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Cloto-dev

CPersona

Official
by Cloto-dev

check_health

Check memory database health by detecting contamination, duplicates, embedding issues, and schema drift. Returns status and can auto-repair fixable issues.

Instructions

Check memory database health (25-check registry, each issue tagged with severity critical/warn/info). Detects contamination, duplicates, oversized content, embedding issues, FTS integrity (count + content-level), schema version/object drift (missing UNIQUE indexes or FTS triggers), SQLite file integrity, project_id naming drift, invalid JSON/timestamps, timestamp format drift, stale tasks, missing profiles, empty content, invalid/anonymous sources. Returns storage stats incl. project_id/channel distributions. Set fix=true to auto-repair (agent-scoped, locked-safe); critical file-integrity findings are report-only. Two repairs are lossy and irreversible: oversized memories AND the agent's profile row are cut to CPERSONA_MAX_CONTENT_LENGTH (default 2000 characters), keeping the start. Lower that cap and a fix run shortens rows that were within the old one. Use checks parameter to run a subset. The verdict is status: healthy / degraded / unhealthy, derived from severity counts (info never degrades). The pre-2.5.2b1 healthy boolean (len(issues) == 0) is gone — it reported False for an info-only database that status called healthy; read issues / severity_summary for the underlying counts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fixNoAuto-fix detected issues
checksNoRegistry check names to run (empty = all). See cpersona.checks.HEALTH_CHECK_NAMES.
agent_idNoAgent ID to check (empty = all agents)
Behavior5/5

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

Annotations only indicate non-read-only. The description details the tool's behavior: auto-repair capability, irreversible repairs (lossy truncation), report-only critical findings, verdict derivation, and legacy changes. This goes well beyond annotations.

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 long but well-structured, front-loading the main purpose and listing specifics. Every sentence adds value, though some details (e.g., historical boolean behavior) could be condensed. Overall, it remains focused.

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 tool's complexity (multiple checks, repair nuances, verdict logic, output interpretation), the description covers all essential aspects. It explains the return values (status, issues, severity_summary) despite no output schema, ensuring complete understanding.

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?

Schema coverage is 100%, so baseline is 3. The description adds significant context: fix parameter's effects (agent-scoped, locked-safe, specific irreversible repairs), checks parameter purpose, and agent_id scope. This extra detail elevates it to 4.

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 explicitly states 'Check memory database health' and enumerates specific checks (contamination, duplicates, etc.), clearly distinguishing it from sibling tools like 'deep_check'. It provides a specific verb and resource.

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?

The description explains when to use the tool (detect issues), when to set fix=true (auto-repair with caveats), and notes alternatives (e.g., using checks parameter for subset). It also clarifies the deprecated boolean behavior, guiding correct interpretation.

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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