SQL Benchmarks Lab

Query pre-computed SQL engine benchmarks.

Connector by rctruta · GitHub account verified by the MCP Registry

IndexedApache-2.0Local (stdio)★ 2

Install

Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/rctruta-sqlbenchdag

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Query pre-computed SQL engine benchmarks. Runs standalone, no server setup required.

Reviews

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Permissions

What this server can do and what it asks for, from its tool list, install config and code. Checked Oct 25, 2026.

Tools: 9 read · 1 write · 0 delete
Secrets: None asked for
Install scripts: None found
Runs as: Python package

Tools by what they do 10

Read from the source code; the running server may list different tools.

Read 9 · Look things up without changing anything.

analyze_experiment
get_experiment_status
get_template
list_categories
list_engines
list_results
list_suites
list_templates
recommend_engine

Write 1 · Create, change, send or run things.

submit_experiment

Secrets and settings it asks for

Other settings (environment variables or headers).

SB_API_BASE
SB_RESULTS_DIR

Network hosts it talks to 3

astral.sh · found in code
github.com · found in code
sqlbench.local · found in code

Files it touches

~/.ssh/benchmark-key-pair.pem
/mnt/data
/opt/Actian/VectorVW/ingres/bin/sql
/opt/Actian/VectorVW/ingres/bin/vwload
/root/sql-benchmarks-dagster
/tmp/a.parquet
/tmp/b.parquet
/tmp/benchmark_data
/tmp/benchmark_test_data
/tmp/data
/tmp/data/duckdb_isolation
/tmp/duckdb_data
/tmp/q
/tmp/s.parquet
/tmp/sc.parquet
/tmp/smoke_mcp_stderr.log
/tmp/test_data
/tmp/test_duckdb_data
/tmp/test.parquet
/tmp/x.parquet
/Users/ramona/Downloads/production_customers.csv
/Users/ramona/Downloads/production_orders.csv
/var/lib/postgresql/data
/var/lib/typedb/data
Your home directory (~)
~/.dagster
~/Projects/ai-agent-utils/templates/gates

Tools (10)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

analyze_experiment
Analyze a completed experiment. Choose the right intent for your question. Intents: - "summary": Compact digest (means + scaling + narrative). Start here. - "means": Mean duration per partition/engine. - "scaling": Pairwise scaling factors across partitions. - "stability": Std dev, min, max, CV for reliability questions. - "compare": Ranked cross-engine comparison. (Can filter by `partition`). - "compare_by_partition": Ranked comparison broken down per partition. - "raw": The full raw JSON payload (use ONLY if projections aren't enough). Args: experiment_id: 8-character experiment ID. …
get_experiment_status
Check the status of a submitted experiment. Status values: "queued", "running", "complete", "not_found". Args: experiment_id: 8-character experiment ID returned by submit_experiment().
get_template
Return the raw YAML content of a template by name. Args: name: Name of the template from list_templates().
list_categories
List the category taxonomy to narrow down test suites. Small payload — call this FIRST to narrow the suite search.
list_engines
List all available database engines (postgres, duckdb, actian) and the benchmark test suites each engine has SQL for.
list_results
List completed benchmark experiments. Args: suite: Optional filter by test suite name (e.g. "analytical_wall"). engine: Optional filter by engine name (e.g. "duckdb").
list_suites
List benchmark test suites for a specific category. Suites include: analytical_wall, group_by, joins, null_logic, null_sentinel, recursion, selectivity, tpch, acid_test. Args: category: Required filter by category (e.g. "scaling"). Use list_categories to find valid options. include_sql: If true, include the raw SQL per engine (can be large).
list_templates
List curated experiment templates. Each is a valid, human-authored config you can get_template(name) and adapt.
recommend_engine
Get an engine recommendation based on pre-computed benchmark data. Returns the fastest engine for the given workload with confidence level and reasoning. Args: suite: Test suite name representing the workload type (e.g. "analytical_wall", "joins", "selectivity"). scale: Partition key substring to filter by data scale (e.g. "large", "medium", "1000000").
submit_experiment
Submit a new benchmark experiment. The experiment will run asynchronously. Use get_experiment_status() to poll for completion, then get_result() to retrieve data. If this exact experiment has been run before (same config + SQL + code), it returns status "duplicate" and you can immediately call get_result(). CRITICAL: For dataset.tables.*.rows, you MUST use a string alias that maps to definitions.rows, NOT a literal integer. Literal integers will be rejected. Args: config_yaml: YAML string defining the experiment (dataset, engines, matrix).

Signals

Stars: 2
Forks: 0
Open issues: 0
Weekly downloads: 194
Last commit: Oct 21, 2026 (8 days ago)
Last release: Aug 10, 2026 (2 months ago)
Last package publish: Oct 20, 2026 (8 days ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name: io.github.rctruta/sqlbenchdag
Version: 0.1.2
Auth: No auth
Packages: pypi sqlbenchdag
License: Apache-2.0
Repository: github.com/rctruta/sql-benchmarks-dagster
Website: github.com/rctruta/sql-benchmarks-dagster
First seen: Oct 24, 2026
Last checked: Oct 24, 2026
Source: Official MCP Registry github-graphql

Cite this page

SQL Benchmarks Lab MCP server. Agent Mart. https://agentmart.dev/connectors/rctruta-sqlbenchdag (last updated 2026-10-24)