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Edge Skills: report an Edge issue

report_issue

Report a bug or idea about Edge itself: an Edge call that failed or answered wrongly, a skill suggestion that did not fit, setup trouble, or a feature idea. You may call this on your own when an Edge call fails. A host approval denial is not an Edge failure; skip the blocked call and do not report it as an Edge bug. For how well a loaded skill did on the task, use rate_skill instead. Describe what happened without secrets or private user data. Include contact only if the user explicitly gave an email for this.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYesbug, wrong_skill, setup, or idea
contactNoAn email to reply to, only if the user explicitly gave it for this report
messageYesWhat happened or what you suggest, at most 2000 characters. No secrets or private user data.
request_idNoThe request_id of the Edge call this is about, if any

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations only declare the write/openWorld/non-idempotent profile. The description adds real behavior beyond that: the agent may file this autonomously, must not include secrets or private user data, and may include contact only with explicit user consent. It does not describe what happens after submission (confirmation, follow-up), which keeps it short of a 5.

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?

Purpose is front-loaded in the first sentence, followed by the guardrail on host approvals and the sibling pointer. Every sentence carries a distinct instruction (self-initiation, exclusion, alternative tool, privacy, contact consent) with no filler.

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 4-parameter, no-output-schema write tool, the description covers routing, exclusions, privacy, and consent well. It stops short of saying what acknowledgement or follow-up the caller receives, which is a minor gap given no output schema exists.

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 coverage is 100%, so the baseline is 3, but the description meaningfully enriches the enum beyond the schema's terse 'bug, wrong_skill, setup, or idea' by mapping each kind to a scenario. It also restates the contact-consent constraint stated in the schema, adding intent but not new syntax.

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?

States a specific verb and resource (report a bug or idea about Edge itself) and enumerates the concrete situations that qualify: failed/wrong Edge calls, misfit skill suggestions, setup trouble, feature ideas. It explicitly contrasts itself with the sibling rate_skill, so an agent can route without opening either schema.

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?

Gives explicit when-to-use (agent may self-initiate on an Edge call failure) and an explicit when-not (host approval denial is not an Edge failure; skip the blocked call). It names the alternative tool, rate_skill, for evaluating skill performance, leaving nothing to inference.

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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