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lint_wiki

Audit wiki channels for health problems such as stale, orphaned, duplicated, or inconsistent pages. Returns structured findings with severity and suggested actions.

Instructions

Audit a channel's wiki for health problems and return a list of findings. Call it to check whether wiki pages are stale, orphaned, duplicated, or internally inconsistent before relying on them or recommending a refresh.

When to use: validating wiki quality, or diagnosing why an answer looked wrong. When NOT to use: routine reading (use read_wiki_page / list_wiki_pages) — linting is heavier. Set run_coherence_check=false to avoid the per-page LLM cost when you only need structural checks.

Prerequisites: a channel_id from list_channels.

Returns (long-running when run_coherence_check=true — one LLM call per page; read-only, writes nothing): {findings: [{severity, category, page_id, section_id, message, suggested_action}, ...], pages_scanned: N}.

Error modes (returned as dicts): 'authentication_missing' (no principal); 'channel_access_denied' (token lacks access to channel_id); 'lint_failed' (returned as {findings: [], error: 'lint_failed'} on an internal lint error).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channel_idYesChannel id whose wiki to lint. Get it from list_channels (e.g. 'ch-eng'). Required.
target_langNoOptional BCP-47 language tag to lint (e.g. 'en'). Omit to lint the channel's primary language. Default null (treated as 'en').
run_coherence_checkNoIf true, also run the LLM coherence pass (one model call per page — slower and incurs token cost). Set false for a fast structural-only lint. Default true.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Discloses behavior beyond annotations (none provided): read-only operation ('writes nothing'), potential long runtime when run_coherence_check=true, and error modes. No contradictions.

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?

Well-structured with front-loaded purpose, usage, params, return, and errors. Each section serves a purpose, though slightly lengthy. Minor redundancy with schema descriptions.

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?

Outlines return format with example structure, lists error modes, and prerequisites. Sufficient for an agent to understand what to expect and how to handle outcomes.

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%; description rephrases parameter info but adds little new meaning. For run_coherence_check, the description echoes the schema's cost/latency note. Baseline of 3 is appropriate.

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?

Clearly states 'Audit a channel's wiki for health problems' with specific verb and resource. Differentiates from siblings by explicitly mentioning not to use for routine reading (use read_wiki_page/list_wiki_pages).

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?

Provides explicit when-to-use (validating wiki quality, diagnosing wrong answers) and when-not-to-use (routine reading, heavier linting). Includes prerequisite (channel_id) and best practice (run_coherence_check=false to avoid cost).

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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