Causal Decision Agent

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xtdpdsys

Read-only

Estimates dynamic panel models using system GMM (Blundell-Bond) to handle lagged dependent variables and endogenous regressors, producing reliable causal evidence for data-driven decisions.

Instructions

Blundell-Bond system GMM for dynamic panels (alias for xtabond with method='system'). Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`h`	No	xtabond2 h(): one-step error covariance; 2 is Stata xtdpdsys, 3 xtabond2's default, 1 the identity.
`x`	No	Exogenous regressors
`y`	Yes	Dependent variable
`id`	No	Unit identifier	id
`lags`	No	lags parameter (int).
`time`	No	Time column	time
`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
`twostep`	No	twostep parameter (bool).
`collapse`	No	Collapse instruments (Roodman 2009) to curb proliferation
`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.
`iv_equation`	No	Equation(s) the exogenous regressors instrument; 'diff' is Stata xtdpdsys, 'both' is xtabond2's iv() default.	diff
`data_columns`	No	Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.

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

A3.6/5.0

Behavior4/5

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

Annotations already mark the tool as read-only, so the safety burden is low. The description adds that it behaves as an alias for xtabond with method='system' and asserts certified parity evidence, which tells an agent the implementation is intentionally aligned with a known baseline. No contradiction with annotations.

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

Conciseness4/5

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

The description is compact and front-loaded with the estimator identity. The 'certified parity evidence' phrase is somewhat vague but adds credibility without wasting space.

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

Completeness3/5

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

Given the large parameter surface, the description is minimal, but the input schema carries detailed parameter documentation and an output schema exists. It still leans on the alias and parity claim rather than explaining estimator prerequisites or result behavior.

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 already documents all 15 parameters, including defaults and enums. The description contributes no parameter-level semantics beyond fixing method='system' as part of the tool's identity.

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 identifies the tool as a Blundell-Bond system GMM estimator for dynamic panels and clarifies it is an alias for xtabond with method='system'. It lacks an explicit verb like 'estimates', but the estimator and method are unmistakable and distinct from the sibling tools.

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

Usage Guidelines3/5

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

The alias relationship to xtabond with method='system' gives useful context and implies use for dynamic-panel system GMM. However, it does not explicitly state when to choose xtdpdsys over xtabond or other dynamic-panel siblings, nor does it provide any exclusion criteria.

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

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