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Glama
pydantic

Logfire MCP Server

Official
by pydantic
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
LOGFIRE_BASE_URLNoThe base URL of the Logfire API instancehttps://logfire-api.pydantic.dev
LOGFIRE_READ_TOKENYesYour Logfire read token for accessing the Logfire APIs

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
find_exceptions_in_fileA

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

arbitrary_queryB

Run an arbitrary query on the Pydantic Logfire database.

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

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

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.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4/5.0

Scored across 4 tools

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

ActivityStale
ResponsivenessNo issues

Resources