Skip to main content
Glama

Get audit trail

get_audit
Read-onlyIdempotent

Query a model's durable, append-only audit trail (newest-first): one record per committed reactive cycle (mutations, derivedUpdated, traces, flaggedConstraints, dispatchedEffects, source, sequence). This is the queryable superset of get_history/explain — it survives the in-memory ring buffer rolling over. Filter with an optional 'pathPrefix' (canonical address), an ISO-8601 'from'/'to' window, and 'limit'. (Embedded mode keeps this in memory for the session; remote/paired mode reads the server's durable store.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe model id.
toNoOptional ISO-8601 upper bound (exclusive).
fromNoOptional ISO-8601 lower bound (inclusive).
limitNoOptional max records (default 100, newest-first).
pathPrefixNoOptional canonical address prefix; keep only records that touched a matching field/derivation/constraint.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

The annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds meaningful behavioral context on top: 'durable, append-only', 'newest-first', 'one record per committed reactive cycle', and the embedded vs remote/paired storage-mode distinction. This goes well beyond what annotations alone communicate.

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?

Two information-dense sentences with the core purpose front-loaded before the filter and mode details. The parenthetical lists (mutations, derivedUpdated, traces, etc.) add jargon but are relevant to understanding the record contents and earn their place, so the description is efficient without being minimal.

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 read-only query tool with no output schema, the description supplies the important contextual pieces: what a record contains, ordering, durability, filters, and storage-mode differences. An agent has enough to call the tool correctly and interpret the returned records.

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 description coverage is 100%, so the schema documents all five parameters. The description groups the filters and clarifies that pathPrefix is a 'canonical address', but it does not add substantial parameter-level semantics beyond what the schema already provides; per the calibration baseline, a 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?

The description opens with a specific verb and resource ('Query a model's durable, append-only audit trail') and states the ordering and record granularity. It explicitly differentiates itself from siblings by calling itself 'the queryable superset of get_history/explain' and explains how it survives the ring-buffer rollover, so an agent can distinguish it without opening schemas.

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?

The description gives clear context for when this tool is appropriate: it is the durable superset that outlives the in-memory ring buffer. It names the alternatives (get_history/explain) and the distinguishing condition, but it does not explicitly state when to prefer those alternatives instead, stopping short of full when/when-not coverage.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but get_audit explicitly subsumes get_history and explain, and get_state with paths overlaps get_field, creating minor selection ambiguity. The detailed descriptions help, but an agent could still reach for the wrong getter.

Naming Consistency4/5

Naming is overwhelmingly consistent: snake_case with verb_noun structure and coherent get_/create_/delete_ clusters. Minor deviations like bare verbs (mutate, explain, restore, snapshot) and eval instead of evaluate prevent a perfect score.

Tool Count3/5

27 tools is above the comfortable range and feels heavy, especially with several overlapping audit/state getters that could be consolidated. That said, the domain is broad enough that the count is defensible, so it is heavy but not chaotic.

Completeness4/5

The tool set covers the full model lifecycle well: create, validate, test, mutate, evolve, read, delete, plus snapshot/restore, audit, blobs, views, library, and expression evaluation. Minor gaps like explicit export/import or separate view-management tools are workable around.