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session_artifacts

List files generated by executed code in a session, excluding runner internals, to identify outputs and artifacts for review or download.

Instructions

List files created by executed code in a session (excluding runner internals like main.py/run.out).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYes

Schema Changelog

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

  1. Changed4 schema fields changedv0.2.0
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / session_id / title
      Added value: +"Session Id"
    • addedInput schema / title
      Added value: +"session_artifactsArguments"
    • changedOutput schema / (root)
      Previous value: -{
      -  "additionalProperties": true,
      -  "type": "object"
      -}New value: +null
  2. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

The description adds meaningful behavioral context by stating that runner internals like main.py/run.out are excluded, which is useful for an agent deciding whether to call this tool. With no annotations or output schema, though, it still leaves unspecified whether results are file names, full paths, or include metadata, and how invalid session IDs are handled.

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?

The description is a single, front-loaded sentence that states the action and scope immediately, then adds a useful parenthetical exclusion. Every word contributes to the tool's understanding, with no redundancy or filler.

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?

For a simple one-parameter list operation, the description covers the core purpose and even clarifies exclusions, which is mostly adequate. However, without an output schema or sibling differentiation, it does not fully describe the return shape, edge cases, or how it relates to tools like session_files, leaving some gaps for the agent.

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 description coverage is 0%, and the description does not explicitly explain session_id beyond the phrase 'in a session.' The parameter name/title are self-evident, but the description does not compensate for the complete absence of parameter-level documentation with examples or format guidance.

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 uses a specific verb ('List') and identifies a precise resource ('files created by executed code in a session'), while also excluding runner internals like main.py/run.out. This clearly distinguishes it from sibling tools such as session_list and session_files by defining exactly what artifact scope it covers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'created by executed code' implies when the tool is appropriate, and the explicit exclusion of runner internals provides a scoping rule. However, the description does not name any alternative tools or state when not to use this one, leaving the choice mostly to inference.

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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