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lint

Run read-only structural checks over the particle store to surface findings and counts for operator review, without applying fixes or LLM analysis.

Instructions

Run structural lint checks over the particle store (no fixes, no LLM).

The MCP surface is read-only, so ``--fix`` is forced off and the
LLM-assisted semantic checks are disabled — agents read findings
and surface them to the operator, who decides whether to act.

Args:
    category: Optional ``finding_type`` filter (e.g. ``STALENESS``,
        ``PHANTOM_SUBJECT``, ``WIKIDATA_LINK_MISMATCH``). When set,
        only findings of that kind are returned; ``summary`` is
        always the full counts dict and reflects every category
        actually emitted by this run.
    summary_only: When ``True``, drop the ``findings`` list and
        return only the ``summary`` counts dict (plus ``run_at``).
        Useful when the agent only needs to know what *kinds* of
        problems exist before drilling in with ``category``.
    limit: Maximum findings to include after filtering (default
        100). Hard cap to keep the response under per-tool-result
        size limits even when many findings of one category exist.
        A ``truncated`` flag is added when the cap fires.

Returns:
    ``{run_at, findings, summary}`` matching the ``LintReport``
    schema, optionally with ``truncated: True`` and (when
    ``summary_only``) without the ``findings`` key.

Input Schema

NameRequiredDescriptionDefault
limitNo
categoryNo
summary_onlyNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.210.2

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations present, the description carries the full burden and does so: it declares the tool is read-only, that --fix is forced off, that LLM semantic checks are disabled, and what the agent is expected to do with results. It also discloses the response cap behavior (default 100, hard cap, truncated: True when it fires) and that summary always reflects full counts regardless of category filtering.

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 key constraint (read-only, no fixes, no LLM) is front-loaded before the Args block, and each argument paragraph adds real information rather than restating the schema. The Args/Returns layout is well structured, though the prose is slightly longer than strictly necessary.

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?

For a zero-required-parameter read tool with an output schema and no annotations, the description covers purpose, safety profile, parameter semantics, truncation behavior, and return shape. The Returns section slightly overlaps the output schema but adds the truncated flag and the summary_only shape variation, which are not otherwise obvious.

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 description coverage is 0%, so the description must compensate, and it does for all three parameters. It explains category as a finding_type filter with concrete example values and notes summary is unaffected by it; summary_only as dropping the findings list and returning only summary plus run_at; and limit as a post-filter cap defaulting to 100 with a truncated flag.

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 opening sentence states a specific verb and resource ("Run structural lint checks over the particle store") and immediately scopes it with "no fixes, no LLM." That scope clause separates it from mutation-oriented or LLM-assisted siblings such as links_suggest and quality_report.

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?

It gives clear operational context — agents read findings and surface them to the operator, who decides whether to act — and describes a concrete workflow (use summary_only to learn what kinds of problems exist, then drill in with category). It never names a sibling alternative or states when a different tool would be preferable, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.