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Glama

get_event_log

Inspect the forensic event log for a task, finding, attempt, plan, or phase to see every state change, actor, and reason. Use it to find why an entity reached needs_human_review.

Instructions

Forensic event log for a specific entity (task / finding / attempt / plan / phase). Used to investigate 'why did this task end up in needs_human_review' — the events table records every state change with the actor and reason.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
LimitNo
SinceNo
EntityIdYes
as_agentNo
EntityTypeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It helpfully discloses that the log records every state change with actor and reason, and 'get' implies a read, but it says nothing about ordering, pagination behavior (Limit/Since), auth/impersonation (as_agent), or how large the log can get. Meaningful but incomplete behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences: the resource definition is front-loaded, then the investigative purpose. No filler, no repetition of the tool name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema and no annotations, so the description has to cover a lot. It conveys what the log contains (state changes with actor and reason), but for a 5-parameter read tool it under-specifies time filtering, pagination, and the as_agent parameter, leaving real gaps for an agent to resolve by trial.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate, and it only covers the entity dimension: the entity-kind enumeration directly clarifies EntityType and 'for a specific entity' implies EntityId. Limit, Since (a nested object), and as_agent are left entirely opaque, and the nested Since is especially risky to guess at.

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?

States a specific verb (get) and resource (forensic event log) scoped to a specific entity, and enumerates the entity kinds (task / finding / attempt / plan / phase). This is clearly distinguishable from retrieval siblings like get_finding or get_plan_dashboard, which return entity state rather than the change history.

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?

Gives a concrete investigative scenario ('why did this task end up in needs_human_review'), which tells the agent when this tool is the right one. It stops short of naming alternatives or an explicit when-not-to-use, so it is clear context without exclusions.

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