synth_compare

Read-only

Run and compare multiple synthetic control method variants side by side, with placebo inference, to identify the most suitable estimator for your causal analysis.

Instructions

Run multiple SCM variants and compare them side by side. Cost: Runs every estimator in methods= end to end, so cost is the sum of the individual fits -- and each placebo-enabled member internally re-runs once per donor. Expect it to be the slowest call in a synthetic-control workflow; narrow methods= once you have shortlisted.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`time`	Yes	Time period column.
`unit`	Yes	Unit identifier column.
`alpha`	No	Significance level for confidence intervals.
`detail`	No	Payload depth: 'minimal' (~150 tokens) for sub-step calls where only the point estimate is needed; 'standard' (~1K tokens) for diagnostics + coefficient table; 'agent' (~2K tokens, default) adds violations / next_steps / suggested_functions so the LLM can plan its next call without another round-trip.	agent
`methods`	No	SCM variants to compare. If ``None`` (default), all 20 registered methods are attempted, in ascending complexity order: ``classic, penalized, demeaned, detrended, unconstrained, elastic_net, augmented, sdid, gsynth, mc, discos, scpi, penscm, fdid, sparse, cluster, kernel, kernel_ridge, bayesian, bsts``. Pass an explicit subset to reduce runtime.
`outcome`	Yes	Outcome variable column name.
`placebo`	No	Whether to run placebo inference for each method.
`as_handle`	No	If true, cache the fitted result on the server and return result_id + result_uri alongside the JSON payload so a subsequent tools/call can chain without re-running.
`data_path`	Yes	Absolute path or URL to a data file. Supported: .csv / .tsv / .txt (delimited), .parquet / .pq, .feather / .arrow, .xlsx / .xls, .dta (Stata), .json / .jsonl. Schemes: file://, s3://, gs://, https://.
`result_id`	No	Optional handle to a previously-fitted result (returned by an earlier call when as_handle=true). Tools that operate on a fitted object accept this in place of re-supplying data_path + columns.
`data_columns`	No	Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
`treated_unit`	No	Identifier of the treated unit.
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.
`treatment_time`	No	First treatment period (inclusive).

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 15, 2026

TDQS

A4.1/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, and the description adds valuable behavioral context beyond that: cost is the sum of individual fits, placebo-enabled members rerun per donor, and it is the slowest call. This helps an agent budget compute time appropriately.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences with no filler. The core purpose is front-loaded, and the cost warning is directly relevant to tool selection and invocation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the rich input schema, output schema, and readOnly annotation, the description provides the essential extra context: cost profile, workflow placement, and how to control runtime. Nothing critical is missing for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all 14 parameters. The description references methods= and advises narrowing it, but adds no param-specific meaning beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action and resource: 'Run multiple SCM variants and compare them side by side.' This distinguishes it from single-estimator tools like synth, though it does not explicitly differentiate it from comparison tools like compare_estimators.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear workflow context: expect it to be the slowest call in a synthetic-control workflow and narrow methods= once shortlisted. It implies when to use it, though it does not explicitly state exclusions or name alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Causal Decision Agent