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Systems MCP

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시스템-mcp

systems-mcp는 시스템 모델링을 위한 lethain:systems 라이브러리와 상호작용하는 MCP 서버입니다.

여기에는 두 가지 도구가 제공됩니다.

  • run_systems_model 시스템 모델의 systems 사양을 실행합니다. 사양과 선택적으로 모델 실행 라운드 수(기본값 100)를 매개변수로 받습니다.

  • load_systems_documentation 컨텍스트 창에 문서와 예제를 로드합니다. 이는 모델이 시스템 모델을 작성하는 데 더 도움이 되도록 준비하는 데 유용합니다.

Claude Desktop이나 비슷한 도구와 함께 로컬로 실행하도록 설계되었습니다.

용법

다음은 systems-mcp 사용하여 모델을 실행하고 렌더링하는 예입니다.

systems-mcp 사용에 대한 프롬프트 예

시스템 모델을 실행하여 생성된 출력을 포함하여 해당 프롬프트에서 생성된 아티팩트는 다음과 같습니다.

systems-mcp의 출력을 사용하기 위한 아티팩트 예

마지막으로, load_systems_documentation 도구를 사용하여 컨텍스트 창을 준비하고 시스템 사양을 생성하는 방법을 보여드리겠습니다. 이는 컨텍스트 창에 lethain:systems/README.md 포함하는 것과 거의 동일하지만, 몇 가지 추가 예제도 포함되어 있습니다( ./docs/ 에 포함된 파일 참조).

컨텍스트 창에 문서를 로드하는 예시 프롬프트

그러면 이전과 마찬가지로 모델을 렌더링할 수 있습니다.

생성된 모델을 렌더링하는 예제 프롬프트

여기서 가장 흥미로운 점은 제가 개인적으로 소셜 네트워크를 모델링하는 systems 사용해 본 적이 없지만, LLM은 그런 사실에도 불구하고 사양을 생성하는 데 놀라울 정도로 괜찮은 성과를 냈다는 것입니다.

Related MCP server: MCP Server Demo

설치

이 지침에서는 OS X에 Claude Desktop을 설치하는 방법을 설명합니다. 다른 플랫폼에서도 비슷한 방식으로 작동합니다.

  1. Claude Desktop을 설치하세요.

  2. 편리한 위치에 systems-mcp를 복제합니다. /Users/will/systems-mcp 라고 가정합니다.

  3. uv 설치되어 있는지 확인하고 다음 지침을 따르 세요.

  4. Claude Desktop, 설정, 개발자로 이동하여 MCP 구성 파일을 생성하세요. 그런 다음 claude_desktop_config.json 파일을 업데이트하세요. ( will 사용자 이름으로 바꿔야 합니다. 예: whoami 출력값)

    지엑스피1

    그런 다음 이 섹션을 추가합니다.

     {
   "mcpServers": {
     "systems": {
       "command": "uv",
       "args": [
         "--directory",
         "/Users/will/systems-mcp",
         "run",
         "main.py"
       ]
     }
   }
 }

  5. 클로드를 닫았다가 다시 열어보세요.

  6. 작동해야 합니다...

Available Tools

2 tools
load_systems_documentationB

Load systems documentation, examples, and specification details to improve the models ability to generate specifications.

Returns: Documentation and several examples of systems models

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses that the tool returns 'documentation and several examples of systems models' which describes output behavior. However, it doesn't mention important behavioral traits like whether this is a read-only operation, if it requires authentication, rate limits, or what happens if documentation isn't available. The description adds some behavioral context but leaves significant gaps.

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 appropriately concise with two sentences that each serve a purpose: the first states what the tool does and its purpose, the second describes the return value. It's front-loaded with the main action. There's minimal waste, though the second sentence could be integrated more smoothly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description is reasonably complete. It explains what the tool does and what it returns. The existence of an output schema means the description doesn't need to detail return values extensively. For a simple parameterless documentation loading tool, this provides adequate context.

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

Parameters4/5

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

The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't waste space discussing non-existent parameters. No additional parameter information is needed or provided, which is correct for a parameterless tool.

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 'loads systems documentation, examples, and specification details' which is a clear purpose, but it's somewhat vague about what exactly is being loaded. It distinguishes from the sibling tool 'run_systems_model' by focusing on loading documentation rather than executing models, but the distinction could be more explicit. The description doesn't specify verb+resource with precision.

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 mentions the tool helps 'improve the models ability to generate specifications' which implies usage context, but provides no explicit guidance on when to use this tool versus alternatives. There's no mention of prerequisites, timing, or comparison with the sibling tool 'run_systems_model'. The usage context is implied rather than clearly stated.

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

run_systems_modelC

Run a systems model and return output of list of dictionaries in JSON.

Args: spec: The systems model specification rounds: Number of rounds to run (default: 100)

ParametersJSON Schema
NameRequiredDescriptionDefault
specYes
roundsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

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 states the tool runs a model and returns JSON output, but doesn't describe what 'running a systems model' entails (e.g., computational requirements, execution time, side effects, error conditions, or authentication needs). For a tool that presumably performs computation, this is a significant gap in behavioral context.

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 appropriately concise with two sentences that directly address purpose and parameters. The structure is front-loaded with the core functionality first, followed by parameter details. No wasted words, though it could be slightly more polished in formatting.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there's an output schema (which handles return value documentation) but no annotations and incomplete parameter semantics, the description is minimally adequate. It covers the basic purpose and parameters but lacks important behavioral context for a computational tool. The presence of an output schema reduces the need to describe return values, but other gaps remain.

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 description adds some semantic context beyond the schema: it explains that 'spec' is 'The systems model specification' and 'rounds' is 'Number of rounds to run (default: 100)'. However, with 0% schema description coverage, the description doesn't fully compensate - it doesn't explain what format the 'spec' should be in (e.g., JSON, YAML, specific syntax) or what 'rounds' means in the context of systems modeling.

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: 'Run a systems model and return output of list of dictionaries in JSON.' This specifies the verb ('Run'), resource ('a systems model'), and output format. However, it doesn't explicitly differentiate from the sibling tool 'load_systems_documentation', which appears to be a documentation loading function rather than a model execution tool.

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 alternatives. It doesn't mention the sibling tool 'load_systems_documentation' or any other context for selection. The only usage hint is the default value for 'rounds', but this doesn't help with tool selection decisions.

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. 2 tool updatesv1.0.0
    • Changedload_systems_documentation1 field changed
      • changedOutput schema / (root)
        Previous value: -nullNew value: +{
+  "properties": {
+    "result": {
+      "title": "Result",
+      "type": "string"
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "title": "load_systems_documentationOutput",
+  "type": "object"
+}
    • Changedrun_systems_model1 field changed
      • changedOutput schema / (root)
        Previous value: -nullNew value: +{
+  "properties": {
+    "result": {
+      "title": "Result",
+      "type": "string"
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "title": "run_systems_modelOutput",
+  "type": "object"
+}
  2. 2 tool updates
    • First observedload_systems_documentation
    • First observedrun_systems_model
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TDQS

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have completely distinct purposes: one loads documentation/examples for reference, while the other executes a model with given specifications. There is no overlap in functionality, and an agent would easily differentiate between them based on their clear descriptions.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with snake_case naming: load_systems_documentation and run_systems_model. The naming is predictable and readable, with no deviations in style or convention across the set.

Tool Count2/5

With only 2 tools, the server feels thin for the domain of 'systems modeling,' which typically involves more operations like creating, updating, or analyzing models. This limited set may hinder agents from performing comprehensive tasks, as it lacks tools for specification generation, validation, or result analysis beyond basic execution.

Completeness2/5

The tool set is severely incomplete for systems modeling. It provides documentation loading and model execution but misses essential operations such as creating or editing specifications, validating models, analyzing outputs in-depth, or managing model versions. This creates significant gaps that will likely cause agent failures in real-world scenarios.

Maintenance

ActivityInactive
ResponsivenessNo issues

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