A
licenseNot graded
qualityF
maintenanceAn MCP server that provides tools to load and fetch documentation from any llms.txt source, giving users full control over context retrieval for LLMs in IDE agents and applications.
1,033
MIT
Systems MCP
by lethain
系统-mcp
systems-mcp是一个 MCP 服务器，用于与lethain:systems库交互以进行系统建模。
它提供了两个工具：
run_systems_model运行系统模型的systems规范。它接受两个参数：规范以及可选的运行轮数（默认为 100）。load_systems_documentation将文档和示例加载到上下文窗口。这对于准备模型非常有用，使其更有助于编写系统模型。
它旨在与 Claude Desktop 或类似工具一起在本地运行。
用法
这是使用systems-mcp运行和渲染模型的示例。
这是从该提示生成的工件，包括运行系统模型的输出。
最后，这里有一个使用load_systems_documentation工具来启动上下文窗口并使用它生成系统规范的示例。这大致相当于在上下文窗口中包含lethain:systems/README.md ，但还包含了一些额外的示例（请参阅./docs/中的包含文件）。
然后您可以像以前一样渲染模型。
这里最有趣的是，我个人从未使用过systems来模拟社交网络，但尽管如此，LLM 仍然能够出色地生成规范。
Related MCP server: MCP Server Demo
安装
这些说明描述了在 OS X 上安装Claude Desktop的方法。它在其他平台上应该可以类似地工作。
安装Claude Desktop 。
将systems-mcp克隆到一个方便的位置，我假设是
/Users/will/systems-mcp确保你已经安装了
uv，你可以按照这些说明进行操作前往 Cladue Desktop，然后依次选择“设置”、“开发者”，并让其创建你的 MCP 配置文件。之后，你需要更新
claude_desktop_config.json文件。（注意，你应该将will替换为你的用户，例如whoami的输出。）cd /Users/will/Library/Application Support/Claude vi claude_desktop_config.json然后添加此部分：
{ "mcpServers": { "systems": { "command": "uv", "args": [ "--directory", "/Users/will/systems-mcp", "run", "main.py" ] } } }关闭 Claude 并重新打开它。
它应该可以工作...
Available Tools
2 toolsload_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
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
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
| Name | Required | Description | Default |
|---|---|---|---|
| spec | Yes | ||
| rounds | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
2 tool updates
v1.0.0- Changed
load_systems_documentation1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "load_systems_documentationOutput", + "type": "object" +}
- Changed
run_systems_model1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "run_systems_modelOutput", + "type": "object" +}
2 tool updates
- First observed
load_systems_documentation - First observed
run_systems_model
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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