Causal Decision Agent

by vikranthviki

Overview Schema Source Related Servers Discussions Admin

Python

Local

synth_to_markdown

Read-only

Format synthetic-control results into a GitHub-flavoured Markdown table for clear reporting and audit trails.

Instructions

GitHub-flavoured Markdown table for synthetic-control results.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`obj`	Yes	obj parameter (Union[CausalResult, 'SynthComparison', List[CausalResult]]).
`title`	No	title parameter (Optional[str]).
`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
`digits`	No	digits parameter (Optional[int]).
`show_ci`	No	show_ci parameter (bool).
`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://.
`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.
`method_names`	No	method_names parameter (Optional[Sequence[str]]).
`show_weights`	No	show_weights parameter (bool).
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.
`top_n_weights`	No	top_n_weights parameter (int).

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

C2.3/5.0

Behavior2/5

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

The annotations (readOnlyHint=true) already indicate no state mutation, and the description adds nothing about behavior. It does not disclose whether it accepts a pre-fitted result (result_id) or fits from raw data (data_path), which the schema suggests but the description omits.

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

Conciseness3/5

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

The description is a single concise sentence, which is appropriately sized for simplicity, but it is under-specified and fails to front-load critical usage details. It is not wordy, but it sacrifices informativeness.

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?

Given the tool's complexity (13 parameters, output schema, and a large sibling set), the description is incomplete. It doesn't explain return values, how to chain results, or when to use this formatting tool over others, leaving significant gaps for an agent.

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?

The schema provides 100% coverage with detailed descriptions for key parameters like detail, as_handle, and data_path. The tool description adds no parameter information, so the baseline of 3 applies.

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

Purpose3/5

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

The description states the output format (GitHub-flavoured Markdown table) and the subject (synthetic-control results), but the verb is implicit and it doesn't clarify whether the tool converts an existing result or fits a model. It distinguishes minimally from output siblings like synth_to_excel and synth_to_latex only by format.

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

Usage Guidelines1/5

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

No guidance is provided on when to use this tool versus alternatives such as synth_to_excel, synth_to_latex, or synth_report. The description gives no context about preferred scenarios or how it fits into a workflow.

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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