Aident AI

Submit a bug report to Aident

loadout_bug_submit

Submit a redacted Aident Loadout bug report for product triage.

Input Schema

Name	Required	Description
`report`	Yes	Redacted Markdown bug report with observed and expected behavior. No tokens, credentials, or personal data.
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`surface`	No	Caller surface such as codex or claude-code.
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`agentSessionId`	No	Optional agent session id associated with the trace.

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedSep 14, 2026

removedInput schema / additionalProperties
```
Removed value: -false
```

addedInput schema / properties / context

Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "report"
-]New value: +[
+  "report",
+  "context",
+  "llm_model"
+]

First observedAug 7, 2026

TDQS

C2.9/5.0

Behavior2/5

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

Annotations already establish this is a non-read-only, non-destructive, non-open-world write (readOnlyHint=false, destructiveHint=false, openWorldHint=false). The description's only added signal is that the report is "redacted" for triage, and the redaction requirement is already spelled out in the schema's report description. It says nothing about what happens after submission, retention, or the analytics-only nature of the required context/model arguments.

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?

A single front-loaded sentence with zero filler; the action verb leads and the qualifiers follow. It is efficient, though so compressed that it strains to carry the tool's context.

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?

This is a write tool (readOnlyHint=false) with 5 parameters, three of them required, and no output schema. The description does not indicate what a successful submission yields, whether a confirmation or bug ID is returned, or how the analytics-only context/llm_model fields differ from the report content — gaps the schema alone only partly covers.

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% and the context and llm_model parameters carry unusually detailed constraints in the schema itself. The description adds no parameter-level meaning beyond what is already documented, so the baseline 3 applies.

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 states a specific verb and resource ("Submit a ... bug report") and adds scope qualifiers ("redacted", "for product triage"). No sibling tool in the list (affiliate, audit, billing, capabilities_*, skills_*, vault) overlaps with bug reporting, so explicit sibling differentiation is unnecessary rather than missing.

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?

"for product triage" hints at why the report exists but gives no when-to-use trigger, no prerequisites, and no when-not-to-use guidance. An agent gets no help deciding whether a given situation warrants submitting a bug report versus using capabilities_feedback or skills_feedback for user feedback.

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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