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ryanmat

io.github.ryanmat/logicmonitor

by ryanmat

calculate_error_budget

Read-onlyIdempotent

Calculate SLO error budget consumption and projected exhaustion date to assess remaining budget, burn rate, and service health status.

Instructions

Calculate SLO error budget consumption and projected exhaustion date. Computes remaining budget, burn rate, and status (healthy/warning/critical/exhausted) based on actual availability vs target SLO.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
group_idNoFilter to a device group
device_idNoFilter to a specific device
target_sloNoTarget SLO percentage (default: 99.9)
period_daysNoSLO measurement period in days (default: 30)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv4.2.0

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already disclose readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well-covered. The description adds output details (status categories) but no additional behavioral caveats like permission needs or data-scoping behavior, which is acceptable given the annotations.

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?

Two sentences with zero redundancy. The primary action is front-loaded in the first sentence, and the second sentence adds essential detail in a compact list. The structure is highly efficient.

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?

The description explains the computation and output categories, but it does not clarify the scope when no filters are provided (e.g., does it calculate for the entire environment?) or how group_id and device_id interact. With no required parameters and no output schema, these gaps leave some ambiguity, though annotations cover the safety aspects.

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%, with each parameter (group_id, device_id, target_slo, period_days) having a clear description. The tool description does not add extra meaning beyond the schema, such as filter precedence or behavior when no parameters are supplied, 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.

Purpose5/5

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

The description uses a specific verb ('Calculate') and resource ('SLO error budget consumption and projected exhaustion date'), and lists concrete outputs (remaining budget, burn rate, status). This clearly distinguishes it from siblings like calculate_availability, which focuses on raw availability rather than error budget.

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. The description does not mention sibling tools, prerequisites, or exclusions; usage is only implied by the name and description, which is not enough given the large set of similar analytic tools.

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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