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vikranthviki

Causal Decision Agent

by vikranthviki

synthdid_rmse_plot

Read-only

Plot pre-treatment RMSE between treated and synthetic trajectories to assess synthetic control fit before estimating treatment effects.

Instructions

Pre-treatment RMSE of treated vs synthetic trajectory.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
axNoax parameter.
detailNoPayload 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
resultYesresult parameter (CausalResult).
figsizeNofigsize parameter (Tuple[float, float]).
as_handleNoIf 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_pathNoAbsolute 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_idNoOptional 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_columnsNoOptional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
data_sample_nNoOptional uniform random subsample size (seed=0, deterministic) — useful on huge panels.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

Annotations declare readOnlyHint=true, so the description doesn't need to repeat safety. However, it adds no additional behavioral context—such as requiring a fitted result, returning a figure, or caching behavior—beyond what the name and schema already imply. 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 a single, efficient sentence with no wasted words. It conveys the core content immediately, though it could have included a bit more context without becoming verbose. Front-loaded and appropriately sized for a simple plot tool.

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

Completeness2/5

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

Despite having an output schema and annotations, the description is too thin for a tool with 9 parameters and a rich sibling context. It doesn't explain what 'pre-treatment RMSE' means in practice, when to choose this over synthdid_plot, or what the 'result' parameter represents. An agent would likely need to inspect the schema and output schema to use it correctly.

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 baseline is 3. The description adds no parameter-specific meaning; it doesn't clarify that 'result' must be a fitted CausalResult or how 'figsize' affects the plot. But because all parameters are already documented in the schema, the description doesn't need to compensate.

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 metric ('Pre-treatment RMSE') and the comparison ('treated vs synthetic trajectory'), which distinguishes it from sibling tools like synthdid_plot or synthdid_units_plot. However, it lacks an explicit verb (e.g., 'plots'), relying on the tool name to imply visualization, so it's not a full 5.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus alternatives. It doesn't mention that it requires a fitted CausalResult or that it's a diagnostic for pre-treatment fit. The single sentence offers no context for selection among the many synthdid-related siblings.

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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