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Glama

get_session_detail

Retrieve full details for an automation session, including logs, action history, and timing; use it after listing sessions to inspect what happened, with partial results for active sessions.

Instructions

Get full detail for an automation session including logs, action history, and timing. Use after list_sessions to inspect what happened. For an in-flight session id (one that get_task_status reports as currentSessionId), returns a partial snapshot with final: false and the latest known action; the full payload becomes available once the session finishes. Returns structured JSON or human-readable text report. Related: list_sessions (find session IDs first), get_logs (get raw log entries), get_task_status (check if a session is still in flight).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoOutput format: json (structured, default) or text (human-readable report)
sessionIdYesSession ID from list_sessions

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries full burden and does disclose a non-obvious behavior: in-flight sessions return a partial snapshot with `final: false`, and the full payload only arrives after completion. It also discloses the read-vs-write nature implicitly and output format options. It does not mention permissions or rate limits, but for a read tool the disclosed partial-payload semantics are the key risk and are covered.

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?

Front-loaded with purpose and usage before the in-flight caveat and related-tools list. Every sentence carries information, though the 'Related:' enumeration is slightly verbose relative to the rest.

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, and the description compensates by describing what the payload contains, the two output formats, and the partial-vs-full return behavior. Nothing essential for correct invocation is missing.

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 both parameters (sessionId, format enum) are already fully documented in the schema. The description's mention of 'structured JSON or human-readable text report' restates the format parameter rather than adding new semantics. Baseline 3 applies.

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 and resource ('Get full detail for an automation session') and enumerates the payload contents (logs, action history, timing). This clearly separates it from siblings like get_logs, get_session_replay, and list_sessions without opening any schema.

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?

Gives an explicit trigger ('Use after list_sessions to inspect what happened') and names three alternatives with their distinct roles: list_sessions (find IDs), get_logs (raw entries), get_task_status (in-flight check). An agent can route between these siblings directly from the text.

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