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tF_critical_value

Read-only

Get Lee-McCrary-Moreira-Porter adjusted tF critical values for a first-stage F statistic to test weak instruments. Input the observed F to obtain the validated threshold for reliable inference.

Instructions

Lee-McCrary-Moreira-Porter (2022, AER) tF adjusted critical value. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`alpha`	No	Significance level. Only ``0.05`` is implemented (the only level for which LMMP publish a complete table).
`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`	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.
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.
`first_stage_F`	Yes	Observed first-stage F statistic (or Olea-Pflueger F_eff).

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

B3.1/5.0

Behavior3/5

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

The annotations already mark the tool as read-only, so the safety profile is covered. The description adds a citation and a validation-evidence-tier note, which gives some provenance context, but it does not disclose operational behaviors such as handling of unsupported alpha levels, though the schema largely covers that.

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 short and front-loads the main purpose, but the second sentence is vague and reads like a truncated metadata field rather than a meaningful explanation. It is not wastefully long, but it does not earn full credit for clarity of structure.

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 rich schema, output schema, and read-only annotation, the description does not need to explain return values or safety. However, it omits any mention of the closely related tF_adjustment sibling and does not provide enough methodological context for an agent to confidently distinguish or choose this tool.

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 parameters including first_stage_F and the alpha limitation. The description adds no parameter-level meaning beyond pointing to the LMMP method and the tF critical value concept, so it meets the baseline without elevating it.

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 computing the Lee-Mcrary-Moreira-Porter (2022, AER) tF adjusted critical value, which is a specific, well-defined resource. It lacks an explicit verb and does not contrast with the closely related sibling tF_adjustment, so it stops short of full differentiation, but the intent is clear.

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 about when to use this tool versus tF_adjustment or any alternative. The validation-tier sentence does not help an agent decide whether this is the right call, leaving usage inference entirely to the schema and tool name.

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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