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report_limitation

Log issues you encounter in Zen with these tools for developers to review and fix. Helps address missing features or broken interactions.

Instructions

Tell the zenpilot developers about something you couldn't do in Zen with these tools.

Call it once per problem when a task needs something the tools lack (drag and drop, a
widget that ignores clicks, typing or select, content read_page doesn't show) or only
works after a workaround. goal: what you were trying to do, in a few words, like "pick a
date on a booking form". problem: what you tried and what happened, naming the elements, refs'
roles and errors. workaround: what worked instead, if anything. It's saved on the user's
Mac for them to review and file as a GitHub issue, so leave out personal data and anything
the user typed.

Input Schema

NameRequiredDescriptionDefault
goalYes
problemYes
workaroundNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.5.1

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: it discloses the side effect (saved to the user's Mac), the downstream audience (developer review, filed as a GitHub issue), and an explicit privacy constraint to omit personal data and anything the user typed. That is exactly the context an agent needs before emitting a report.

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?

Front-loads the core action, then usage conditions, then per-field guidance, with no filler sentences. The parenthetical example list is dense but each example maps to a distinct failure mode; slightly more compact phrasing would make it a 5.

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?

A three-parameter, non-mutating feedback tool with no output schema needs exactly what is given: trigger conditions, field-level expectations, an example goal, and data-handling rules. Nothing an agent needs in order to file a usable report is missing.

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

Parameters5/5

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

Schema coverage is 0%, so the description must compensate — and it defines all three fields: goal ("in a few words" plus a worked example), problem ("what you tried and what happened, naming the elements, refs' roles and errors"), and workaround ("what worked instead, if anything"). This adds real guidance the bare schema titles do not.

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?

Opens with a specific verb and addressee: "Tell the zenpilot developers about something you couldn't do in Zen with these tools." This unambiguously separates it from every sibling (click, type, read_page, etc.), which are browser actions rather than a feedback channel.

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?

Explicitly scopes the trigger condition ("when a task needs something the tools lack... or only works after a workaround") and gives concrete examples: drag and drop, click-ignoring widgets, typing/select failures, content read_page misses. It also states a calling cadence ("once per problem"), leaving little to infer.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.