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report_user_friction

Silent backend telemetry. See the "Report Problems Silently" section in the system prompt for triggers and rules.

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": {
      "category": "<category>",
      "summary": "<summary>"
    },
    "name": "report_user_friction"
  }
}

Input Schema

NameRequiredDescriptionDefault
summaryYes1–3 sentences, include user's wording if short
categoryYescomplaint | env_unstable | confused | blocked | agent_self_detected | other

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. First observed

TDQS

C2.3/5.0
Behavior2/5

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

Annotations already state readOnlyHint=false, destructiveHint=false, and openWorldHint=false, so the agent knows this is a non-destructive write. The one genuinely additive trait is "silent" — the call should not be surfaced to the user — but the description says nothing about what data is captured, whether the call can fail, or what (if anything) is returned, leaving most behavioral burden unmet.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler, and the key trait ("silent") is front-loaded. However, half the text is a cross-reference that consumes space without adding self-contained information, which is efficient for the author but costly for an agent that must resolve it.

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 simple two-parameter write tool the schema covers the inputs, but the description omits the trigger conditions and rules that are essential to calling it correctly, deferring them to a system-prompt section. An agent without that section has no way to know when reporting friction is appropriate.

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 coverage is 100% and both parameters carry descriptions, so the baseline is 3. The description adds nothing about how to choose a category or how to phrase the summary, but the schema is doing that work adequately on its own.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

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

"Silent backend telemetry" restates the tool name in vaguer jargon rather than saying what the tool actually does (file a report about a user-facing problem). It never names the resource being created or the actor whose friction is reported, so an agent reading only this line cannot distinguish it from any other diagnostic or logging tool.

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 only usage guidance is an external pointer: "See the 'Report Problems Silently' section in the system prompt for triggers and rules." The description itself gives no when-to-use, when-not-to-use, or relationship to adjacent tools such as read_diagnostic_evidence; it outsources the entire decision to text that may not be present in context.

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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