Skip to main content
Glama
Log-LogN

langfuse-mcp-java

get_scores

get_scores
Destructive

Retrieve evaluation scores from Langfuse with filters for trace, observation, name, data type, and timestamp to analyze LLM application performance.

Instructions

List evaluation scores with optional filters. dataType values: NUMERIC | CATEGORICAL | BOOLEAN. Returns: id, traceId, observationId, name, value, dataType, comment, source. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageYesPage number
limitYesItems per page, max 100
traceIdYesFilter by trace ID
observationIdYesFilter by observation ID
nameYesFilter by score name
dataTypeYesFilter by type: NUMERIC | CATEGORICAL | BOOLEAN
fromTimestampYesISO-8601 start timestamp
toTimestampYesISO-8601 end timestamp
Behavior1/5

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

Critical contradiction: description states 'Read-only' but annotations specify 'readOnlyHint: false' and 'destructiveHint: true', implying data destruction. While it lists return fields ('Returns: id, traceId...'), the safety profile contradiction makes this actively misleading.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Efficiently structured with four discrete lines covering purpose, parameter hint, return values, and safety. However, two of these lines contain false information ('optional', 'Read-only'), meaning the brevity trades off with accuracy.

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?

With 8 required parameters and no output schema, the tool is moderately complex. The description lists return fields, which helps, but fails to explain why all filters are required (unusual for a list endpoint), pagination mechanics, or the destructive behavior indicated by annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, establishing a baseline of 3, but the description misleadingly claims filters are 'optional' when all 8 parameters are required. It repeats the dataType enum values already present in the schema ('NUMERIC | CATEGORICAL | BOOLEAN') without adding syntax clarification or explaining the unusual 'all required' constraint.

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?

States specific verb+resource ('List evaluation scores') clearly. However, fails to distinguish from sibling tool 'get_score' (singular vs plural), and inaccurately describes parameters as 'optional' when the schema marks all 8 as required.

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?

Lacks explicit guidance on when to use this vs 'get_score' or other alternatives. The phrase 'with optional filters' provides minimal implicit guidance, but contradicts the schema's required parameter constraint and gives no sense of pagination usage or filter combination behavior.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Log-LogN/langfuse-mcp-java'

If you have feedback or need assistance with the MCP directory API, please join our Discord server