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MCP Inception MCP Server

by tanevanwifferen
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네, 어려운 질문입니다. 아쉽게도 설정하는 데 시간이 좀 걸릴 것 같습니다. 하지만 좀 더 쉽게 설명해 주실 수 있다면 홍보 자료를 보내주시면 감사하겠습니다.

mcp-inception MCP 서버

MCP 클라이언트에서 다른 MCP 클라이언트를 호출합니다. 작업을 위임하고, 컨텍스트 창을 오프로드합니다. 에이전트를 위한 에이전트!

이는 간단한 LLM 쿼리 시스템을 구현한 TypeScript 기반 MCP 서버입니다.

  • MCP 서버와 클라이언트가 하나로

  • mcp-client-cli를 사용하여 만들어졌습니다.

  • 오프로드 컨텍스트 창

  • 업무 위임

  • 작업의 병렬 및 맵리듀스 실행

특징

도구

  • execute_mcp_client - 별도의 LLM에 질문을 하고, 도구를 쿼리할 때 필요한 모든 중간 단계를 무시하고 출력을 반환합니다.

    • 질문을 필수 매개변수로 사용합니다.

    • 중간 컨텍스트를 모두 무시하고 답변을 반환합니다.

  • execute_parallel_mcp_client - 입력 목록과 기본 프롬프트를 받아 입력된 각 문자열에 대해 프롬프트를 병렬로 실행합니다. 예를 들어, 현재 6개 주요 도시(런던, 파리, 도쿄, 리우, 뉴욕, 시드니)의 시간을 구합니다.

    • 주요 프롬프트 "이 도시의 시간은 몇 시입니까?"를 사용합니다.

    • 런던, 파리 등의 입력 목록을 가져옵니다.

    • 각 입력에 대해 병렬로 프롬프트를 실행합니다.

    • 참고: 이 기능을 사용하기 전에 기다려주세요 .

  • execute_map_reduce_mcp_client - 여러 항목을 병렬로 처리한 다음 결과를 순차적으로 줄여 단일 출력으로 만듭니다.

    • 개별 항목 처리를 위해 {item} 플레이스홀더를 사용하여 mapPrompt 사용합니다.

    • 결과를 결합하기 위해 {accumulator} 및 {result} 플레이스홀더와 함께 reducePrompt 사용합니다.

    • 처리할 items 목록을 가져옵니다.

    • 누산기에 대한 선택적 initialValue

    • 항목을 병렬로 처리한 다음 순차적으로 결과를 줄입니다.

    • 예시 사용 사례: 여러 문서를 분석한 다음 모든 문서의 주요 통찰력을 요약으로 합성합니다.

Related MCP server: Orchestration MCP

개발

종속성:

  • mcp-client-cli를 설치하세요

    • 또한 ~/.llm/config.json 에 필요한 구성 파일과 mcp 서버를 설치합니다.

  • venv를 활성화하고 llm 실행 파일을 실행하는 bash 파일을 어딘가에 만드십시오.

지엑스피1

패키지 설치

종속성 설치:

npm install

서버를 빌드하세요:

npm run build

자동 재빌드를 사용한 개발의 경우:

npm run watch

설치

Claude Desktop과 함께 사용하려면 서버 구성을 추가하세요.

MacOS의 경우: ~/Library/Application Support/Claude/claude_desktop_config.json Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "mcp-inception": {
      "command": "node",
      "args": ["~/Documents/Cline/MCP/mcp-inception/build/index.js"], // build/index.js from this repo
      "disabled": false,
      "autoApprove": [],
      "env": {
        "MCP_INCEPTION_EXECUTABLE": "./run_llm.sh", // bash file from Development->Dependencies
        "MCP_INCEPTION_WORKING_DIR": "/mcp-client-cli working dir"
      }
    }
  }
}

디버깅

MCP 서버는 stdio를 통해 통신하므로 디버깅이 어려울 수 있습니다. 패키지 스크립트로 제공되는 MCP Inspector를 사용하는 것이 좋습니다.

npm run inspector

검사기는 브라우저에서 디버깅 도구에 액세스할 수 있는 URL을 제공합니다.

Available Tools

3 tools
execute_map_reduce_mcp_clientC

Process multiple items in parallel then sequentially reduce the results to a single output.

ParametersJSON Schema
NameRequiredDescriptionDefault
mapPromptYesTemplate prompt for processing each individual item. Use {item} as placeholder for the current item.
reducePromptYesTemplate prompt for reducing results. Use {accumulator} and {result} as placeholders.
initialValueNoInitial value for the accumulator (optional).
itemsYesArray of items to process.

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 of behavioral disclosure. It mentions parallel processing and sequential reduction, but lacks details on execution limits (e.g., rate limits, concurrency), error handling, or output format. For a tool with 4 parameters and no output schema, this is insufficient to inform safe or effective use.

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 that front-loads the core functionality. Every word earns its place by concisely explaining the two-phase process, with no redundant or vague language.

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 (map-reduce with 4 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like performance, errors, or result format, which are critical for an agent to use this tool correctly in context with siblings.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no parameter-specific semantics beyond implying a map-reduce workflow, which is already suggested by parameter names like 'mapPrompt' and 'reducePrompt'. Thus, it meets the baseline of 3 without compensating for gaps.

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 tool's purpose: 'Process multiple items in parallel then sequentially reduce the results to a single output.' This specifies the verb ('process' and 'reduce') and resource ('multiple items'), but doesn't explicitly distinguish it from sibling tools like 'execute_parallel_mcp_client' or 'execute_mcp_client', which likely have different processing patterns.

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 provides no guidance on when to use this tool versus its siblings ('execute_mcp_client' and 'execute_parallel_mcp_client'). It implies usage for parallel processing followed by reduction, but doesn't specify alternatives, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.

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

execute_mcp_clientB

Offload certain tasks to AI. Used for research purposes, do not use for code editing or anything code related. Only used to fetch data.

ParametersJSON Schema
NameRequiredDescriptionDefault
commandYesThe MCP client command to execute

TDQS

B3/5.0
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 'offload tasks to AI' and 'fetch data', but doesn't disclose behavioral traits such as what types of tasks are supported, how data is fetched, potential side effects, error handling, or performance characteristics. 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 three short sentences with no wasted words, making it efficient. However, it could be more front-loaded by stating the core purpose first, and some phrases like 'Used for research purposes' are a bit vague, but overall it's appropriately sized.

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 has no annotations, no output schema, and a single parameter with good schema coverage, the description is incomplete. It lacks details on what the tool actually does beyond 'fetch data', how it interacts with AI, what results to expect, or how it differs from siblings. For a tool named 'execute_mcp_client', this leaves too much ambiguity.

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 input schema has 1 parameter with 100% description coverage, so the schema already documents the 'command' parameter. The description doesn't add any meaning beyond the schema, such as examples of valid commands or how they relate to 'offloading tasks'. Baseline 3 is appropriate since the schema does the heavy lifting.

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 'offload certain tasks to AI' and 'fetch data', which gives a general purpose but lacks specificity about what 'tasks' or 'data' means. It distinguishes from siblings by mentioning 'do not use for code editing', but doesn't clearly define what it does do versus what siblings do. The purpose is vague rather than specific.

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

Usage Guidelines4/5

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

The description provides clear context on when to use ('for research purposes', 'fetch data') and explicit exclusions ('do not use for code editing or anything code related'). However, it doesn't mention alternatives or compare with sibling tools like execute_map_reduce_mcp_client or execute_parallel_mcp_client, so it's not fully explicit about when to choose this over others.

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

execute_parallel_mcp_clientC

Execute multiple AI tasks in parallel, with responses in JSON key-value pairs.

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesThe base prompt to use for all executions
itemsYesArray of parameters to process in parallel

TDQS

C2.9/5.0
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 discloses that execution is parallel and responses are in JSON key-value pairs, which adds some behavioral context. However, it lacks details on error handling, performance implications (e.g., rate limits, timeouts), authentication needs, or side effects. For a tool executing multiple AI tasks, 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the key information: parallel execution and JSON responses. There is no wasted text, and it directly communicates the core functionality 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 the complexity of executing multiple AI tasks in parallel, with no annotations and no output schema, the description is incomplete. It doesn't explain the return structure beyond 'JSON key-value pairs,' error cases, or how parallelism is managed. For a tool with potential concurrency and AI task execution nuances, more context 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.

Parameters3/5

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 both parameters ('prompt' as the base prompt and 'items' as an array of parameters). The description adds minimal value beyond the schema by implying that 'items' are processed in parallel, but it doesn't provide additional syntax, format details, or usage examples. Baseline 3 is appropriate as the schema does the heavy lifting.

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 tool's purpose: 'Execute multiple AI tasks in parallel, with responses in JSON key-value pairs.' It specifies the verb 'execute' and resource 'AI tasks,' with the parallel execution and JSON output format. However, it doesn't explicitly distinguish from sibling tools like 'execute_map_reduce_mcp_client' or 'execute_mcp_client,' which likely have different execution patterns.

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 provides no guidance on when to use this tool versus its siblings. It mentions parallel execution and JSON responses but doesn't specify scenarios, prerequisites, or alternatives. For example, it doesn't clarify if this is for batch processing, real-time tasks, or how it differs from 'execute_mcp_client' (likely sequential).

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.

  1. 3 tool updates
    • First observedexecute_map_reduce_mcp_client
    • First observedexecute_mcp_client
    • First observedexecute_parallel_mcp_client
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.9/5.0

Scored across 3 tools

Disambiguation2/5

The three tools have overlapping purposes centered around executing MCP client tasks, with unclear boundaries. execute_map_reduce_mcp_client and execute_parallel_mcp_client both handle parallel execution, while execute_mcp_client is described more broadly for offloading tasks, leading to potential confusion about when to use each.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with a 'execute_' prefix, making them predictable and readable. The naming structure is uniform across the set, with no deviations in style or convention.

Tool Count3/5

With only 3 tools, the set feels thin for a server named 'MCP Inception MCP Server', which suggests a broader scope. While the tools cover parallel and sequential execution, the limited count may not fully support complex workflows or diverse use cases implied by the server name.

Completeness2/5

The tool set is severely incomplete for an MCP server, lacking basic operations like configuration, monitoring, or error handling. It focuses narrowly on execution variants without covering setup, management, or integration aspects, leaving significant gaps for agent-driven tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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