A
licenseNot graded
qualityD
maintenanceAn MCP server that enables AI assistants to interact with Signoz observability platform, providing tools to query dashboards, metrics, traces, logs, and APM data with time range support.
14
MIT
MCP Server for langfuse
by z9905080
langfuse 的 MCP 服务器
用于将 AI 助手与 Langfuse 工作区集成的模型上下文协议 (MCP) 服务器实现。
概述
此软件包提供了一个 MCP 服务器，使 AI 助手能够与 Langfuse 工作区交互。它允许 AI 模型执行以下操作：
按时间范围查询 LLM 指标
Related MCP server: langfuse-mcp-extended
安装
# Install from npm
npm install shouting-mcp-langfuse
# Or install globally
npm install -g shouting-mcp-langfuse您可以在 npm 上找到该软件包： shouting-mcp-langfuse
先决条件
在使用服务器之前，您需要创建一个 Langfuse 项目并获取项目的公钥和私钥。您可以在 Langfuse 仪表板中找到这些密钥。
建立一个 Langfuse 项目
获取公钥和私钥
设置环境变量
配置
服务器需要以下环境变量：
LANGFUSE_DOMAIN：Langfuse 域名（默认值：https://api.langfuse.com）LANGFUSE_PUBLIC_KEY：你的 Langfuse 项目公钥LANGFUSE_PRIVATE_KEY：您的 Langfuse 项目私钥
用法
作为 CLI 工具运行
# Set environment variables
export LANGFUSE_DOMAIN="https://api.langfuse.com"
export LANGFUSE_PUBLIC_KEY="your-public-key"
export LANGFUSE_PRIVATE_KEY="your-private
# Run the server
mcp-server-langfuse在代码中使用
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { langfuseClient } from "shouting-mcp-langfuse";
// Initialize the server and client
const server = new Server({...});
const langfuseClient = new LangfuseClient(process.env.LANGFUSE_DOMAIN, process.env.LANGFUSE_PUBLIC_KEY, process.env.LANGFUSE_PRIVATE_KEY);
// Register your custom handlers
// ...可用工具
服务器提供以下 langfuse 集成工具：
getLLMMetricsByTimeRange：按时间范围获取 LLM 指标
执照
国际学习中心
作者
存储库
Available Tools
1 toolquery_llm_metricsC
Query LLM metrics
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| fromTimestamp | No | Start timestamp in ISO 8601 format | |
| toTimestamp | No | End timestamp in ISO 8601 format | |
| page | No | Page number (default 1) | |
| limit | No | limit (default 100) | |
| traceName | No | Trace name | |
| userId | No | User ID, it's can filter by user | |
| tags | No | Tags | |
| environment | No | Environment |
TDQS
C2.7/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description does not disclose any behavioral traits such as side effects, permissions, rate limits, or data scope. It merely describes a query, which implies a read-only operation but does not explicitly confirm safety or other constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (3 words) but underspecified for a tool with 8 parameters. It lacks necessary detail and is not front-loaded with critical information. Conciseness should not sacrifice completeness.
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 8 parameters, no annotations, and no output schema, the description is insufficient. It does not explain what the metrics represent, how results are returned, or any context for interpreting the data. The agent is left without enough information to effectively use the tool.
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 coverage is 100%, so the baseline is 3. The description adds no meaning beyond the schema's parameter descriptions. It does not explain how to use parameters together or provide context that helps the agent understand parameter relationships.
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?
Description states 'Query LLM metrics' which clearly identifies the verb and resource. It is a simple, direct statement of the tool's purpose, though it lacks specificity about which metrics. It is not a tautology and distinguishes the tool as a query operation.
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?
No guidance provided on when to use this tool, what scenarios it is suited for, or any alternatives. The description gives no context for appropriate usage, leaving the agent without decision-making information.
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 tool update
- First observed
query_llm_metrics
TDQS
C2.8/5.0
Scored across 1 tool
Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap with other tools. The tool's purpose is clear and unambiguous.
Naming Consistency5/5
The single tool uses a clear snake_case naming convention. Although there are no other tools to compare, the naming is consistent within itself.
Tool Count1/5
A server named 'MCP Server for langfuse' with only one tool is severely undersized. Langfuse is a comprehensive LLM observability platform expecting tools for traces, scores, sessions, etc., not just metrics queries.
Completeness1/5
The server lacks any tools for creating, updating, or deleting resources, and does not cover common operations like managing traces or observations. It is severely incomplete for its stated domain.
Maintenance
ActivityInactive
ResponsivenessNo issues
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