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Glama

reject_last_action

Mark an unwanted agent edit as rejected to establish preference boundaries. Use when a human undoes or discards a change.

Instructions

Mark the last action as rejected (exploration / preference boundary).

Use when the human undoes or discards an agent edit. Requires telemetry consent.

Parameters:

  • reason: Optional why it was rejected

  • user_prompt: The original user prompt that led to this tool call (for telemetry)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNo
user_promptNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses a meaningful behavioral trait ('Requires telemetry consent') and frames the action as a preference boundary, but it does not state whether the rejection is reversible, whether it modifies or deletes the last action, or what happens if consent is missing.

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

Conciseness5/5

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

The description is compact, front-loaded with the core purpose, and each sentence earns its place: trigger, consent prerequisite, then parameter explanations. No filler or redundancy.

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?

For a simple two-parameter feedback tool, the description covers what, when, prerequisite, and parameter semantics. An output schema is present, so return-value details are not required. It could be more complete by distinguishing itself from sibling feedback tools, but it is not missing critical invocation information.

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

Parameters4/5

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

Schema description coverage is 0%, but the description compensates by explaining both parameters: 'reason' as optional rejection rationale and 'user_prompt' as the originating prompt for telemetry. This adds meaning beyond the schema's type/default fields, though it could explicitly mark user_prompt as optional.

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 uses a specific verb and object ('Mark the last action as rejected') and adds domain context ('exploration / preference boundary'). It clearly conveys the tool's role, though it does not explicitly name or contrast sibling feedback tools like rate_last_action or prefer_candidate.

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?

It gives an explicit trigger: 'Use when the human undoes or discards an agent edit,' plus a prerequisite ('Requires telemetry consent'). However, it does not say when not to use it or point to alternatives such as rate_last_action or prefer_candidate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.