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by io.github.canoydario847-cloud
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SLO error budget

privi_slo_budget
Read-only

Calculate allowed failures and remaining error budget from SLO and request counts.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slo_percent": "<slo_percent>",
      "requests": "<requests>",
      "failures": "<failures>"
    },
    "name": "privi_slo_budget"
  }
}

Input Schema

NameRequiredDescriptionDefault
failuresYes
requestsYes
slo_percentYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior3/5

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

Annotations already declare this as a read-only, closed-world calculation, and the description is consistent with that. The description adds that the output consists of allowed failures and remaining error budget, but it does not disclose the calculation formula, rounding behavior, or edge-case handling such as zero requests.

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 a single front-loaded sentence with no wasted words, making the core computation easy to grasp. However, it is arguably too terse given the semantic gaps in parameters and output behavior.

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

Completeness2/5

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

For a three-parameter calculation tool with no output schema and zero schema coverage, the description is not complete enough. It omits the formula, units, edge cases, and return-value format, all of which an agent would need to invoke and interpret the tool correctly.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must carry the burden of explaining the three parameters. It mentions SLO and request counts, but it does not clarify units (e.g., whether slo_percent is 99.9 or 0.999), whether failures is a raw count, or how remaining budget is derived.

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 names a specific verb (calculate) and concrete outputs (allowed failures and remaining error budget) derived from stated inputs. It is clear what the tool computes, but it does not explicitly differentiate itself from the similarly named sibling privi_pro_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?

The description provides no explicit when-to-use guidance, no conditions for selecting this tool over alternatives, and no prerequisites. With a near-identical sibling like privi_pro_error_budget, the agent is left to infer the distinction from the name alone.

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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