Syl Pulse
Causal analytics: ask a business question, get a deterministic causal answer and charts.
Should I use this
Quality & Safety
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"pulse": {
"url": "https://pulse.sylai.io/mcp"
}
}
}Remote endpoints
https://pulse.sylai.io/mcpstreamable-httpWhat it can do
Tool inventory
Tools (1)
🟢pulse_analyze(question, csv, data, cause, effect, ...)
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.
Input Schema
{
"type": "object",
"properties": {
"question": {
"title": "Question",
"type": "string"
},
"csv": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Csv"
},
"data": {
"anyOf": [
{
"items": {
"additionalProperties": true,
"type": "object"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"title": "Data"
},
"cause": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Cause"
},
"effect": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Effect"
},
"confounders": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"title": "Confounders"
},
"outcomes": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"title": "Outcomes"
},
"time_col": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Time Col"
},
"include_guardian_verdict": {
"default": false,
"title": "Include Guardian Verdict",
"type": "boolean"
},
"include_charts": {
"default": true,
"title": "Include Charts",
"type": "boolean"
},
"include_test_design": {
"anyOf": [
{
"type": "boolean"
},
{
"type": "null"
}
],
"default": null,
"title": "Include Test Design"
}
},
"required": [
"question"
],
"title": "pulse_analyzeArguments"
}Community
Evidence