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reset_session

Clear a conversation session to start a fresh topic, optionally saving the transcript to a file first.

Instructions

Dump (optionally to a file) and clear a Fable conversation session, so the next ask on that key starts a fresh topic.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
saveNoWrite the transcript to a file before clearing.
modelNoWhich tool's conversation to clear: 'fable' for `ask`, 'opus5' (or 'opus') for `ask_opus5`. The two tools namespace their sessions separately, so the same key names two independent conversations.fable
sessionNoSession key to clear.default

Schema Changelog

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

  1. First observedv0.12.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses that the tool performs two actions—optionally dumping the transcript to a file and clearing the session—and communicates the side effect on subsequent `ask` calls. This is sufficient transparency for a destructive operation, though it does not mention irreversibility or any return value.

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, well-structured sentence where the core action and its consequence are front-loaded. Every phrase earns its place: the optional file dump, the clear action, and the resulting fresh topic all fit naturally without redundancy.

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 simple, parameterless-required destructive tool with fully described parameters in the schema, the description is complete enough for correct invocation. It explains the tool's purpose, effect, and the optional save behavior; no output schema is expected for such a side-effect-oriented operation, and the context signals indicate no hidden complexity.

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?

The top-level description adds no parameter details beyond the input schema, and every parameter in the schema already has a clear, semantically rich description, including defaults and the model namespacing behavior. With 100% schema coverage, the description need not compensate, so the baseline score of 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 states a specific verb-resource pair ('dump and clear a Fable conversation session') and explicitly explains the intended effect ('so the next `ask` on that key starts a fresh topic'). This clearly distinguishes it from sibling tools like `session_list` or `session_peek`, which inspect rather than mutate, and `context_delete`, which targets context entries rather than conversation sessions.

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 to use the tool: before the next `ask` on a session key when you want a fresh topic. It does not explicitly name alternatives or list exclusion criteria, but the purpose and trigger condition are stated without ambiguity, so an agent can infer the appropriate usage scenario.

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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