Skip to main content
Glama

@arizeai/phoenix-mcp

Official
by Arize-ai
README.md1.69 kB
# Data Generation Scripts This directory contains scripts for generating synthetic trace data in PostgreSQL. ## Getting Started Generate spans: ```bash python generate_spans.py --num-batches 10 --traces-per-batch 100 ``` Add annotations to spans: ```bash python generate_span_annotations.py ``` Check table sizes for `spans`, `traces`, and `projects`: ```bash python report_spans_table_sizes.py ``` ## Sample Queries to Run after Data is Generated ### Select Random Conversation ID ```sql SELECT (attributes->'metadata'->>'conversation_id')::uuid as conversation_id FROM spans TABLESAMPLE SYSTEM (1) WHERE attributes->'metadata'->>'conversation_id' IS NOT NULL LIMIT 1; ``` ### Select Spans by Conversation ID ```sql SELECT * FROM spans WHERE attributes->'metadata'->>'conversation_id' = '123e4567-e89b-12d3-a456-426614174000' ORDER BY start_time; ``` ### Show Spans Table Size Stats ```sql SELECT pg_size_pretty(pg_total_relation_size('spans')) as total_size, pg_size_pretty(pg_relation_size('spans')) as table_size, pg_size_pretty(pg_total_relation_size('spans') - pg_relation_size('spans')) as index_size, (SELECT count(*) FROM spans) as row_count, (SELECT last_value FROM spans_id_seq) as current_sequence; ``` ## Files - `generate_spans.sql` - Generates spans with realistic timing and metadata - `generate_spans.py` - Python wrapper to run generate_spans.sql in batches - `generate_span_annotations.sql` - Adds annotations to existing spans - `generate_span_annotations.py` - Python wrapper for span annotations - `report_spans_table_sizes.sql` - Reports table sizes and row counts - `report_spans_table_sizes.py` - Python wrapper for table size reporting

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/Arize-ai/phoenix'

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