Pg Aiguide vs MCP Victorialogs
A side-by-side look at the Pg Aiguide and MCP Victorialogs MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Pg Aiguide for databases work over a hosted endpoint; choose MCP Victorialogs for databases over a hosted endpoint. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Pg Aiguide when
- You need dedicated capabilities in the Databases domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
search_docs, view_skill.
Choose MCP Victorialogs when
- You need dedicated capabilities in the Databases domain.
- You prefer a remote HTTP/SSE transport.
- Its auth model fits you: No auth required.
- You have the keys it needs:
VL_INSTANCE_ENTRYPOINT, VL_INSTANCE_BEARER_TOKEN, VL_INSTANCE_HEADERS, MCP_PASSTHROUGH_HEADERS, VL_DEFAULT_TENANT_ID, MCP_SERVER_MODE, MCP_LISTEN_ADDR. - You want tools like
documentation, facets, field_names.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Databases | Databases |
| Quality signal | 63/100 · Good | 64/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Remote HTTP/SSE | Remote HTTP/SSE |
| Auth | Not declared | No auth required |
| Pricing | Not declared | Free |
| Required env vars | None listed | VL_INSTANCE_ENTRYPOINTVL_INSTANCE_BEARER_TOKENVL_INSTANCE_HEADERSMCP_PASSTHROUGH_HEADERSVL_DEFAULT_TENANT_IDMCP_SERVER_MODEMCP_LISTEN_ADDR |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1,872 | 104 |
| Directory activity | 0 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 2 | 13 |
| Full listing | View Pg Aiguide | View MCP Victorialogs |
Tools & capabilities
Pg Aiguide tools 2
search_docsSearch documentation with hybrid semantic (vector) and keyword (BM25) search. Use semanticWeight to choose keyword-only (0), semantic-only (1), or a blend; mid values fuse rankings with RRF. Supports Tiger Cloud (TimescaleDB), PostgreSQL, and PostGIS.view_skillRetrieve detailed skills for TimescaleDB operations and best practices. ## Available Skills <available_skills> [11 ]{name description}: design-postgis-tables Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications design-postgres-tables "Use this skill for general PostgreSQL table design.\n\n**Trigger when user asks to:**\n- Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones.\n- Choose data types, constraints, or indexes for PostgreSQL\n- Create user tables, order tables, reference tables, or JSONB schemas\n- Understand PostgreSQL best practices for normalization, constraints, or indexing\n- Design update-heavy, upsert-heavy, or OLTP-style tables\n\n\n**Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security\n\nComprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.\n" find-hypertable-candidates "Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.\n\n**Trigger when user asks to:**\n- Analyze database tables for hypertable conversion potential\n- Identify time-series or event tables in an existing schema\n- Evaluate if a table would benefit from Timescale/TimescaleDB\n- Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData\n- Score or rank tables for hypertable candidacy\n\n\n**Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables\n\nProvides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.\n" migrate-postgres-tables-to-hypertables "Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation.\n\n**Trigger when user asks to:**\n- Migrate or convert PostgreSQL tables to hypertables\n- Execute hypertable migration with minimal downtime\n- Plan blue-green migration for large tables\n- Validate hypertable migration success\n- Configure compression after migration\n\n**Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed)\n\n**Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup\n\nStep-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.\n" pgvector-semantic-search "Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.\n\n**Trigger when user asks to:**\n- Store or search vector embeddings in PostgreSQL\n- Set up semantic search, similarity search, or nearest neighbor search\n- Create HNSW or IVFFlat indexes for vectors\n- Implement RAG (Retrieval Augmented Generation) with PostgreSQL\n- Optimize pgvector performance, recall, or memory usage\n- Use binary quantization for large vector datasets\n\n**Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search\n\nCovers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.\n" postgres "Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations.\n\n**Trigger when user asks to:**\n- Explore an existing PostgreSQL database to understand its objects and relationships\n- Design or modify PostgreSQL tables, schemas, or data models\n- Choose data types, constraints, indexes, or partitioning strategies\n- Work with pgvector embeddings, semantic search, or RAG\n- Set up full-text search, hybrid search, or BM25 ranking\n- Use PostGIS for spatial/geographic data\n- Set up TimescaleDB hypertables for time-series data\n- Migrate tables to hypertables or evaluate migration candidates\n- Plan or execute safe schema migrations with zero downtime\n\n**Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series, migration\n" postgres-database-migration "Use this skill for planning, testing, and safely executing PostgreSQL schema migrations — especially when working with production data or shared databases.\n\n**Trigger when user asks to:**\n- Test a schema migration before applying it to production\n- Add, remove, or rename columns safely on a live table\n- Change a column's data type without downtime\n- Add or drop indexes, constraints, or foreign keys on large tables\n- Understand which ALTER TABLE operations lock the table\n- Roll back a failed migration\n- Plan a zero-downtime migration strategy\n- Fork a database to test a migration safely\n\n**Keywords:** migration, schema change, ALTER TABLE, add column, drop column, rename column, change type, zero downtime, lock, AccessExclusiveLock, concurrent index, forking, rollback, backfill, deploy\n\nCovers: lock-level reference for every common DDL operation, safe migration patterns, fork-based testing, zero-downtime column changes, index creation, constraint addition, backfill strategies, pre/post-migration validation, and rollback planning.\n" postgres-hybrid-text-search "Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF).\n\n**Trigger when user asks to:**\n- Combine keyword and semantic search\n- Implement hybrid search or multi-modal retrieval\n- Use BM25/pg_textsearch with pgvector together\n- Implement RRF (Reciprocal Rank Fusion) for search\n- Build search that handles both exact terms and meaning\n\n\n**Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder\n\nCovers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.\n" schema-exploration "Explore an existing PostgreSQL database before answering questions about its data or writing SQL. Use this skill whenever a user asks for a query or a data-backed answer against an unfamiliar schema (counts, missing or failed records, recent changes), asks where a business concept lives, or asks how tables, joins, views, routines, triggers, RLS, or extensions work. Find the relevant objects with read-only pg_catalog queries, then request approval before inspecting data-derived statistics or rows. Not a schema-design or migration guide.\n" setup-timescaledb-hypertables "Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table.\n\n**Trigger when user asks to:**\n- Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available\n- Set up hypertables, compression, retention policies, or continuous aggregates\n- Configure partition columns, segment_by, order_by, or chunk intervals\n- Optimize time-series database performance or storage\n- Create tables for sensors, metrics, telemetry, events, or transaction logs\n\n**Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by\n\nStep-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.\n" timescaledb-hyperfunctions "Use this skill when writing analytical SQL over time-series data with the TimescaleDB Toolkit (timescaledb_toolkit extension) hyperfunctions: approximate percentiles, statistical summaries, time-weighted averages, counter/gauge rates, uptime/heartbeat tracking, state durations, OHLC candlesticks, approximate distinct counts, top-N, and downsampling.\n\n**Trigger when user asks to:**\n- Compute percentiles/medians/p95/p99 over large or rolled-up time-series data\n- Compute rates or deltas from monotonic counters (Prometheus-style) or gauges\n- Compute time-weighted averages or integrals over irregularly sampled data\n- Track uptime/downtime from heartbeats, or time spent in each state\n- Build OHLC/candlestick or VWAP data for financial ticks\n- Store re-aggregatable summaries in continuous aggregates (two-step aggregation, rollup)\n- Approximate COUNT DISTINCT, find top-N / most frequent values, or downsample for charts\n\n**Keywords:** timescaledb_toolkit, hyperfunctions, percentile_agg, uddsketch, tdigest, approx_percentile, stats_agg, time_weight, counter_agg, gauge_agg, heartbeat_agg, state_agg, candlestick_agg, hyperloglog, approx_count_distinct, min_n, max_n, mcv_agg, lttb, asap_smooth, rollup, two-step aggregation\n" </available_skills>
MCP Victorialogs tools 13
documentationSearch documentation resources for the given search query, returning the URIs of the resources that match the search criteria sorted by relevance. This tool can help to get context for any VictoriaLogs related question.facetsThe most frequent values per each log field seen in the logs returned by the given <query> on the given [<start> ... <end>] time range. This tool uses `/select/logsql/facets` endpoint of VictoriaLogs API.field_namesGet field names from results of the given <query> on the given [<start> ... <end>] time range. The response also contains the number of log results per every field name. This tool uses `/select/logsql/field_names` endpoint of VictoriaLogs API.field_valuesGet unique values for the given <fieldName> field from results of the given <query> on the given [<start> ... <end>] time range. The response also contains the number of log results per every field value. This tool uses `/select/logsql/field_values` endpoint of VictoriaLogs API.flagsList of non-default flags (parameters) of the VictoriaLogs instance. This tools uses `/flags` endpoint of VictoriaLogs API.hitsThe number of matching log entries for the given <query> on the given [<start> ... <end>] time range grouped by <step> buckets. The returned results are sorted by time. This tool uses `/select/logsql/hits` endpoint of VictoriaLogs API.queryExecutes LogsQL query expression to search log entries. This tool uses `/select/logsql/query` endpoint of VictoriaLogs API.stats_queryLog stats for the given query at the given timestamp (time) in the format compatible with Prometheus querying API. This tool uses `/select/logsql/stats_query` endpoint of VictoriaLogs API.stats_query_rangeLog stats for the given query on the given [start ... end] time range with the given step interval. The stats is returned in the format compatible with Prometheus querying API. This tool uses `/select/logsql/stats_query_range` endpoint of VictoriaLogs API.stream_field_namesGet log stream field names from results of the given <query> on the given [<start> ... <end>] time range. The response also contains the number of log results per every field name.. This tool uses `/select/logsql/stream_field_names` endpoint of VictoriaLogs API.stream_field_valuesGet unique values for the given <fieldName> field from results of the given <query> on the given [<start> ... <end>] time range. The response also contains the number of log results per every field value. This tool uses `/select/logsql/stream_field_values` endpoint of VictoriaLogs API.stream_idsGet _stream_id values for the log streams from results of the given <query> on the given [<start> ... <end>] time range. The response also contains the number of log results per every _stream_id. This tool uses `/select/logsql/stream_ids` endpoint of VictoriaLogs API.- +1 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Pg Aiguide
{
"mcpServers": {
"pg-aiguide": {
"url": "https://mcp.tigerdata.com/docs"
}
}
}
MCP Victorialogs
{
"mcpServers": {
"victoriametrics-community-mcp-victorialogs": {
"url": "https://play-vmlogs-mcp.victoriametrics.com/mcp"
}
}
}
Frequently asked questions
Pg Aiguide is categorized under Databases and uses a remote HTTP/SSE transport. MCP Victorialogs belongs to Databases and uses a remote HTTP/SSE transport. Pick Pg Aiguide for databases work and MCP Victorialogs for databases.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Pg Aiguide has no declared pricing — check the project's own site. MCP Victorialogs is listed as free. Auth: no auth required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add pg-aiguide and victoriametrics-community-mcp-victorialogs as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Pg Aiguide has a quality signal of 63/100 (Good) with 0 views, 0 installs and 1,872 GitHub stars. MCP Victorialogs has 64/100 (Good) with 1 views, 0 installs and 104 GitHub stars.