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Report

report

Contribute outcomes, feedback, API changes, or qualitative experiences to help the community. PII is auto-masked.

Instructions

Contribute data back to the KanseiLink community. Report success/failure after using a service (5 seconds, helps everyone), submit feedback, record API change events, or share your qualitative experience. PII is auto-masked. This is step 4 of the standard flow: search_services → lookup → (execute) → report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoExplicit mode selection. Auto-detected from params if omitted: success → outcome, question_id → voice, event_type → event, subject+body → feedback.
service_idNoService ID. Required for outcome and voice modes. Optional for feedback and event.
agent_idNoYour agent identifier (optional, for follow-up). Used in feedback and voice modes.
agent_typeNoAgent platform type (claude, gpt, gemini, copilot, llama, deepseek, other). Used in outcome mode (auto-inferred from model_name if omitted) and voice mode.
successNo[outcome] Whether the operation succeeded.
latency_msNo[outcome] Response time in milliseconds.
error_typeNo[outcome] Error category if failed (e.g., 'auth_error', 'timeout', 'rate_limit', 'schema_mismatch').
workaroundNo[outcome] How you resolved the issue, if any. Helps future agents.
contextNo[outcome] Additional context about the usage (PII will be auto-masked).
is_retryNo[outcome] Whether this is a retry of a previously failed call.
estimated_usersNo[outcome] Approximate number of end-users your agent serves.
model_nameNo[outcome] LLM model used (e.g., 'claude-sonnet-4', 'gpt-4o').
task_typeNo[outcome] Operation performed (e.g., 'create_invoice', 'search_contacts').
input_tokensNo[outcome] Input/prompt token count.
output_tokensNo[outcome] Output/completion token count.
cost_usdNo[outcome] Actual cost in USD (estimated from tokens if omitted).
feedback_typeNo[feedback] Type of feedback: suggestion, missing_data, correction, feature_request, workaround_tip, bug_report, praise, other.
subjectNo[feedback] Short summary of your feedback (1 line).
bodyNo[feedback] Your feedback in detail. Write freely.
priorityNo[feedback] How important: low, normal, high, critical. Default: normal.
event_dateNo[event] When the event occurred or takes effect (YYYY-MM-DD).
event_typeNo[event] Category: api_change, api_deprecation, law_amendment, pricing_change, outage, security_incident, feature_launch, competitor_move, mcp_update, other.
titleNo[event] Short event title (e.g., 'freee API v3 deprecation').
descriptionNo[event] Details about the event and expected impact.
impact_expectedNo[event] Expected impact: positive, negative, neutral, unknown.
question_idNo[voice] Which question to answer: selection_criteria, would_recommend, biggest_frustration, best_feature, switching_likelihood, auth_experience, doc_quality, error_handling, compared_to_competitor, mcp_readiness, free_voice.
response_choiceNo[voice] Quick rating where applicable (e.g., 'strongly_yes', 'excellent', 'ready').
response_textNo[voice] Your honest answer in your own words.
confidenceNo[voice] How confident are you in this assessment? high, medium, low.
Behavior4/5

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

Discloses PII auto-masking, auto-detection of mode, and time estimate (5 seconds). Annotations are minimal (readOnlyHint false, idempotentHint false), so description adds necessary context about mutability. No output schema mentioned, but the reporting action is well-explained.

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?

Description is a single paragraph that front-loads purpose and flow. It covers all modes efficiently but could benefit from bullet points for readability. No wasted sentences, but length is justified by complexity.

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?

Given 29 parameters across 4 modes and no output schema, the description thoroughly explains each mode, parameter groups, auto-detection, and flow context. It provides complete guidance for an agent to use the tool correctly.

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 100%, but description adds significant value beyond schema by grouping parameters per mode (e.g., [outcome], [feedback]) and explaining auto-detection rules. This helps agents select correct parameters for each mode.

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?

Description clearly states the tool's purpose: 'Contribute data back to the KanseiLink community' with four distinct modes (outcome, feedback, event, voice). It distinguishes itself from sibling tools (analyze, inspect, lookup, search_services) by being step 4 of a standard flow for reporting after execution.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly positions the tool as step 4 in the flow: 'search_services → lookup → (execute) → report.' Mentions auto-detection of mode and required parameters per mode. Lacks explicit when-not-to-use statements, but the context is clear enough.

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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