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Logfire MCP Server

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Logfire MCP-Server

Dieses Repository enthält einen Model Context Protocol (MCP)-Server mit Tools, die auf die OpenTelemetry-Traces und -Metriken zugreifen können, die Sie an Logfire gesendet haben.

Dieser MCP-Server ermöglicht es LLMs, die Telemetriedaten Ihrer Anwendung abzurufen, verteilte Traces zu analysieren und die Ergebnisse beliebiger SQL-Abfragen zu nutzen, die mit den Logfire-APIs ausgeführt werden.

Verfügbare Tools

  • find_exceptions - Abfrage der Anzahl der Ausnahmen aus nach Dateien gruppierten Traces

    • Erforderliche Argumente:

      • age (int): Anzahl der Minuten, die zurückgeblickt werden soll (z. B. 30 für die letzten 30 Minuten, max. 7 Tage)

  • find_exceptions_in_file - Erhalten Sie detaillierte Trace-Informationen zu Ausnahmen in einer bestimmten Datei

    • Erforderliche Argumente:

      • filepath (Zeichenfolge): Pfad zur zu analysierenden Datei

      • age (int): Anzahl der Minuten, die zurückgeblickt werden soll (max. 7 Tage)

  • arbitrary_query - Führen Sie benutzerdefinierte SQL-Abfragen für Ihre OpenTelemetry-Traces und -Metriken aus

    • Erforderliche Argumente:

      • query (Zeichenfolge): Auszuführende SQL-Abfrage

      • age (int): Anzahl der Minuten, die zurückgeblickt werden soll (max. 7 Tage)

  • get_logfire_records_schema – Holen Sie sich das OpenTelemetry-Schema zur Unterstützung bei benutzerdefinierten Abfragen

    • Keine erforderlichen Argumente

Related MCP server: Observe MCP Server

Aufstellen

uv installieren

Stellen Sie zunächst sicher, dass uv installiert ist, da uv zum Ausführen des MCP-Servers verwendet wird.

Installationsanweisungen finden Sie in den uv Installationsdokumenten .

Wenn Sie bereits eine ältere Version von uv installiert haben, müssen Sie diese möglicherweise mit uv self update aktualisieren.

Erhalten Sie ein Logfire-Lese-Token

Um Anfragen an die Logfire-APIs zu stellen, benötigt der Logfire-MCP-Server ein „Lese-Token“.

Sie können eines im Abschnitt „Read Tokens“ Ihrer Projekteinstellungen in Logfire erstellen: https://logfire.pydantic.dev/-/redirect/latest-project/settings/read-tokens

[!WICHTIG] Logfire-Lesetoken sind projektspezifisch. Sie müssen daher eines für das spezifische Projekt erstellen, das Sie dem Logfire-MCP-Server zur Verfügung stellen möchten.

Manuelles Ausführen des Servers

Sobald Sie uv installiert haben und über ein Logfire-Lese-Token verfügen, können Sie den MCP-Server manuell mit uvx (das von uv bereitgestellt wird) ausführen.

Sie können Ihr Lesetoken mit der Umgebungsvariable LOGFIRE_READ_TOKEN angeben:

LOGFIRE_READ_TOKEN=YOUR_READ_TOKEN uvx logfire-mcp

oder mit dem Flag --read-token :

uvx logfire-mcp --read-token=YOUR_READ_TOKEN

[!NOTIZ]
Wenn Sie Cursor, Claude Desktop, Cline oder andere MCP-Clients verwenden, die Ihre MCP-Server für Sie verwalten, müssen Sie den Server NICHT manuell ausführen. Der nächste Abschnitt zeigt Ihnen, wie Sie diese Clients für die Nutzung des Logfire MCP-Servers konfigurieren.

Konfiguration mit bekannten MCP-Clients

Für Cursor konfigurieren

Erstellen Sie eine .cursor/mcp.json -Datei in Ihrem Projektstamm:

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

Der Cursor akzeptiert das env nicht, daher müssen Sie stattdessen das Flag --read-token verwenden.

Konfigurieren für Claude Desktop

Fügen Sie zu Ihren Claude-Einstellungen hinzu:

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

Konfigurieren für Cline

Fügen Sie Ihren Cline-Einstellungen in cline_mcp_settings.json Folgendes hinzu:

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

Anpassung – Basis-URL

Standardmäßig verbindet sich der Server mit der Logfire-API unter https://logfire-api.pydantic.dev . Sie können dies folgendermaßen überschreiben:

  1. Verwenden des Arguments --base-url :

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

  1. Festlegen der Umgebungsvariablen:

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

Beispielinteraktionen

  1. Suchen Sie alle Ausnahmen in den Spuren der letzten Stunde:

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

Antwort:

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

  1. Erhalten Sie Details zu Ausnahmen von Ablaufverfolgungen in einer bestimmten Datei:

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

Antwort:

[
  {
    "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"
  }
]

  1. Führen Sie eine benutzerdefinierte Abfrage für Spuren aus:

{
  "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
  }
}

Beispiele für Fragen an Claude

  1. „Welche Ausnahmen sind in den Traces der letzten Stunde über alle Dienste hinweg aufgetreten?“

  2. „Zeigen Sie mir die letzten Fehler in der Datei ‚app/api.py‘ mit ihrem Trace-Kontext.“

  3. „Wie viele Fehler gab es in den letzten 24 Stunden pro Dienst?“

  4. „Was sind die häufigsten Ausnahmetypen in meinen Traces, gruppiert nach Dienstnamen?“

  5. „Holen Sie mir das OpenTelemetry-Schema für Traces und Metriken.“

  6. "Finde alle Fehler von gestern und zeige ihre Trace-Kontexte an"

Erste Schritte

  1. Besorgen Sie sich zunächst ein Logfire-Lese-Token von: https://logfire.pydantic.dev/-/redirect/latest-project/settings/read-tokens

  2. Führen Sie den MCP-Server aus:

    uvx logfire-mcp --read-token=YOUR_TOKEN

  3. Konfigurieren Sie Ihren bevorzugten Client (Cursor, Claude Desktop oder Cline) anhand der obigen Konfigurationsbeispiele

  4. Beginnen Sie mit der Verwendung des MCP-Servers zur Analyse Ihrer OpenTelemetry-Traces und -Metriken!

Beitragen

Wir freuen uns über Beiträge zur Verbesserung des Logfire MCP-Servers. Egal, ob Sie neue Trace-Analyse-Tools hinzufügen, die Metrikabfragefunktion verbessern oder die Dokumentation verbessern möchten – Ihr Input ist wertvoll.

Beispiele für andere MCP-Server und Implementierungsmuster finden Sie im Repository der Model Context Protocol-Server .

Lizenz

Logfire MCP ist unter der MIT-Lizenz lizenziert. Das bedeutet, dass Sie die Software unter den Bedingungen der MIT-Lizenz frei verwenden, ändern und verbreiten können.

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
NameRequiredDescriptionDefault
queryYesThe query to run, as a SQL string.
ageYesNumber of minutes to look back, e.g. 30 for last 30 minutes. Maximum allowed value is 30 days.

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

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
NameRequiredDescriptionDefault
filepathYesThe path to the file to find exceptions in.
ageYesNumber of minutes to look back, e.g. 30 for last 30 minutes. Maximum allowed value is 30 days.

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

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.

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
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

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.

  1. 2 tool updatesv0.8.0
    • 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."
  2. 6 tool updatesv1.0.0
    • 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
  3. 4 tool updates
    • First observedarbitrary_query
    • First observedfind_exceptions
    • First observedfind_exceptions_in_file
    • First observedget_logfire_records_schema
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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

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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