Causal Decision Agent

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event_study_table

Read-only

Turn a fitted event-study result into a structured table of coefficient estimates, making pre/post treatment effects easy to inspect.

Instructions

Adapter that turns an event-study fit into a regtable input.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`regex`	No	Pattern with one capture group that matches the relative time in coefficient names. Required when the CausalResult fast path is not applicable. Examples: ``r"^tau_(-?\d+)$"``, ``r"^lag(\d+)$"``, ``r"::(-?\d+)$"``.
`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
`result`	Yes	Fitted model. The CausalResult fast path is used automatically when ``model_info['event_study']`` is present.
`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`	No	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://.
`label_fmt`	No	Format string for the row label of each event-time bin. The ``{t}`` placeholder receives the integer relative time.	t={t}
`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.
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.
`include_reference`	No	Whether to render the reference period row (typically ``t=-1``) where the estimate is identically zero.

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

B3.4/5.0

Behavior3/5

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

The annotations already declare readOnlyHint=true, so the safety profile is covered by structured metadata. The description adds no behavioral context such as the CausalResult fast path, caching via as_handle, or output conventions, but it does not contradict the annotations either.

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 a single sentence with no filler, and the core conversion purpose is front-loaded. It is concise without being bloated, though it could have been slightly more informative.

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?

For a 10-parameter utility with a full output schema and rich parameter documentation, the description is minimally sufficient but leaves the pipeline position implicit. A note about being called after event_study or when a CausalResult fit is available would make it more complete.

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% and the parameter descriptions are detailed, including regex examples, detail-level payload sizes, and as_handle chaining behavior. The tool description itself adds no parameter-level meaning, so the baseline of 3 applies.

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 uses a specific verb and resource: it 'turns an event-study fit into a regtable input,' making clear this is a transformation utility rather than an estimation or plotting tool. It does not explicitly contrast with sibling tools like event_study or enhanced_event_study_plot, so it stops short of full differentiation.

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 phrase 'event-study fit' implies the tool should be called after fitting an event study and when a regression-table input is needed, but no explicit when-to-use or when-not-to-use guidance is given. Naming an alternative like event_study or enhanced_event_study_plot would have made the usage context sharper.

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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