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Logfire MCP Server

Name: Logfire MCP Server
Author: pydantic

Official

by pydantic

Overview Schema Related Servers Score Discussions

Python

Remote

Logfire MCP 서버

이 저장소에는 Logfire에 보낸 OpenTelemetry 추적 및 메트릭에 액세스할 수 있는 도구가 포함된 MCP(Model Context Protocol) 서버가 포함되어 있습니다.

이 MCP 서버를 사용하면 LLM이 애플리케이션의 원격 측정 데이터를 검색하고, 분산 추적을 분석하고, Logfire API를 사용하여 실행한 임의의 SQL 쿼리의 결과를 활용할 수 있습니다.

사용 가능한 도구

find_exceptions - 파일별로 그룹화된 추적에서 예외 수 가져오기
- 필수 인수:
  - age (int): 뒤돌아볼 시간(분)(예: 마지막 30분은 30분, 최대 7일)
find_exceptions_in_file - 특정 파일의 예외에 대한 자세한 추적 정보를 가져옵니다.
- 필수 인수:
  - filepath (문자열): 분석할 파일의 경로
  - age (int): 뒤돌아볼 시간(분)(최대 7일)
arbitrary_query - OpenTelemetry 추적 및 메트릭에 대한 사용자 지정 SQL 쿼리 실행
- 필수 인수:
  - query (문자열): 실행할 SQL 쿼리
  - age (int): 뒤돌아볼 시간(분)(최대 7일)
get_logfire_records_schema - 사용자 정의 쿼리에 도움이 되는 OpenTelemetry 스키마를 가져옵니다.
- 필수 인수 없음

Related MCP server: Observe MCP Server

설정

`uv` 설치

가장 먼저 해야 할 일은 uv 설치되어 있는지 확인하는 것입니다. uv MCP 서버를 실행하는 데 사용됩니다.

설치 지침은 uv 설치 문서를 참조하세요.

이미 이전 버전의 uv 설치되어 있는 경우 uv self update 로 업데이트해야 할 수도 있습니다.

Logfire 읽기 토큰을 얻으세요

Logfire API에 요청을 하려면 Logfire MCP 서버에 "읽기 토큰"이 필요합니다.

Logfire의 프로젝트 설정에서 "토큰 읽기" 섹션에서 토큰을 하나 만들 수 있습니다: https://logfire.pydantic.dev/-/redirect/latest-project/settings/read-tokens

[!중요] Logfire 읽기 토큰은 프로젝트별로 다르므로 Logfire MCP 서버에 공개하려는 특정 프로젝트에 대한 토큰을 만들어야 합니다.

서버를 수동으로 실행하세요

uv 설치하고 Logfire 읽기 토큰이 있으면 uvx ( uv 에서 제공)를 사용하여 MCP 서버를 수동으로 실행할 수 있습니다.

LOGFIRE_READ_TOKEN 환경 변수를 사용하여 읽기 토큰을 지정할 수 있습니다.

지엑스피1

또는 --read-token 플래그를 사용합니다.

uvx logfire-mcp --read-token=YOUR_READ_TOKEN

[!메모]
Cursor, Claude Desktop, Cline 또는 MCP 서버를 자동으로 관리해 주는 다른 MCP 클라이언트를 사용하는 경우, 서버를 직접 수동으로 실행할 필요가 없습니다 . 다음 섹션에서는 이러한 클라이언트가 Logfire MCP 서버를 사용하도록 구성하는 방법을 보여줍니다.

잘 알려진 MCP 클라이언트를 사용한 구성

커서 구성

프로젝트 루트에 .cursor/mcp.json 파일을 만듭니다.

{
  "mcpServers": {
    "logfire": {
      "command": "uvx",
      "args": ["logfire-mcp", "--read-token=YOUR-TOKEN"]
    }
  }
}

커서는 env 필드를 허용하지 않으므로 대신 --read-token 플래그를 사용해야 합니다.

Claude Desktop 구성

Claude 설정에 추가:

{
  "command": ["uvx"],
  "args": ["logfire-mcp"],
  "type": "stdio",
  "env": {
    "LOGFIRE_READ_TOKEN": "YOUR_TOKEN"
  }
}

Cline에 대한 구성

cline_mcp_settings.json 의 Cline 설정에 다음을 추가합니다.

{
  "mcpServers": {
    "logfire": {
      "command": "uvx",
      "args": ["logfire-mcp"],
      "env": {
        "LOGFIRE_READ_TOKEN": "YOUR_TOKEN"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

사용자 정의 - 기본 URL

기본적으로 서버는 https://logfire-api.pydantic.dev 의 Logfire API에 연결됩니다. 다음과 같이 이 설정을 재정의할 수 있습니다.

--base-url 인수를 사용합니다.

uvx logfire-mcp --base-url=https://your-logfire-instance.com

환경 변수 설정:

LOGFIRE_BASE_URL=https://your-logfire-instance.com uvx logfire-mcp

예시 상호작용

지난 1시간 동안의 추적에서 모든 예외를 찾습니다.

{
  "name": "find_exceptions",
  "arguments": {
    "age": 60
  }
}

응답:

[
  {
    "filepath": "app/api.py",
    "count": 12
  },
  {
    "filepath": "app/models.py",
    "count": 5
  }
]

특정 파일의 추적에서 예외에 대한 세부 정보를 가져옵니다.

{
  "name": "find_exceptions_in_file",
  "arguments": {
    "filepath": "app/api.py",
    "age": 1440
  }
}

응답:

[
  {
    "created_at": "2024-03-20T10:30:00Z",
    "message": "Failed to process request",
    "exception_type": "ValueError",
    "exception_message": "Invalid input format",
    "function_name": "process_request",
    "line_number": "42",
    "attributes": {
      "service.name": "api-service",
      "code.filepath": "app/api.py"
    },
    "trace_id": "1234567890abcdef"
  }
]

추적에 대한 사용자 정의 쿼리를 실행합니다.

{
  "name": "arbitrary_query",
  "arguments": {
    "query": "SELECT trace_id, message, created_at, attributes->>'service.name' as service FROM records WHERE severity_text = 'ERROR' ORDER BY created_at DESC LIMIT 10",
    "age": 1440
  }
}

클로드에 대한 질문의 예

"모든 서비스의 지난 1시간 동안 추적에서 어떤 예외가 발생했습니까?"
"'app/api.py' 파일에서 발생한 최근 오류와 해당 추적 컨텍스트를 보여주세요"
"지난 24시간 동안 서비스당 오류가 몇 개 있었나요?"
"서비스 이름별로 그룹화된 추적에서 가장 일반적인 예외 유형은 무엇입니까?"
"추적 및 메트릭에 대한 OpenTelemetry 스키마를 가져오세요"
"어제의 모든 오류를 찾아 해당 추적 컨텍스트를 표시합니다."

시작하기

먼저 https://logfire.pydantic.dev/-/redirect/latest-project/settings/read-tokens 에서 Logfire 읽기 토큰을 얻으세요.
MCP 서버를 실행합니다.
```
uvx logfire-mcp --read-token=YOUR_TOKEN
```
위의 구성 예를 사용하여 선호하는 클라이언트(Cursor, Claude Desktop 또는 Cline)를 구성하세요.
MCP 서버를 사용하여 OpenTelemetry 추적 및 메트릭을 분석해 보세요!

기여하다

Logfire MCP 서버 개선을 위한 여러분의 참여를 환영합니다. 새로운 추적 분석 도구 추가, 메트릭 쿼리 기능 향상, 문서 개선 등 어떤 목적이든 여러분의 의견은 소중합니다.

다른 MCP 서버와 구현 패턴의 예는 Model Context Protocol 서버 저장소를 참조하세요.

특허

Logfire MCP는 MIT 라이선스에 따라 라이선스가 부여됩니다. 즉, MIT 라이선스의 약관에 따라 소프트웨어를 자유롭게 사용, 수정 및 배포할 수 있습니다.

Available Tools

4 tools

arbitrary_queryB

Run an arbitrary query on the Pydantic Logfire database.

The SQL reference is available via the `sql_reference` tool.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes	The query to run, as a SQL string.
`age`	Yes	Number of minutes to look back, e.g. 30 for last 30 minutes. Maximum allowed value is 30 days.

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.4/5.0

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 fails to disclose behavioral traits such as potential for destructive actions, permissions, rate limits, or what happens on error. Given the power of arbitrary SQL, this is insufficient.

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 two sentences: the first states the purpose concisely, the second points to a related tool for SQL reference. It is front-loaded and every sentence adds value.

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?

Despite having an output schema, the description lacks important context for an arbitrary query tool, such as safety considerations, read-only vs write capability, or behavior on failure. It is not complete enough for safe usage.

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 input schema already documents both parameters (query string and age integer). The description does not add any extra meaning beyond what the schema provides, hence a baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Run an arbitrary query on the Pydantic Logfire database,' with a specific verb and resource. It distinguishes from siblings like find_exceptions_in_file, logfire_link, and schema_reference.

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

Usage Guidelines3/5

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

The description mentions that SQL reference is available via the sql_reference tool, implying a prerequisite. However, it does not explicitly state when to use this tool vs alternatives or provide exclusions, so guidance is implied but not explicit.

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

find_exceptions_in_fileA

Get the details about the 10 most recent exceptions on the file.

ParametersJSON Schema

Name	Required	Description	Default
`filepath`	Yes	The path to the file to find exceptions in.
`age`	Yes	Number of minutes to look back, e.g. 30 for last 30 minutes. Maximum allowed value is 30 days.

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.5/5.0

Behavior2/5

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

No annotations provided; description does not reveal behavioral traits such as read-only nature, side effects, or permissions. Only implies retrieval but lacks explicit assurance.

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?

Single concise sentence with no filler, front-loaded with key action and result. Every word serves purpose.

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?

For a simple tool with schema documentation and output schema, description is adequate but lacks completeness on sorting of 'most recent' or interaction between age and filepath.

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?

Input schema has 100% coverage; description adds nuance '10 most recent' beyond schema, but does not detail age interpretation or other edge cases.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'get' and resource '10 most recent exceptions on the file', distinguishing it from siblings like 'arbitrary_query' and 'logfire_link'.

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 on when to use this tool versus alternatives, nor any conditions or exclusions. The description merely states function.

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

logfire_linkA

Creates a link to help the user to view the trace in the Logfire UI.

ParametersJSON Schema

Name	Required	Description	Default
`trace_id`	Yes	The trace ID to link to.

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.8/5.0

Behavior3/5

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

No annotations provided, so description carries full burden. It indicates a non-destructive action, but does not disclose return format, side effects, or permissions. Minimal disclosure beyond the action.

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?

Single sentence, front-loaded purpose, zero unnecessary words. Efficient for an agent to parse.

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 single parameter and output schema (not shown but indicated as present), description covers basic usage. Could mention return value format (e.g., URL) but not critical since output schema likely covers it.

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% with the parameter 'trace_id' already described. The description 'trace ID to link to' adds no new information beyond the schema, meeting the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states verb (creates), resource (link), and purpose (view trace in Logfire UI). Distinguishes from sibling tools like arbitrary_query and find_exceptions_in_file.

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

Usage Guidelines3/5

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

Description implies usage context (when you have a trace_id and want a UI link), but no explicit when-to-use or when-not-to-use guidance. No mention of alternatives.

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

schema_referenceA

The database schema for the Logfire DataFusion database.

This includes all tables, columns, and their types as well as descriptions.
For example:

```sql
-- The records table contains spans and logs.
CREATE TABLE records (
    message TEXT, -- The message of the record
    span_name TEXT, -- The name of the span, message is usually templated from this
    trace_id TEXT, -- The trace ID, identifies a group of spans in a trace
    exception_type TEXT, -- The type of the exception
    exception_message TEXT, -- The message of the exception
    -- other columns...
);
```
The SQL syntax is similar to Postgres, although the query engine is actually Apache DataFusion.

To access nested JSON fields e.g. in the `attributes` column use the `->` and `->>` operators.
You may need to cast the result of these operators e.g. `(attributes->'cost')::float + 10`.

You should apply as much filtering as reasonable to reduce the amount of data queried.
Filters on `start_timestamp`, `service_name`, `span_name`, `metric_name`, `trace_id` are efficient.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.2/5.0

Behavior4/5

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 SQL syntax is similar to Postgres but uses Apache DataFusion, explains how to access nested JSON, and advises on efficient filtering. No destructive actions are mentioned, which is appropriate for a read-only schema tool.

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 front-loaded with the purpose and provides detailed examples. While the SQL example takes space, it is relevant and informative. Could be slightly more concise, but overall well-structured.

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

Completeness5/5

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

Given the tool's purpose (providing schema), the description covers all necessary context: database type, SQL dialect, nested JSON access, and filtering advice. The output schema exists, so return values need not be detailed further.

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 input schema has 0 parameters and 100% schema_description_coverage, so baseline is 4. The description adds value by explaining SQL syntax and operators for querying nested data, which aids in interpreting the schema output.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states that the tool provides the database schema for the Logfire DataFusion database, including tables, columns, types, and descriptions. This is a specific verb+resource combination that clearly distinguishes its purpose.

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

Usage Guidelines3/5

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

The description implies that this tool is used to understand the schema for crafting queries, but it does not explicitly state when to use it versus alternatives like arbitrary_query. No direct exclusions or alternative tool names are mentioned.

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. Dates show when Glama detected each change.

2 tool updatesv0.8.0May 4, 2026

Changedarbitrary_query1 field changed

changedInput schema / properties / age / description

Previous value: -"Number of minutes to look back, e.g. 30 for last 30 minutes. Maximum allowed value is 7 days."New value: +"Number of minutes to look back, e.g. 30 for last 30 minutes. Maximum allowed value is 30 days."

Changedfind_exceptions_in_file1 field changed

changedInput schema / properties / age / description

Previous value: -"Number of minutes to look back, e.g. 30 for last 30 minutes. Maximum allowed value is 7 days."New value: +"Number of minutes to look back, e.g. 30 for last 30 minutes. Maximum allowed value is 30 days."

6 tool updatesv1.0.0Jan 3, 2026

Changedarbitrary_query3 fields changed

addedInput schema / properties / age / description

Added value: +"Number of minutes to look back, e.g. 30 for last 30 minutes. Maximum allowed value is 7 days."

addedInput schema / properties / query / description

Added value: +"The query to run, as a SQL string."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "result": {
+      "items": {},
+      "title": "Result",
+      "type": "array"
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "title": "arbitrary_queryOutput",
+  "type": "object"
+}

Removedfind_exceptions

Changedfind_exceptions_in_file3 fields changed

addedInput schema / properties / age / description

Added value: +"Number of minutes to look back, e.g. 30 for last 30 minutes. Maximum allowed value is 7 days."

addedInput schema / properties / filepath / description

Added value: +"The path to the file to find exceptions in."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "result": {
+      "items": {},
+      "title": "Result",
+      "type": "array"
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "title": "find_exceptions_in_fileOutput",
+  "type": "object"
+}

Removedget_logfire_records_schema
Addedlogfire_link
Addedschema_reference

4 tool updatesMay 8, 2025
- First observedarbitrary_query
- First observedfind_exceptions
- First observedfind_exceptions_in_file
- First observedget_logfire_records_schema

Install Server

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TDQS

A4/5.0

Disambiguation5/5

Each tool serves a unique purpose: querying, exception viewing, link generation, and schema reference. No overlap or ambiguity.

Naming Consistency5/5

All tools use consistent snake_case naming with clear verbs (arbitrary_query, find_exceptions_in_file, logfire_link, schema_reference).

Tool Count5/5

With 4 tools, the set is concise and well-scoped for querying and debugging Logfire databases, covering key workflows without bloat.

Completeness4/5

The set covers querying, schema exploration, exception analysis, and UI linking. Missing explicit write operations, but that may be by design.

Maintenance

ActivitySlowing

ResponsivenessSyncing

Resources

GitHub Repository

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quality
D
maintenance
Model Context Protocol server for Langfuse observability. Query traces, analyze accuracy, detect failures, track costs, debug latency, manage prompts and datasets.
Updated 5 months ago (2026-04-21 14:50 UTC)
58
2
MIT
MLflow MCP Server
AI & Machine Learning Developer Tools
kkruglik
A
license
A
quality
A
maintenance
A Model Context Protocol server that enables LLMs to interact with MLflow tracking servers, allowing users to query experiments, analyze runs, compare metrics, manage the model registry, and promote models through natural language.
Updated a month ago (2026-08-08 13:03 UTC)
40
15
MIT

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