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Delimit Drift History

delimit_drift_history
Read-onlyIdempotent

Review past drift-check results to identify when API spec drift was detected and what changed. Read-only access to historical monitor data.

Instructions

List recent drift-check results from the drift monitor.

When to use: to investigate when API spec drift was last detected and what changed. When NOT to use: to perform a fresh drift check (use delimit_drift_check).

Sibling contrast: delimit_drift_check runs a check; this reads historical results.

Side effects: read-only. Calls ai.drift_monitor.get_drift_history.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax entries to return. Default 20.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv4.7.9
    • changedInput schema / properties / limit / description
      Previous value: -"Max entries to return."New value: +"Max entries to return. Default 20."
  2. Addedv4.5.5

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context by stating "Side effects: read-only" and revealing the underlying call ai.drift_monitor.get_drift_history, which goes beyond the structured data.

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?

The description is well-structured and front-loaded with the core purpose. There is mild redundancy between the "When NOT to use" section and the "Sibling contrast" section, both making the same delimit_drift_check comparison, but overall the content is tightly written and scannable.

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

Completeness5/5

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

For a read-only list operation with one optional parameter and an output schema available, the description is complete: it explains the purpose, the use case, the alternative, and the underlying side-effect behavior. Nothing essential for correctly selecting or invoking the tool is missing.

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 only parameter, limit, is fully documented in the input schema with default and meaning, so the description does not need to carry that burden. However, the description itself adds no extra semantic detail about the limit parameter beyond what the schema already states, making the baseline 3 appropriate.

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?

The description opens with a specific verb and resource: "List recent drift-check results from the drift monitor." It also explicitly contrasts itself with delimit_drift_check, making the tool's role unmistakable.

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

Usage Guidelines5/5

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

The description gives clear when-to-use context (investigate when API spec drift was last detected) and an explicit when-not-to-use instruction with the named alternative delimit_drift_check. The sibling contrast reinforces the decision with no ambiguity.

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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