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covalschi

dayz-agentic-modding-mcp

knowledge_status

Detect outdated knowledge layers by comparing recorded sizes and timestamps against current files, revealing changes, additions, deletions, and clarifying skipped vs outstanding sources.

Instructions

What each knowledge layer holds, how old it is, and whether it still matches what is on disk.

Staleness is a measurement, never a guess: each layer records the size and modification time of every source it read, and this compares them against the files as they are now. A layer reports what changed, what appeared and what is gone -- so "the project was edited" is a different fact from "the game was updated", which is the whole reason there are three layers.

Two counts that must not be confused, and both are here: sources is everything the build walked, and empty_sources is how many of those gave no declarations at all -- an archive that could not be read, or a file that genuinely declares nothing. Which of the two is in last_build: skipped is what the LAST build could not read, and outstanding is every source the layer still holds nothing from, including ones an earlier build found. The second exists because the first is correctly empty after an incremental build that re-read nothing, which left empty_sources standing with no explanation at all.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
dataNo
hintNo
errorNo
Behavior5/5

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

With no annotations, the description carries the full burden. It fully discloses the measurement mechanism (comparing size and modification time against disk), the semantics of 'sources' vs 'empty_sources', and the subtle distinction between 'skipped' and 'outstanding', including why 'outstanding' exists after incremental builds. This goes far beyond typical transparency.

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 longer than strictly necessary, but it is structured logically—first the overall purpose, then the staleness measurement, then the detailed explanation of the two counts. The front-loaded purpose is clear, and every sentence adds substantive information, though it could be tightened.

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 the complexity of the staleness semantics and the presence of an output schema, the description is entirely complete. It covers what each layer holds, how staleness is computed, and the meaning of both counts, including edge cases like incremental builds. No necessary information is missing.

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

Parameters4/5

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

The schema has zero parameters, so the baseline is 4. The description adds valuable meaning to the return values by explaining the meaning of the counts and their relationship, which compensates for the lack of an output schema in the given context (though an output schema exists per signals). No params are left undocumented.

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 states a specific verb ('holds', 'how old', 'matches') for the knowledge layers, and explains the core concept of staleness as a measurement. It clearly distinguishes the purpose from other knowledge tools (e.g., searching or showing) by focusing on status and integrity, though it does not name a sibling explicitly.

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

Usage Guidelines3/5

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

The description implies usage for inspecting layer status and staleness, but it does not explicitly contrast with alternatives like knowledge_find or knowledge_show, nor does it state conditions for when to prefer this tool. The context is clear but exclusions are absent.

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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