Jentic
OfficialJentic MCP插件
为什么使用 Jentic MCP?
Jentic MCP 使开发人员能够快速发现和集成外部 API 和工作流,而无需编写或维护任何特定于 API 的代码。通过利用 MCP 协议和 Jentic 的 Agentic Runtime,开发人员可以:
按功能(而不是按供应商或实施)搜索 API 和工作流程。
立即生成与特定 API 细节无关的集成代码示例。
通过依赖标准化、声明式集成来避免样板并减少维护。
专注于构建功能,而 Jentic MCP 处理 API 交互的复杂性。
Related MCP server: agent-zone-mcp
API 工具
Jentic MCP 插件提供以下工具:
search_apis:在 Jentic 目录中搜索符合特定功能需求的 APIload_execution_info:从 Jentic 目录中检索 API 和操作的详细规范。这将包含您可能需要在mcpServers.jentic.env配置中提供的身份验证信息。execute:执行特定的 API 或工作流操作。
入门
推荐的方法是使用uvx直接从 GitHub 仓库运行服务器。您需要先使用以下命令安装uv :
brew install uv或pip install uv
获取您的 Jentic UUID
要使用 Jentic SDK,您必须首先获取 Jentic UUID。最简单的方法是使用 Jentic CLI。您可以选择添加电子邮件地址,以获得更高的速率限制并提前体验新功能。
pip install jentic
jentic register --email '<your_email>'这将打印您的 UUID 和一个导出命令以在您的环境中进行设置:
export JENTIC_UUID=<your-jentic-uuid>在您的 MCP 客户端配置中设置 Jentic UUID,如下所示。
配置文件的位置取决于您使用的客户端和操作系统。以下是一些常见示例:
风帆冲浪:
~/.codeium/windsurf/mcp_config.jsonClaude 桌面:
~/Library/Application Support/Claude/claude_desktop_config.json克劳德代码:
~/.claude.json光标:
~/cursor/.mcp.json
对于其他客户端,请查看客户端文档以了解如何添加 MCP 服务器。
{
"mcpServers": {
"jentic": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp",
"mcp"
],
"env": {
"JENTIC_UUID": "<your-jentic-uuid>"
}
}
}
}**注意:**保存配置文件后,您可能需要重新启动客户端应用程序(Windsurf、Claude Desktop)才能使更改生效。
MCP 工具使用
一旦 MCP 服务器运行,您就可以轻松使用 LLM 代理中的 MCP 工具来发现和执行 API 和工作流。
search_apis:在 Jentic 目录中搜索符合特定功能需求的 APIload_execution_info:从 Jentic 目录中检索 API 和操作的详细规范。这将包含您可能需要在mcpServers.jentic.env配置中提供的身份验证信息。execute:执行特定的 API 或工作流操作。
环境变量
当您使用需要身份验证的 API 时, load_execution_info工具会描述所需的环境变量。您可以通过 Windsurf 中的命令行来设置环境变量,但在某些客户端(例如 Claude Desktop)中,您需要将它们添加到 MCP 配置中:
{
"mcpServers": {
"jentic": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp",
"mcp"
],
"env": {
"DISCORD_BOTTOKEN=": "YOUR BOT TOKEN"
}
}
}
}替代方案(使用本地路径进行开发):
如果您正在本地积极开发 MCP 插件,请使用此选项。将/path/to/your/project/mcp替换为您的项目目录的绝对路径。
{
"mcpServers": {
"jentic": {
"command": "uvx",
"args": [
"--from",
"/path/to/your/project/mcp",
"mcp"
]
}
}
}*可选:*将JENTIC_API_URL环境变量添加到您的mcp_config.json文件以指向特定的 Jentic API(适用于两种方法):
{
"mcpServers": {
"jentic": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp",
"mcp"
],
"env": {
"JENTIC_API_URL": "https://your-jentic-api.url/"
}
}
}
}配置完成后,重新启动 Windsurf,Jentic MCP 工具将可用。
您可以通过运行以下命令来跟踪本地运行的 MCP 服务器生成的日志:
tail /path/to/mcp/jentic_ark2_mcp.log安装
通过 Smithery 安装
要通过Smithery自动为 Claude Desktop 安装 Jentic 插件:
npx -y @smithery/cli install @jentic/jentic-tools --client claude手动安装
确保已安装pdm ( pipx install pdm )。
要安装开发包及其依赖项:
# From the project root directory (e.g., /Users/kc/c/sdk/mcp)
pdm install -G dev运行 MCP 服务器
Jentic MCP 插件设计为使用uvx运行,它处理环境设置和执行。
默认模式(Stdio)
直接使用uvx运行 MCP 插件,使用--from和mcp脚本指定项目目录作为源:
从本地路径(开发):
# Use --from with the project directory and specify the 'mcp' script
uvx --from /path/to/your/project/mcp mcp
# Or, if running from within the project directory:
uvx --from . mcp来自远程存储库(建议一般使用):
uvx --from \
git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp \
mcp这会自动使用mcp脚本回调中定义的默认serve --transport stdio命令。
HTTP 模式
以 HTTP 模式运行服务器(例如,使用claude-cli进行测试):
从本地路径(开发):
# Default HTTP (port 8010)
uvx --from /path/to/your/project/mcp mcp serve --transport http
# With custom port
uvx --from /path/to/your/project/mcp mcp serve --transport http --port 8080
# With custom host
uvx --from /path/to/your/project/mcp mcp serve --transport http --host 0.0.0.0 --port 8080从远程存储库(推荐):
uvx --from \
git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp \
mcp serve --transport http --port 8080从远程 Git 存储库运行
您还可以直接从 Git 存储库 URL 运行 MCP 服务器,而无需使用uvx --from在本地克隆它:
# Example from a specific branch and subdirectory
uvx --from \
git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp
# Explanation:
# - git+https://... .git : The repository URL
# - @main : The branch, tag, or commit hash
# - #subdirectory=mcp : The folder within the repo containing the pyproject.toml此命令获取指定版本,在临时环境中安装其依赖项,并运行默认命令(即serve --transport stdio )。
您可以在 URL 片段后添加其他参数,如--log-level DEBUG或--mock :
uvx --from \
git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp \
--log-level DEBUG --mock要从远程源以 HTTP 模式运行:
uvx --from \
git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp \
serve --transport http --port 8080其他选项
日志记录
# Set logging level (applies to default stdio or explicit serve)
uvx --from /path/to/your/project/mcp mcp --log-level DEBUG
uvx --from /path/to/your/project/mcp mcp serve --transport http --log-level DEBUG
# Log to file (applies to default stdio or explicit serve)
uvx --from /path/to/your/project/mcp mcp --log-file jentic_mcp.log
uvx --from /path/to/your/project/mcp mcp serve --transport http --log-file jentic_mcp.log模拟模式
启用模拟模式进行开发,无需连接到实际的 Jentic API Knowledge Hub:
# Mock mode with default stdio
uvx --from /path/to/your/project/mcp mcp --mock
# Mock mode with explicit HTTP
uvx --from /path/to/your/project/mcp mcp serve --transport http --mock环境变量
使用.env文件提供环境变量:
# Env file with default stdio
uvx --from /path/to/your/project/mcp mcp --env-file .env
# Env file with explicit HTTP
uvx --from /path/to/your/project/mcp mcp serve --transport http --env-file .env与 Claude 一起使用
MCP 插件可以与 Claude 或其他支持 MCP 规范的 LLM 一起使用:
从远程存储库运行(推荐):
# Run the server in HTTP mode first
uvx --from \
git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp \
mcp serve --transport http --port 8000
# Then connect claude-cli
claude-cli --mcp http://localhost:8000从本地路径运行(开发):
# Run the server in HTTP mode first
uvx --from /path/to/your/project/mcp mcp serve --transport http --port 8000
# Then connect claude-cli
claude-cli --mcp http://localhost:8000发展
详细开发说明请见CLAUDE.md 。
包结构
src/mcp/:主 MCP 包transport/:传输实现(HTTP、stdio)mock/:用于开发的模拟数据提供者tools.py:工具定义handlers.py:请求处理程序main.py:CLI入口点adapters/:适配器实现core/:核心功能
tests/:测试套件
测试
# Ensure dev dependencies are installed: pdm install -G dev
pdm run test代码检查和格式化
通过 pdm 脚本使用ruff 、 black 、 isort 、 mypy 。
# Run all linters/formatters
pdm run lint
# Run only ruff
pdm run linter执照
专有 - Jentic
Available Tools
3 toolsexecuteC
Perform the chosen action for the user using the provided details (if any are needed).
| Name | Required | Description | Default |
|---|---|---|---|
| execution_type | Yes | Specify whether to execute an 'operation' or a 'workflow'. | |
| inputs | Yes | The input parameters required by the operation or workflow. | |
| uuid | Yes | The UUID of the operation or workflow to execute. |
TDQS
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 'perform the chosen action' which implies a write/mutation operation, but doesn't disclose critical behavioral traits: whether this is synchronous/asynchronous, what permissions are required, whether it's idempotent, what happens on failure, or what the response contains. For a tool that appears to execute potentially complex operations/workflows, 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 a single, efficient sentence that gets straight to the point without unnecessary words. However, this conciseness comes at the cost of being under-specified for a tool with three required parameters and no annotations - it could benefit from one more clarifying sentence about what gets executed.
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 3 required parameters (including a nested object), no annotations, and no output schema, the description is inadequate. It doesn't explain what types of actions can be executed, what the expected outcomes are, or provide any error handling context. The agent would struggle to use this tool effectively without trial and error.
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 three parameters thoroughly. The description adds no parameter-specific information beyond the generic 'using the provided details' - it doesn't clarify the relationship between execution_type, uuid, and inputs, or provide examples of valid input objects. Baseline 3 is appropriate when the schema does all 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 'Perform the chosen action for the user using the provided details' is tautological - it essentially restates the tool name 'execute' without specifying what kind of actions or resources are involved. While it mentions 'chosen action' and 'provided details', it doesn't clarify whether this executes operations, workflows, scripts, or other entities, nor does it distinguish this from sibling tools like 'load_execution_info' or 'search_apis'.
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 minimal guidance - it mentions 'chosen action' and 'provided details' but gives no explicit when-to-use criteria, no prerequisites, and no comparison to alternatives like 'load_execution_info' (which presumably loads execution information rather than performing execution) or 'search_apis'. The agent must infer usage from the parameter schema alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
load_execution_infoC
Get more details about a specific action (like what information it needs from the user) before confirming you want to run it. Usually used if the action requires specific inputs. Coding Agents: To provide tools to an LLM that you have selected at runtime, use this tool to retrieve the necessary information and save it to 'jentic.json' at the root of your project.
| Name | Required | Description | Default |
|---|---|---|---|
| operation_uuids | Yes | The UUIDs of the operations to include in the configuration files. | |
| workflow_uuids | Yes | The UUIDs of the workflows to include in the configuration files. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers limited behavioral insight. It mentions retrieving information and saving to a file, but doesn't disclose critical traits like whether this is a read-only operation, if it modifies state, authentication needs, rate limits, or error handling. The description adds some 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise but not optimally structured. The first sentence clearly states the purpose, but the second sentence adds implementation details for coding agents that may not apply to all users. The information is somewhat front-loaded but could be more focused.
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 no annotations and no output schema, the description should provide more complete context. It mentions retrieving details and saving to a file, but doesn't explain what format the details take, what 'jentic.json' contains, or what happens if parameters are invalid. For a tool with 2 required parameters and mutation implications, this is inadequate.
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 parameters are well-documented in the schema. The description doesn't add meaningful semantic context beyond what the schema provides about operation_uuids and workflow_uuids. It mentions 'configuration files' but doesn't clarify how parameters relate to this output.
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 'Get more details about a specific action' which indicates its purpose, but it's vague about what 'details' means and doesn't clearly differentiate from sibling tools like 'execute' or 'search_apis'. It mentions saving to 'jentic.json' which adds specificity but confuses the core purpose with implementation details for coding agents.
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 some guidance ('Usually used if the action requires specific inputs') but lacks explicit when-to-use vs. alternatives. It doesn't clarify when to choose this over 'execute' or 'search_apis', and the coding agent note is situational rather than general usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_apisA
Search for available actions or information based on what the user wants to do (e.g., 'find Discord servers', 'send a message'). Use this first to understand what's possible.
| Name | Required | Description | Default |
|---|---|---|---|
| capability_description | Yes | Natural language description of the action needed (e.g., 'send emails', 'weather forecasting', 'natural language processing') | |
| keywords | No | Optional list of specific keywords to help narrow down the search | |
| max_results | No | Maximum number of actions to return |
TDQS
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 the tool searches for actions or information but lacks details on behavioral traits such as response format, error handling, rate limits, or authentication needs. For a search tool with zero annotation coverage, this is a significant gap, though it does imply it returns results (e.g., 'to return' in the schema).
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 appropriately sized and front-loaded, with two sentences that efficiently convey purpose and usage guidelines. Every sentence earns its place by providing essential information without redundancy. It could be slightly more concise by integrating examples more seamlessly, but overall it's well-structured.
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 moderate complexity (3 parameters, no output schema, no annotations), the description is adequate but has clear gaps. It covers purpose and usage well but lacks behavioral details like return format or error handling. Without annotations or an output schema, the description should do more to compensate, making it minimally viable but incomplete.
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 three parameters thoroughly. The description does not add any additional meaning beyond what the schema provides, such as explaining how 'capability_description' interacts with 'keywords' or detailing search algorithms. Baseline 3 is appropriate when the schema does 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 tool's purpose: 'Search for available actions or information based on what the user wants to do.' It provides a specific verb ('search') and resource ('available actions or information'), with examples like 'find Discord servers' and 'send a message' to illustrate usage. However, it doesn't explicitly distinguish this from sibling tools like 'execute' or 'load_execution_info', which prevents a perfect score.
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 explicit guidance on when to use this tool: 'Use this first to understand what's possible.' This clearly indicates it should be employed as an initial step to discover capabilities before invoking other tools, effectively differentiating it from siblings like 'execute' (which likely performs actions) and 'load_execution_info' (which might retrieve details).
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.
3 tool updates
v1.0.0- First observed
execute - First observed
load_execution_info - First observed
search_apis
TDQS
Scored across 3 tools
The tools have distinct primary purposes: 'search_apis' discovers available actions, 'load_execution_info' retrieves details for a specific action, and 'execute' performs the action. There is minor potential overlap between 'load_execution_info' and 'execute' in handling action details, but the descriptions clarify their sequential workflow roles.
The naming is mixed: 'search_apis' and 'load_execution_info' use snake_case with descriptive verbs, while 'execute' is a single verb without a noun. This inconsistency in structure (two tools follow a verb_noun pattern, one does not) reduces predictability, though the names remain readable.
With 3 tools, the count is reasonable for a server focused on action discovery and execution. It feels slightly thin but covers core workflows (search, detail retrieval, execution), avoiding bloat. A few more tools might enhance completeness, but this is a solid minimal set.
The tool set covers a basic workflow (search, load info, execute) for action-based operations, but there are notable gaps. For example, there's no tool for managing or listing previously executed actions, error handling, or status checks, which could limit agent effectiveness in more complex scenarios.
Maintenance
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