/path/to/some-mcp-server/dist/server.js"]
}永続的なセッションを作成:
{
"command": "node",
"args": ["/path/to/some-mcp-server/dist/server.js"]
}
// Returns: { "session_id": "sess_abc123", "server_info": {...} }セッションを使用してツールを呼び出す:
{
"session_id": "sess_abc123",
"tool_name": "search",
"tool_args": {"query": "hello"}
}├── src/
│ ├── server.ts # MCP server exposing inspector tools
│ ├── client.ts # Client wrapper (hybrid stateless/session mode)
│ ├── transport.ts # Transport factory (stdio, SSE, HTTP) + TracingWrapper
│ ├── session.ts # SessionRegistry with GC (30-min TTL)
│ └── events.ts # EventBuffer (ring buffer for notifications)
├── bin/
│ └── mcp-steer.mjs # CLI tool for human steering
├── tests/ # Integration test scripts (run with npx tsx)
└── vitest.config.ts # Unit test + coverage configオリジナルのMCP Inspectorは、複数のプロジェクトにまたがるWebベースのUIとCLIの組み合わせでした。これを、LLMが以下を行うために使用できる単一の軽量なMCPサーバーに統合しました：
MCPサーバーを反復的に開発およびデバッグする
会話を離れることなくMCPサーバーの機能をテストする
MCPサーバーが公開しているツール/リソース/プロンプトを探索する
永続的なセッションでステートフルな動作をデバッグする
npm install # install dependencies
npm run build # compile TypeScript
npm run dev # watch mode
npm test # run unit tests
npm run test:cov # run tests with coverage
npm run lint # lint source files
npm run format # auto-format with Prettier
npm run typecheck # type-check without emittingヒューマン・イン・ザ・ループワークフローのための人間の誘導 (insp_inject_steering) を追加
外部からの誘導/可観測性のためにポート9847でHTTP APIを追加
人間が簡単に操作できるように mcp-steer.mjs CLIツールを追加
TracingTransportWrapper におけるハンドラーキャプチャのタイミングによりメッセージが消失する重大なバグを修正
セッション管理を追加 (insp_connect, insp_disconnect, insp_list_sessions)
イベントバッファリングを追加 (insp_read_events)
すべての検査ツールが永続的な接続のためにオプションの session_id をサポート
自動ガベージコレクションを追加（アイドルセッションのTTLは30分）
下位互換性: 従来の一時的な動作には session_id を省略可能
一時的な接続による初期リリース
MIT
Get a specific prompt from an MCP server.
| Name | Required | Description | Default |
|---|---|---|---|
| command | No | Command to run the MCP server (e.g., 'node', 'python') | |
| args | No | Arguments to pass to the command (e.g., ['build/index.js']) | |
| url | No | URL for SSE/HTTP transport (alternative to command) | |
| transport | No | Transport type (auto-detected if not specified) | |
| headers | No | HTTP headers for SSE/HTTP transport | |
| prompt_name | Yes | Name of the prompt to get | |
| prompt_args | No | Arguments to pass to the prompt |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal information. It states the tool retrieves a prompt but doesn't describe what happens if the prompt doesn't exist, whether authentication is required, if there are rate limits, what format the prompt returns in, or whether this is a read-only operation. The description is too basic for a tool with 7 parameters and server interaction.
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 a single, clear sentence that gets straight to the point with zero wasted words. It's appropriately sized for a retrieval operation and front-loads the essential information. Every word earns its place in communicating the core functionality.
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 7 parameters, server communication, and no output schema, the description is insufficiently complete. It doesn't explain what constitutes a 'prompt' in this system, what the return format looks like, error handling, or authentication requirements. The combination of complex parameters and no annotations means the description should provide more contextual information about the operation.
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 the schema already documents all 7 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema - it doesn't explain relationships between parameters (like command/args vs url/transport), provide examples of prompt_name formats, or clarify when prompt_args are needed. This meets the baseline for high schema coverage.
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 action ('Get') and resource ('a specific prompt from an MCP server'), making the purpose immediately understandable. It distinguishes from sibling tools like 'insp_prompts_list' by specifying retrieval of a single prompt rather than listing multiple. However, it doesn't explicitly mention what 'prompt' refers to in this context (e.g., AI prompt templates, system prompts).
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 doesn't mention when to choose this over 'insp_prompts_list' (for listing all prompts) or 'insp_tools_call' (which might handle different operations). There's no discussion of prerequisites, error conditions, or typical use cases for prompt retrieval.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all prompts exposed by an MCP server.
| Name | Required | Description | Default |
|---|---|---|---|
| command | No | Command to run the MCP server (e.g., 'node', 'python') | |
| args | No | Arguments to pass to the command (e.g., ['build/index.js']) | |
| url | No | URL for SSE/HTTP transport (alternative to command) | |
| transport | No | Transport type (auto-detected if not specified) | |
| headers | No | HTTP headers for SSE/HTTP transport |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states it's a list operation, implying read-only behavior, but doesn't mention any side effects, permissions required, rate limits, or what the output format looks like. For a tool that interacts with external servers, this lack of operational context is a significant gap.
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 a single, clear sentence that efficiently conveys the core purpose without any fluff. It's front-loaded with the main action and resource, making it easy to parse. Every word earns its place in defining what the tool does.
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 complexity of connecting to external servers via multiple transport methods and the lack of annotations and output schema, the description is insufficient. It doesn't explain what 'prompts' are in this context, how results are returned, or any error conditions. For a tool with 5 parameters and no structured safety hints, more operational detail is needed.
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 the schema fully documents all 5 parameters. The description adds no parameter-specific information beyond implying the tool connects to an MCP server. This meets the baseline of 3 where the schema does the heavy lifting, but the description doesn't compensate with additional context like default behaviors or parameter interactions.
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 action ('List all prompts') and the target resource ('exposed by an MCP server'), making the purpose immediately understandable. It distinguishes from siblings like insp_tools_list by specifying 'prompts' rather than 'tools', but doesn't explicitly contrast with insp_prompts_get, which would fetch a specific prompt rather than list all.
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 like insp_prompts_get or insp_tools_list. It mentions the scope ('all prompts') but offers no context about prerequisites, typical use cases, or limitations that would help an agent decide between this and sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all resources exposed by an MCP server.
| Name | Required | Description | Default |
|---|---|---|---|
| command | No | Command to run the MCP server (e.g., 'node', 'python') | |
| args | No | Arguments to pass to the command (e.g., ['build/index.js']) | |
| url | No | URL for SSE/HTTP transport (alternative to command) | |
| transport | No | Transport type (auto-detected if not specified) | |
| headers | No | HTTP headers for SSE/HTTP transport |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it 'lists' resources, implying a read-only operation, but doesn't cover aspects like whether it requires authentication, how it handles errors, if it's rate-limited, or what the output format looks like (e.g., JSON list). This leaves significant gaps for an agent to understand its behavior.
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 a single, clear sentence that front-loads the core purpose without unnecessary words. It efficiently conveys the essential 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 complexity (5 parameters, no annotations, no output schema), the description is insufficient. It doesn't explain what 'resources' entail in this context, how results are returned, or any behavioral traits like error handling. For a tool that likely inspects server capabilities, more context is needed to guide effective use.
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 input schema has 100% description coverage, so parameters are well-documented in the schema itself. The description adds no additional meaning about parameters beyond implying the tool interacts with an MCP server, which is already inferred from the schema's command/args/url fields. This meets the baseline for high schema coverage.
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 action ('List all resources') and the target ('exposed by an MCP server'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'insp_resources_read' or 'insp_resources_templates', which likely have different purposes related to resources.
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. With siblings like 'insp_resources_read' (likely for reading a specific resource) and 'insp_resources_templates' (likely for templates), there's no indication of context, prerequisites, or exclusions for this list operation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read a specific resource from an MCP server.
| Name | Required | Description | Default |
|---|---|---|---|
| command | No | Command to run the MCP server (e.g., 'node', 'python') | |
| args | No | Arguments to pass to the command (e.g., ['build/index.js']) | |
| url | No | URL for SSE/HTTP transport (alternative to command) | |
| transport | No | Transport type (auto-detected if not specified) | |
| headers | No | HTTP headers for SSE/HTTP transport | |
| uri | Yes | URI of the resource to read |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Read a specific resource,' implying a read-only operation, but doesn't cover critical aspects like authentication needs, rate limits, error handling, or what the output looks like (e.g., raw data, structured format). For a tool with 6 parameters and no output schema, this is a significant gap in transparency.
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 a single, clear sentence that directly states the tool's purpose. It's front-loaded with the core action ('Read a specific resource') and avoids unnecessary details. Every word earns its place, making it highly concise and well-structured for quick understanding.
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 (6 parameters, nested objects, no output schema) and lack of annotations, the description is incomplete. It doesn't explain the resource type, how parameters like 'transport' affect behavior, or what the read operation returns. For a tool that likely involves server interaction and resource retrieval, more context is needed to guide effective use.
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%, meaning all parameters are documented in the input schema. The description adds no additional meaning beyond the schema, such as explaining how parameters interact (e.g., 'command' vs. 'url' for transport) or providing examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 states the tool 'Read a specific resource from an MCP server,' which clearly indicates a read operation on a resource. However, it doesn't specify what type of resource (e.g., file, data object) or differentiate from sibling tools like 'insp_resources_list' (which likely lists resources) or 'insp_resources_templates' (which might handle templates). The purpose is clear but lacks sibling differentiation.
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 doesn't mention prerequisites (e.g., server setup), exclusions (e.g., not for writing), or compare to siblings like 'insp_resources_list' for listing resources. Without such context, an agent might struggle to select the correct tool in a given scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List resource templates exposed by an MCP server.
| Name | Required | Description | Default |
|---|---|---|---|
| command | No | Command to run the MCP server (e.g., 'node', 'python') | |
| args | No | Arguments to pass to the command (e.g., ['build/index.js']) | |
| url | No | URL for SSE/HTTP transport (alternative to command) | |
| transport | No | Transport type (auto-detected if not specified) | |
| headers | No | HTTP headers for SSE/HTTP transport |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists resource templates but doesn't describe what 'exposed by an MCP server' entails, such as whether this requires server connectivity, authentication, or specific permissions. For a tool with 5 parameters and no annotation coverage, this is a significant gap in transparency.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy to understand at a glance while being appropriately sized for its function.
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 complexity (5 parameters, no annotations, no output schema), the description is minimal but covers the basic purpose. It lacks details on behavioral aspects like server interaction requirements or output format, which are important for a tool that likely involves external communication. However, the high schema coverage mitigates some gaps, making it adequate but with clear room for improvement.
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 input schema has 100% description coverage, providing clear details for all 5 parameters (e.g., command, args, url, transport, headers). The description doesn't add any parameter-specific information beyond what's in the schema, such as examples or usage context. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the heavy lifting.
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 action ('List') and target ('resource templates exposed by an MCP server'), providing a specific verb+resource combination. However, it doesn't explicitly distinguish this from sibling tools like 'insp_resources_list' or 'insp_resources_read', which likely handle different aspects of resources, leaving some ambiguity about differentiation.
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 doesn't mention any prerequisites, context for usage, or comparisons to sibling tools such as 'insp_resources_list', which might handle actual resources rather than templates. This lack of guidance could lead to confusion in tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Call a tool on an MCP server. Connects, calls the tool, and disconnects.
| Name | Required | Description | Default |
|---|---|---|---|
| command | No | Command to run the MCP server (e.g., 'node', 'python') | |
| args | No | Arguments to pass to the command (e.g., ['build/index.js']) | |
| url | No | URL for SSE/HTTP transport (alternative to command) | |
| transport | No | Transport type (auto-detected if not specified) | |
| headers | No | HTTP headers for SSE/HTTP transport | |
| tool_name | Yes | Name of the tool to call | |
| tool_args | No | Arguments to pass to the tool (key=value pairs) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions connecting, calling, and disconnecting, which implies network/process operations, but doesn't disclose critical traits like error handling, timeouts, authentication needs, rate limits, or what happens if the server is unavailable. For a tool that interacts with external servers, this lack of behavioral context is a significant gap.
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 (one sentence) and front-loaded with the core purpose. Every word earns its place by summarizing the tool's lifecycle (connect, call, disconnect). There's no redundancy or fluff, making it efficient for quick understanding.
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 (7 parameters, no annotations, no output schema), the description is incomplete. It doesn't address what the tool returns, error conditions, or how to interpret results from the called tool. For a tool that dynamically invokes other tools on a server, more context about output format, success/failure states, and integration patterns is needed to be fully helpful.
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 the schema already documents all 7 parameters thoroughly. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't explain parameter interactions or provide examples). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract from the well-documented 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 clearly states the tool's purpose: 'Call a tool on an MCP server' with specific verbs (connects, calls, disconnects). It distinguishes from siblings like insp_tools_list (which lists tools) but doesn't explicitly contrast with other tools that might also involve calling operations. The purpose is well-defined but could be more specific about what distinguishes it from potential alternatives.
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 doesn't mention siblings like insp_tools_list (which might be used to discover tools before calling) or other tools that might handle MCP server interactions differently. There's no context about prerequisites, error conditions, or typical use cases, leaving the agent with minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all tools exposed by an MCP server. Connects, lists tools, and disconnects.
| Name | Required | Description | Default |
|---|---|---|---|
| command | No | Command to run the MCP server (e.g., 'node', 'python') | |
| args | No | Arguments to pass to the command (e.g., ['build/index.js']) | |
| url | No | URL for SSE/HTTP transport (alternative to command) | |
| transport | No | Transport type (auto-detected if not specified) | |
| headers | No | HTTP headers for SSE/HTTP transport |
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 connection and disconnection process, which is helpful, but lacks critical details such as whether this is a read-only operation, potential side effects (e.g., server state changes), error handling, or performance considerations (e.g., timeouts). For a tool that interacts with external servers, this is a significant gap.
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—just one sentence with three clauses—and front-loaded with the core purpose. Every word earns its place by conveying essential information about the tool's function and operational flow without any redundancy or fluff.
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 complexity of connecting to and querying an MCP server, the description is incomplete. It lacks details on output format (no output schema is provided), error conditions, authentication needs, or rate limits. While the schema covers parameters well, the overall context for safe and effective use is insufficient, especially for a tool with external dependencies.
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 schema description coverage is 100%, meaning all parameters are documented in the input schema. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain parameter interactions or provide examples). This meets the baseline score of 3 for high schema coverage, but doesn't compensate with extra value.
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 action ('List all tools') and resource ('exposed by an MCP server'), providing a specific verb+resource combination. It also mentions the operational flow ('Connects, lists tools, and disconnects'), which adds useful context. However, it doesn't explicitly differentiate this tool from its sibling 'insp_tools_call', which appears to be for invoking tools rather than listing them.
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 like 'insp_tools_call' or other sibling tools. It mentions the operational steps but doesn't specify prerequisites, use cases, or exclusions. This leaves the agent without clear direction on tool selection in context.
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.
v1.0.0insp_prompts_getinsp_prompts_listinsp_resources_listinsp_resources_readinsp_resources_templatesinsp_tools_callinsp_tools_listScored across 7 tools
Every tool has a clearly distinct purpose targeting different MCP server components: prompts (get/list), resources (list/read/templates), and tools (list/call). There is no overlap or ambiguity in functionality, making it easy for an agent to select the correct tool.
All tools follow a consistent 'insp_[component]_[action]' pattern with snake_case, using clear verbs like get, list, read, call, and templates. This predictability enhances usability and reduces confusion.
With 7 tools, the server is well-scoped for inspecting MCP servers, covering prompts, resources, and tools comprehensively. Each tool earns its place without being excessive or insufficient for the domain.
The tool set provides complete coverage for inspecting MCP servers, including listing and accessing prompts, resources (with templates), and tools (with calling capability). There are no obvious gaps, ensuring agents can perform all necessary inspection tasks.
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
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.
Security scanner for MCP servers. Detect vulnerabilities, prompt injection, and tool poisoning.
Remote MCP server for supportsheep: run AI interviews and manage support content for your blog.