Syl Pulse

Syl Pulse MCP

Syl Pulse is causal analytics for business questions. Give your agent a dataset and a question like "Does ad spend drive sales?" and get back the full Pulse report: the adjusted causal effect with its confidence interval, a by-channel breakdown, caveats and assumptions, and charts (causal graph, effect, trend). When the question asks about sample size or test duration, it also returns a spend-cut test design.

All numbers are deterministic statistics. They are never computed by an LLM, and the figures in the report prose are checked against the computed numbers. Optional LLM help (dataset cleaning, causal-graph pruning, estimator choice, report prose) is controlled per step with config; see Engine config.

This repository holds the public listing metadata and connection instructions for the hosted Pulse MCP server. Pulse itself is a hosted service.

• Endpoint: https://pulse.sylai.io/mcp (stateless streamable HTTP)
• Auth: X-API-Key header on every request. It is the same key as Syl Guardian; get a trial key at https://sylai.io/guardian.
• Product page: https://sylai.io/pulse
• Docs: https://sylai.io/guardian/docs (MCP connector section)

Connect

Claude Code:

claude mcp add --transport http syl-pulse https://pulse.sylai.io/mcp \

--header "X-API-Key: "

Claude Desktop (via mcp-remote):

{

"mcpServers": { "syl-pulse": { "command": "npx", "args": ["-y", "mcp-remote", "https://pulse.sylai.io/mcp", "--header", "X-API-Key:${SYL_API_KEY}"], "env": { "SYL_API_KEY": " " } } } }

Then ask your agent: *"Use pulse_analyze on this data: does ad_spend drive new_customers, adjusting for seasonality? How long would a spend-cut test need to run?"*

Every request needs the key, including tool listing. Each successful analysis counts against your Pulse quota or trial.

Tool: pulse_analyze

Estimates a causal effect from a dataset and answers a business question.

The result's first content block is the report text, followed by chart images. The structured result has the effect size with its confidence interval, per-channel effects, confidence, caveats, and the optional verdict and test design. It also reports the estimator_used, the resolved config, and engine_steps. An invalid config (unknown key or a value other than rule or llm) comes back as a tool error, and misspelled argument names are rejected rather than ignored.

Engine config

config lets you choose, per step, whether the engine uses fixed rules or an LLM. Each value is "rule" or "llm". Omitted keys use the defaults:

The model key is provided by the service; you never supply one. rule always works. If an llm step cannot run, it falls back to rule, and engine_steps reports {requested, used, fallback_reason} for each step. Setting all four to rule makes no LLM calls.

Example: {"config": {"estimator": "llm", "summary": "rule"}}.

Which settings can change a number: cleaning: llm (imputation strategy, derived-column drops), discovery: llm (the adjustment set) and estimator: llm (the method). summary: llm changes prose only. For results that don't depend on an LLM at all, set cleaning, discovery and estimator to rule.

For a quick pass/caution/hold check on a single claim, see the sibling server Syl Guardian MCP.

Support

Questions or issues: https://sylai.io or connor@sylai.io.

License

The files in this repository (metadata and documentation) are MIT licensed; see LICENSE. The Syl Pulse service is proprietary.