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haoyifan

Silicon Pantheon

by haoyifan
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

report_issue

Report an agent-observed problem: bug, confusion, unclear rules, scenario issue, imbalance, or suggestion. Helps maintain game quality and consistency.

Instructions

Record an agent-observed problem (bug / confusion / suggestion).

Called by the agent when something during play doesn't match what it expected — rules that seem broken, a scenario that feels inconsistent, tool results that contradict each other, or just "I'm confused about X". The server persists the report to three sinks so it's easy to review later:

  1. Match replay (as an agent_report event, turn-tagged).

  2. Server log, logger silicon.agent_report at INFO.

  3. Per-day jsonl file at ~/.silicon-pantheon/debug-reports/YYYYMMDD.jsonl.

category must be one of: bug, confusion, rules_unclear, scenario_issue, imbalance, suggestion. Any other value is rejected so grep -c on the file gives meaningful counts. Use imbalance specifically for "this scenario feels lopsided" observations (one team has structural advantage that makes the match trivial / unwinnable) — separate from scenario_issue (broken placement / wrong unit / unreachable tile) so balance-tuning reviews can be filtered cleanly.

Always available (no SILICON_DEBUG gate) — whether a player reports depends on whether the prompt tells them to, which IS debug-gated in the client. This keeps the tool usable for anyone who wants to flag something regardless of mode.

── Locking ── Resolve (state + room + session + viewer) atomically under state_lock; the three sink writes happen OUTSIDE the lock (they do I/O — file append, logger write).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
connection_idYes
categoryYes
summaryYes
detailsNo

Tool Definition Quality

A4.1/5.0
Behavior4/5

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

With no annotations, the description fully explains the tool's behavior: it persists reports to three sinks (replay, log, jsonl), handles category validation, and describes locking mechanics. This provides sufficient transparency about side effects and conditions.

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?

The description is well-structured with clear sections (purpose, sinks, categories, availability, locking). It is front-loaded with the main purpose. While slightly verbose, every sentence adds value, and the structure aids readability.

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?

Given the lack of annotations and output schema, the description provides comprehensive context: persistence sinks, category semantics, availability, and locking. It covers most behavioral aspects, though it does not detail return values or error handling for invalid categories, which would be helpful.

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?

The input schema has 0% description coverage, so the description must compensate. It thoroughly explains the 'category' parameter with allowed values and usage, and implies 'summary' as a brief description. However, 'connection_id' is not explicitly described, and 'details' is only mentioned as optional. This leaves some ambiguity for the agent.

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?

The description clearly states that the tool records agent-observed problems like bugs, confusion, or suggestions. It provides specific examples and distinguishes between similar categories such as 'imbalance' and 'scenario_issue', making it distinct from sibling tools like 'record_thought' or 'report_tokens'.

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?

The description details when to use the tool (when expectations are violated) and provides specific category usage (e.g., 'imbalance' for lopsided scenarios). It also clarifies availability ('Always available') but does not explicitly mention alternatives or when not to use it, which is a minor gap.

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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