Syl Guardian

Syl Guardian MCP

Is this ROAS / spend-to-revenue lift actually causal?

Syl Guardian is a Model Context Protocol (MCP) server that works as a causal-validation agent guardrail. When an AI agent (Claude or any MCP client) is about to act on a claim about ad spend or budget, such as "this campaign's ROAS is 4x, scale it up" or "ad spend drove new customers", Guardian checks the claim against your data and returns a pass, caution or hold verdict, with the naive correlation versus the confounder-adjusted causal effect, a confidence score and a plain-English recommendation.

Every number is computed deterministically by statistical code and never by an LLM. By default the summary is a fixed template. For strict reproducibility, set cleaning and discovery to rule in config: the default (llm) for those two steps can change the adjustment set, and so the estimate. See Engine config and Determinism and reproducibility.

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

• Endpoint: https://guardian.sylai.io/mcp (streamable HTTP)
• Auth: X-API-Key header. Get a trial key at https://sylai.io/guardian.
• Docs: https://sylai.io/guardian/docs

Connect

Claude Code:

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

--header "X-API-Key: "

Claude Desktop (via mcp-remote):

{

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

Then ask your agent: *"Use guardian_review on this data: did ad_spend cause new_customers? Treat seasonality as a confounder."*

Listing the tools does not require a key. A key is checked when guardian_review is called, and each review counts against your plan or trial quota. Requesting an upload link (guardian_request_upload) does not count toward quota.

Use with agents

Add this rule to your agent's instructions so it checks ad-spend and budget claims before recommending a change:

Whenever evaluating marketing campaigns, ad platforms, or budget allocation, verify the

claimed effect is causal using the Syl Guardian MCP tool (guardian_review) before giving

a final recommendation.

Ready-to-paste sections for CLAUDE.md, AGENTS.md and .cursorrules are in AGENT_RULES.md. Also available:

• Rules file: https://sylai.io/guardian-agent-rules.md
• Machine-readable summary: https://sylai.io/llms.txt
• Docs: https://sylai.io/guardian/docs
• Trial key: https://sylai.io/guardian

Python client

For Python agents and scripts, syl-guardian-client is a thin, MIT-licensed client for the hosted service. It only makes HTTP calls to https://guardian.sylai.io; every number is still computed deterministically by the service, never by an LLM. Set your key in SYL_GUARDIAN_API_KEY.

pip install syl-guardian-client            # extras: [crewai], [langchain]

from syl_guardian_client import GuardianClient

result = GuardianClient().review( "ad_spend", "conversion_value", data=rows, time_col="date", step=500, known_confounders=["brand_query_click_pct"], ) result.verdict # "pass" | "caution" | "hold"

• PyPI: https://pypi.org/project/syl-guardian-client/
• Source: https://github.com/connor274/syl-guardian-client

Tool: guardian_review

Sanity-checks a causal claim before an agent acts on it.

The result includes a text summary (verdict, recommended action, effect versus correlation, interpretation) and the full structured response. Bad input (including an unknown config key or value), auth failures and quota errors come back as MCP tool errors with a descriptive message. Misspelled argument names are rejected, not 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 the result's engine_steps reports {requested, used, fallback_reason} for each step (for example llm_unavailable). Setting all four to rule makes no LLM calls.

Example: {"config": {"cleaning": "rule", "discovery": "rule"}}.

Determinism and reproducibility

Under the defaults, the same data and request give the same verdict, estimate and interval. Randomness uses a fixed seed that is returned. What can change the estimate:

LLM calls run at temperature 0 with a pinned model, and each decision is recorded in llm_decisions and cached, but providers do not guarantee bit-identical output on a cache miss or model change, and the cache is in memory only. For strict reproducibility set cleaning and discovery to rule.

Estimator choice and DAG

With estimator omitted or "auto", the estimator comes from config.estimator. The default is rule: a deterministic choice (no confounders: naive OLS; binary treatment: AIPW; otherwise adjusted OLS). With config.estimator = "llm", an LLM picks one estimator (plus bounded parameters) from a fixed whitelist, filtered to what your data supports. It sees only a column schema summary, the DAG edges and the adjustment set, never your rows, and it never computes a number: the chosen estimator is run deterministically with fixed seeds. Passing a whitelist id forces that estimator with no LLM involved.

If the LLM is unavailable or returns an invalid or ineligible choice, or a requested estimator is ineligible for the data or fails, Guardian falls back to the deterministic rule. This is always reported, never hidden.

Every result includes these fields:

• estimator: id, params, source, rationale and fallback_reason. source is rule (also the value when no LLM is available), llm, override, or fallback (an LLM choice or override was attempted but rejected or failed). rationale is LLM-written prose and is set only for llm. fallback_reason explains why the requested estimator was not used, otherwise null.
• dag: the inferred causal graph, as nodes and edges of the form {"source": ..., "target": ...}. It reflects the cleaned data and any known_confounders.
• engine_steps: requested versus used mode for each step, with any fallback reason.
• stamp: a method and version stamp (core version, estimator, methods run, resolved config, seeds, models used, a data fingerprint, and the service name and version). If an answer changes between two runs, the stamp shows whether the data or a Syl release changed.
• llm_decisions: the LLM decisions made on this review (empty when no step used an LLM).
• confounder_importance: with summary: rule, how much the estimate moves when each adjustment variable is dropped, as a share of the total movement.
• cate and sensitivity: present only when requested.

Tool: guardian_request_upload

Returns a one-time upload link for a dataset too large to pass inline.

The result also includes a ready-to-run curl hint.

Large datasets

Use inline csv or data for small data. For more than about 200 rows, use the upload flow:

1. Call guardian_request_upload, then upload the file with the returned URL:

   curl -T data.csv "<upload_url>"

Gzip is allowed, e.g. curl -T data.csv.gz -H "Content-Encoding: gzip" " ". The PUT response returns only the schema (columns, dtypes, n_rows), never rows.

1. Call guardian_review with dataset_id instead of csv or data (they are mutually exclusive). Each uploaded dataset is single-use.

Uploaded datasets are visible only to the API key that uploaded them. Each dataset is deleted as soon as guardian_review runs on it, even if the review fails, so a retry needs a fresh guardian_request_upload. Unused uploads are deleted after 30 minutes.

A dataset_id can be reviewed once.

Example prompt: *"My data is in sales.csv (50,000 rows). Request an upload link, upload the file with curl, then use guardian_review once with the returned dataset_id (uploads are single-use): did ad_spend cause new_customers?"*

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 Guardian service is proprietary.