phoenix-mcp-eval
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_projectsA | List all Phoenix tracing projects. |
| get_tracesB | Retrieve LLM traces for a project with optional filters. |
| get_spansA | Get individual spans with input, output, and latency data. |
| list_datasetsB | List evaluation datasets in Phoenix. |
| get_datasetA | Fetch dataset examples for review or eval comparison. |
| list_evaluationsA | List evaluation runs and their scores for a project. |
| get_evaluation_summaryB | Get aggregated evaluation metrics (mean score, pass rate, etc.). |
| query_tracesC | Run a structured query over trace data by time range or metadata filter. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 8 tools
Most tools have clearly distinct purposes: list vs get vs query operations are separated, and each targets a different entity (datasets, evaluations, traces, spans, projects). The main potential confusion is between get_traces and query_traces, though descriptions clarify query_traces as a structured filtering alternative.
All tool names follow the verb_noun pattern consistently: get_ for single entities, list_ for collections, and query_ for the structured trace query. No style mixing or vague verbs; the naming is predictable and uniform.
Eight tools provide a focused and well-scoped surface for a Phoenix evaluation/tracing server. Each tool addresses a distinct need without redundancy or bloat, fitting squarely in the ideal range.
The server covers the core read-only interactions with datasets, evaluations, traces, spans, and projects, including both list and detail views. Notable gaps include no tool to fetch a specific evaluation run's detailed results or a specific project's configuration, but these are minor for a query-focused eval server.