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register_probe

Identify recurring model weak spots by registering self-test probes with verifiers; regex, exact, or manual grading surfaces failures for targeted improvement.

Instructions

grader: 'regex' (pattern match in response), 'exact' (substring), or 'manual' (claude self-grades — always counts as failure unless caller explicitly confirms success via record_attempt). expected_pattern optional for 'manual'.

Categories should be claude-shape: 'count_long_context', 'date_arithmetic', 'recall_verbatim_block', 'detect_contradiction', 'follow_negative_instruction', 'preserve_list_order', 'respect_length_limit', 'needle_mid_context', 'fact_vs_inference', 'notice_absence', 'strict_format_compliance', 'uncertainty_acknowledgment'.

Input Schema

TableJSON Schema

Name	Required	Default
`grader`	No	regex
`prompt`	Yes
`category`	Yes
`expected_pattern`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.13.2Jun 17, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Discloses key behavioral trait: manual grader always counts as failure unless confirmed via record_attempt. Adds value beyond annotations by explaining grading logic.

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?

Concise but includes important details; could be more tightly structured but is not verbose.

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

Completeness4/5

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

Covers registration purpose, grader types, and categories. Could mention expected output or side effects, but core info is present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, description compensates fully: explains grader options (regex, exact, manual), expected_pattern optionality for manual, and enumerates valid categories.

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

Purpose5/5

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

Description clearly states the tool registers a self-test probe with a verifier. It specifies grader types and categories, distinguishing it from run_probe and other tools.

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

Usage Guidelines4/5

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

Provides guidance on grader types and behavior (e.g., manual grader default failure unless record_attempt). Does not explicitly compare with sibling tools like run_probe, but context is sufficient.

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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