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mem_session_summary

Save a structured end-of-session summary to persistent memory, preserving key decisions, bug fixes, and discoveries for future AI agent recall.

Instructions

Save a structured end-of-session summary as persistent memory.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesStructured session summary content.
projectNoOptional explicit project. Accepted only when backed by known context or an ambiguous-project recovery.
session_idNoSession identifier. Defaults to manual-save-{project}.
recovery_tokenNoShort-lived token returned by an ambiguous_project error. Required with project_choice_reason.
project_choice_reasonNoMust be user_selected_after_ambiguous_project, and only after the user picked one of available_projects from an ambiguous_project error.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintNoSaid when a summary was saved without a name anybody could find it by.
statusNo
projectNo
candidatesNo
judgment_idNo
observationNo
project_pathNoWhere the project lives, when the answer came from a directory.
project_sourceNo
judgment_statusNo
judgment_requiredNo
Behavior3/5

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

Annotations indicate readOnlyHint=false and destructiveHint=false, covering the basic safety profile. The description adds that the summary is 'persistent memory', which is some behavioral context, but does not disclose details about overwriting, session relationships, or the recovery-token workflow hinted by the schema parameters.

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, direct sentence that front-loads the tool's purpose. It contains no fluff or redundancy, making it efficient and appropriately sized.

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?

While the schema and annotations are rich, the description lacks important context such as the ambiguous-project recovery workflow (recovery_token, project_choice_reason) and how this tool differs from mem_save. Given the tool's complexity, more contextual guidance would be needed for complete understanding, though the schema partially compensates.

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 parameters are thoroughly documented in the JSON schema. The description adds no parameter-specific meaning beyond what the schema already provides, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool saves a structured end-of-session summary as persistent memory, specifying the resource (summary) and action (save). However, it does not explicitly differentiate from sibling tools like mem_save, which also saves to memory, though the 'end-of-session' qualifier provides some distinction.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. There are no references to mem_save, mem_session_end, or exclusion criteria. The description only states what the tool does, leaving the agent to infer usage context.

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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