F
licenseB
qualityNot graded
maintenanceA Model Context Protocol server that enables AI assistants to interact with Datadog's observability platform through natural language.
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Logfire MCP Server
Officialby pydantic
The Logfire MCP Server allows LLMs to retrieve and analyze OpenTelemetry traces and metrics data, providing insights into application telemetry. You can:
Find Exceptions: Retrieve exception counts grouped by file using
find_exceptionsGet Exception Details: Access detailed trace information about exceptions in specific files with
find_exceptions_in_fileRun Custom Queries: Execute arbitrary SQL queries on your OpenTelemetry data via
arbitrary_queryAccess Schema: View the OpenTelemetry schema to help with query construction using
get_logfire_records_schema
The server supports analysis of distributed traces and metrics for time periods up to 7 days.
Enables access and analysis of OpenTelemetry traces and metrics data stored in Logfire, with tools for finding exceptions, retrieving trace information, and executing SQL queries against telemetry data.
Integrates with Logfire, a Pydantic service, to retrieve and analyze application telemetry data through the Logfire APIs using read tokens from the Logfire project settings.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Logfire MCP Serverfind the 10 most recent exceptions in app.py from the last hour"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Pydantic Logfire MCP Server
IMPORTANT
This repository is archived. The STDIO MCP server in this package is no longer being updated. We now have a remote MCP server, which allows us to iterate faster on tools and provide a better experience.
Read more in our documentation.
If you have any questions, reach out to us on Slack or email us at engineering@pydantic.dev.
Available Tools
4 toolsarbitrary_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.
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.
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