sqreg

Read-only

Run simultaneous quantile regression across multiple quantiles to assess distributional differences and produce certified parity evidence for evidence-backed decisions.

Instructions

Simultaneous quantile regression at multiple quantiles. Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	Yes	Primary running variable, regressor, or feature input for this estimator.
`y`	Yes	Outcome variable column name or outcome array.
`alpha`	No	Significance level for confidence intervals and tests.
`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
`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://.
`quantiles`	No	quantiles parameter (Optional[List[float]]).
`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.

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

C2.8/5.0

Behavior2/5

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

Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds no further behavioral context such as performance characteristics, limitations, or side effects. The phrase 'Validation: certified parity evidence' is too vague to convey any actionable behavioral detail and adds little beyond the annotation.

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 very short and front-loaded with the core purpose, but the second sentence 'Validation: certified parity evidence' is cryptic and adds no clear value. It feels like a disjointed afterthought that wastes space without contributing to usability. A cleaner structure would replace it with a usage note or remove it.

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?

This is a complex statistical tool with 10 parameters and an output schema, yet the description gives almost no context about expected inputs, output structure, or practical use. The output schema partially compensates, but the description does not prepare an agent to call the tool correctly, especially with the unexplained 'Validation' phrase.

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?

With 100% schema description coverage, the input schema already documents all 10 parameters. The description adds no extra meaning about how quantiles are specified or how x/y and data_path interact. It only repeats the general concept of quantile regression, so the baseline score of 3 is appropriate.

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 first sentence clearly identifies the tool as performing simultaneous quantile regression at multiple quantiles, a specific statistical task. The phrase 'at multiple quantiles' hints at a distinction from single-quantile regression siblings, though it does not name them. The cryptic second sentence about 'certified parity evidence' slightly muddies the purpose without providing a clear alternate meaning.

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 like qreg, ivqreg, or qte. The description lacks any mention of use cases, prerequisites, or conditions that would steer an agent toward or away from this tool. An agent would have to infer usage purely from the tool name and schema.

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