Skip to main content
Glama
Cloto-dev

CPersona

Official
by Cloto-dev

get_session_findings

Read-only

Retrieve full-database storage-integrity findings with per-probe kind, check, severity, and health to identify forgotten or contradictory state. Read-only, on-demand audit.

Instructions

Pull the storage-integrity findings on demand (SuperAuditor v1 pull contract, docs/SUPERAUDITOR_STANDARD.md) instead of reading them off check_health. Same detector as check_health(fix=false) over the WHOLE database, delivered as findings: each carries kind (the finding's name: a check registry name, or an escalation tier this seam mints for a runner that grades its own severity, e.g. null_embedding_pipeline_down — a tier is NOT a registry name), check (the registry name that produced it, so check_health(checks=[finding['check']]) re-runs exactly that probe) and a static per-kind severity (critical = the read contract is broken now / warn = two stored facts contradict / info = an observation). check_health's own instance verdict rides along as health_severity; a probe that raised is reported as kind check_crashed instead of failing the pull, so a partial result says which probe is missing. Read-only, never repairs. NOT free, though: the registry runs unfiltered, which includes two whole-database reads (the FTS5 integrity-check over both indexes, and PRAGMA quick_check over the file), so every pull is O(database) on a channel meant to be pulled once a session — budget it by call frequency. There is deliberately no cheap subset: choosing which probes run would be choosing which forgotten state stays forgotten. Findings are NOT filtered by agent_id or project_id — the channel surfaces forgotten state, and slicing it by the caller's bucket would hide exactly the rows that were forgotten (scope a repair with check_health(agent_id=...)). Honest caps: findings holds at most per_kind_limit rows per kind, capped_kinds names every kind that had more (observed, not inferred from count == limit), total and the counts describe the RETURNED set only, and per_kind_limit echoes the limit applied. summary restates the same trimmed set in prose (pass include_summary=false to skip paying for it). On a shared remote transport with no session_key declared the response carries identity_shared: true — this server has no session-scoped probes, so the key is a partition hint, not a filter. _meta.server_version identifies the running instance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_keyNoOpaque client-declared session identity (partition hint, not authentication). Empty on a non-stdio transport marks the response identity_shared.
per_kind_limitNoMaximum findings returned per kind (default 5, minimum 1). Kinds that hit it are listed in capped_kinds.
include_summaryNoInclude the human-readable `summary` rendering (default true). It restates `findings` in prose — set false when machine-reading.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.5.10

TDQS

A4.9/5.0
Behavior5/5

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

The description goes far beyond the readOnlyHint annotation by disclosing the O(database) cost, the unfiltered registry, the check_crashed degradation behavior, the cap semantics, and the identity_shared case. It also explicitly states it is read-only and never repairs, matching the readOnlyHint annotation.

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 text is long and dense, but each sentence carries meaningful caveats or field semantics and the purpose is front-loaded. It loses a point for being a single wall of text; light formatting or grouping would improve scannability without dropping content.

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?

There is no output schema, yet the description covers the shape of findings (kind, check, severity, health_severity), the special check_crashed finding, the caps/totals semantics, identity_shared, and _meta.server_version. An agent has everything it needs to invoke and interpret the result correctly.

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?

Although schema coverage is 100%, the description adds essential semantics: session_key is a partition hint rather than a filter, cap-related fields describe the returned set only, and include_summary=false can skip the prose. These details materially change how an agent would set and interpret the parameters.

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 opens with a specific action ('Pull the storage-integrity findings on demand') and explicitly contrasts it with check_health, calling out the same detector over the whole database. This clearly differentiates the tool from its closest sibling and leaves no ambiguity about the resource it operates on.

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?

It tells the agent when to use the tool ('instead of reading them off check_health'), when to prefer the sibling ('scope a repair with check_health(agent_id=...)'), and what to avoid ('NOT free', 'budget it by call frequency', 'deliberately no cheap subset'). The when/when-not guidance is explicit and concrete.

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