LLM이 다른 MCP 서버를 검사하고 테스트할 수 있도록 하는 가벼운 MCP 서버입니다. 외부 CLI를 호출하지 않고 MCP SDK를 직접 사용하는 독립형 구현입니다.
직접적인 SDK 통합: 서버 및 클라이언트 작업 모두에 @modelcontextprotocol/sdk를 직접 사용
모든 전송 유형: stdio, SSE 및 HTTP(스트리밍 가능) 전송 지원
최소한의 설치 공간: 단일 종속성 (@modelcontextprotocol/sdk)
전체 MCP 검사: 도구 나열, 도구 호출, 리소스 나열, 리소스 읽기, 프롬프트 나열, 프롬프트 가져오기
세션 관리: 자동 가비지 컬렉션이 포함된 지속적 연결
이벤트 버퍼링: 디버깅을 위한 알림, 트래픽 및 오류 캡처
Related MCP server: Mock MCP Server
npm install
npm run buildMCP 구성에 추가하세요. 하네스마다 약간의 차이가 있지만 일반적인 형식은 동일합니다:
{
"mcpServers": {
"mcp-inspector": {
"command": "node",
"args": ["/path/to/mcp-inspector-as-mcp-server/dist/server.js"]
}
}
}도구 | 설명 |
| MCP 서버에 대한 지속적 연결을 설정합니다. |
| 지속적 세션을 닫고 리소스를 해제합니다. |
| 상태 및 유휴 시간과 함께 모든 활성 세션을 나열합니다. |
| 세션에서 버퍼링된 이벤트(알림, 트래픽, 오류)를 읽습니다. |
| 세션의 큐에 인간의 조종 메시지를 주입합니다. |
도구 | 설명 |
| MCP 서버가 노출하는 모든 도구를 나열합니다 |
| MCP 서버의 도구를 호출합니다 |
| MCP 서버가 노출하는 모든 리소스를 나열합니다 |
| 특정 리소스를 읽습니다 |
| 리소스 템플릿을 나열합니다 |
| 모든 프롬프트를 나열합니다 |
| 특정 프롬프트를 가져옵니다 |
모든 도구는 다음 연결 매개변수를 허용합니다:
stdio 전송(로컬 명령)의 경우:
command: 실행할 명령 (예: "node", "python")
args: 인수 배열 (예: ["path/to/server.js"])
SSE/HTTP 전송(원격 서버)의 경우:
url: 서버 URL (예: "http://localhost:3000/sse")
headers: 선택적 HTTP 헤더 객체
공통:
transport: 전송 유형 강제 지정 ("stdio", "sse" 또는 "http"). 지정하지 않으면 자동 감지됩니다.
session_id: (선택 사항) 일시적인 연결을 만드는 대신 기존 지속적 세션을 사용합니다.
상태 저장 서버 동작을 디버깅하려면 지속적 세션을 사용하세요:
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) → cleanup세션은 30분 동안 활동이 없으면 자동으로 닫힙니다.
이 검사기는 LLM 기반 MCP 테스트를 실시간으로 관찰하고 안내할 수 있는 인간 개입(human-in-the-loop) 워크플로우를 가능하게 합니다.
┌─────────────┐ 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)LLM을 통해: 에이전트가 insp_read_events를 호출하여 무슨 일이 일어나고 있는지 확인할 수 있습니다:
{
"session_id": "sess_abc123",
"types": ["traffic_in", "traffic_out"],
"limit": 20
}HTTP를 통해: 조종 API를 직접 쿼리합니다:
curl http://127.0.0.1:9847/api/sessionsLLM의 다음 도구 응답에 나타나는 안내 메시지를 주입합니다.
CLI 사용:
./bin/mcp-steer.mjs "Focus on testing the error handling paths"
./bin/mcp-steer.mjs --session sess_abc123 "Try calling with invalid params"HTTP 사용:
curl -X POST http://127.0.0.1:9847/api/steer \
-H "Content-Type: application/json" \
-d '{"message": "Check the authentication flow next"}'MCP 도구 사용:
{
"tool": "insp_inject_steering",
"arguments": {
"session_id": "sess_abc123",
"message": "Great progress! Now test edge cases."
}
}유형 | 설명 |
| 대상 서버로 전송된 메시지 |
| 대상 서버로부터 수신된 메시지 |
| 대상 서버로부터의 MCP 알림 |
| 통신 중 발생한 오류 |
| 세션에 주입된 인간 조종 메시지 |
LLM이 세션 생성: insp_connect → sess_abc123 획득
LLM이 테스트 시작: session_id와 함께 insp_tools_call 호출
인간이 관찰: curl http://127.0.0.1:9847/api/sessions
인간이 조종: ./bin/mcp-steer.mjs "배치 엔드포인트도 테스트하세요"
LLM이 조종 수신: 다음 도구 응답에 ⚡ STEERING from human: ... 포함
LLM이 적응: 인간의 안내를 고려하여 작업 수행
로컬 MCP 서버의 도구 나열 (일시적):
{
"command": "node",
"args": ["/path/to/some-mcp-server/dist/server.js"]
}지속적 세션 생성:
{
"command": "node",
"args": ["/path/to/some-mcp-server/dist/server.js"]
}
// Returns: { "session_id": "sess_abc123", "server_info": {...} }세션을 사용하여 도구 호출:
{
"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 config기존 MCP Inspector는 여러 프로젝트에 걸쳐 있는 웹 기반 UI + CLI 조합입니다. 이 프로젝트는 핵심 기능을 LLM이 다음을 수행할 수 있는 단일 가벼운 MCP 서버로 통합합니다:
MCP 서버를 반복적으로 개발하고 디버깅
대화를 떠나지 않고 MCP 서버 기능 테스트
MCP 서버가 노출하는 도구/리소스/프롬프트 탐색
지속적 세션으로 상태 저장 동작 디버깅
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 emitting인간 개입 워크플로우를 위한 인간 조종(insp_inject_steering) 추가
외부 조종/관찰 가능성을 위해 9847 포트에 HTTP API 추가
쉬운 인간 상호작용을 위한 mcp-steer.mjs CLI 도구 추가
핸들러 캡처 타이밍으로 인해 메시지 손실이 발생하던 TracingTransportWrapper의 치명적인 버그 수정
세션 관리 추가 (insp_connect, insp_disconnect, insp_list_sessions)
이벤트 버퍼링 추가 (insp_read_events)
모든 검사 도구가 이제 지속적 연결을 위한 선택적 session_id 지원
자동 가비지 컬렉션 추가 (유휴 세션에 대해 30분 TTL)
하위 호환성: 기존의 일시적인 동작을 위해 session_id 생략 가능
일시적 연결을 포함한 초기 릴리스
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.
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