Syl Pulse

io.sylaiPublished by sylai.ioOfficial publisherv0.1.0Updated Oct 30, 2026

Causal analytics: ask a business question, get a deterministic causal answer and charts.

The publisher's own server: Syl Guardian

VerifiedStreamable HTTPWeb executableData & AnalyticsAI & ML

Overview

AI-generated overview

Hosted causal-analytics server that lets an assistant estimate a causal effect from a dataset and return a full report with charts.

What you can do
  • Run a causal analysis on a supplied dataset and business question, returning the adjusted effect with confidence interval, per-channel breakdown, caveats and charts
  • Pass data as CSV text or as a list of row objects, and optionally name the cause, effect, confounder, outcome and time columns
  • Choose the estimator (adjusted OLS, AIPW, IPW, PSM, DML forest or a deterministic rule default)
  • Switch individual engine steps between fixed rules and LLM help, and optionally add a pass/caution/hold verdict or a spend-cut test design
When to use it
Useful when you want an assistant to answer a business question such as whether ad spend drives sales from your own data, with a deterministic statistical report rather than an LLM guess. Also suited to sizing a test or getting a quick verdict on a claim.
Requirements
A remote streamable HTTP endpoint; no local package. Every request, including tool listing, needs an X-API-Key header with a Syl API key (the same key as Syl Guardian; a trial key is available). Each successful analysis counts against your Pulse quota or trial.
Before you install
The API key is sent as a header on every request and should be kept secret. Your dataset and question are sent to the hosted service, and LLM steps are on by default for cleaning, discovery and summary, so data may reach a third-party model unless you set those steps to rule. Analyses consume quota.

Installation

In SourceWeft

  1. Open Syl Pulse in the dashboard and add it to a workspace.
  2. Connect X-API-Key credentials. They are stored privately and never shown on public pages.
  3. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/io.sylai%2Fpulse/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "mcpServers": {
    "syl-pulse": {
      "type": "http",
      "url": "https://pulse.sylai.io/mcp",
      "headers": {
        "X-API-Key": "<YOUR_API_KEY>"
      }
    }
  }
}

README

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.

Connect

Claude Code:

bash
claude mcp add --transport http syl-pulse https://pulse.sylai.io/mcp \  --header "X-API-Key: <your key>"

Claude Desktop (via mcp-remote):

json
{  "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": "<your 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.

ArgumentDescription
questionRequired. The business question, e.g. "Does ad spend drive sales?".
csv or dataDataset as CSV text or a list of row objects (exactly one).
cause, effectOptional. Treatment and outcome columns; inferred from the question if omitted.
confoundersOptional. Columns to adjust for, in addition to those discovered.
outcomesOptional. Several outcome columns, for by-channel effects.
time_colOptional. Time column, used for the test-design cadence.
include_guardian_verdictAlso return a pass / caution / hold verdict, computed locally (uses no Guardian quota).
include_chartsAttach chart images (default true).
include_test_designForce the spend-cut test design on or off; by default it appears only when the question asks about sample size, power or test duration.
estimatorOptional. One of ols_adjusted, aipw, ipw, psm, dml_forest, or rule (the deterministic default). Omit to follow config.estimator.
configOptional object choosing rule or llm per step: cleaning, discovery, estimator, summary. See Engine config.

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:

StepDefaultWhat it controls
cleaningllmDataset cleaning: derived columns and null imputation. The LLM only picks a strategy per column; the values are computed deterministically, and your cause and effect columns are never imputed.
discoveryllmPruning and orienting the causal graph, naming cause and effect from your question when you omit them, and reading your question to decide on optional extras such as a subgroup breakdown, a robustness check or a test design.
estimatorruleChoosing the estimator. With llm, the model picks from the supported set based on each method's documentation, and may also choose a matching method. With rule, the deterministic default is used.
summaryllmllm writes the report prose; rule uses a fixed template.

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 [email protected].

License

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

Source: README.md at commit 8b5c0b8

Tools

1

pulse_analyze

Estimate a causal effect from a dataset and answer a business question. Provide exactly one of ``csv`` (CSV text) or ``data`` (list of row objects). Returns the full long-form Pulse report (the same answer an email reply contains) as the primary text content and as ``report`` in the structured result, followed by chart PNGs as image content blocks (unless ``include_charts`` is false). Structured fields: the adjusted effect size (``ate``/``effect_size``) with its confidence interval (``ci_low``, ``ci_high``, ``ci_range``), per-channel effects (``channels``, keyed by outcome), confidence, and caveats. A ``test_design`` (how long a spend-cut test would take to confirm the effect, and whether the data already suffices) is included only when the question asks about sample size, power, or how long a test would take, or when ``include_test_design`` is true; otherwise it is null. With ``include_guardian_verdict`` it also returns ``guardian_verdict`` ``{verdict: pass|caution|hold, reason}``, computed locally. All numbers are deterministic statistics, never LLM-generated. Args: question: The business question, e.g. "Does ad spend drive sales?". csv: Dataset as CSV text. data: Dataset as a list of row records (alternative to ``csv``). cause: Treatment column (inferred from the question if omitted). effect: Outcome column (inferred if omitted). confounders: Columns to adjust for, in addition to those discovered. outcomes: Several outcome columns, for by-channel effects. time_col: Time column (used for the test-design cadence). include_guardian_verdict: Also return a pass/caution/hold verdict. include_charts: Attach chart images (DAG, effect, trend) as image content. include_test_design: Force the spend-cut test design / sample-size section on (true) or off (false). Omit (null) to include it only when the question asks about sample size, statistical power, or how long a test would take.

Parameterscsvdatacauseeffectoutcomesquestionrequiredtime_colconfoundersinclude_chartsinclude_test_designinclude_guardian_verdict

Version history

1
  1. v0.1.0LatestOct 22, 2026

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