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Get Memory

vault_get_memory
Read-onlyIdempotent

Read structured memory entries from About Me/ files. Retrieve all entries, a specific file, or a section within a file for user preferences and context.

Instructions

Read semantic memory from About Me/ files. These are structured memory files containing dated bullet entries organized under H2 headings. With file: single file content. With file+section: just that H2 section's entries. No args: all files concatenated (frontmatter stripped) — can be large. Returns empty string when no memory files exist yet.

Example: vault_get_memory({ file: "Principles", section: "Decision heuristics (newest first)" })

When to use: Reading user preferences, principles, opinions, or other persistent context stored in About Me/ files. Call vault_list_memory_files first to discover valid file and section names. Prefer vault_read_note for reading non-memory notes.

Returns: Raw markdown text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileNoMemory file name without .md (e.g. "Principles", "Opinions")
sectionNoH2 section heading (e.g. "Decision heuristics (newest first)"). Call vault_list_memory_files first to discover valid names.
Behavior5/5

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

Adds value beyond annotations: describes empty string return when no files exist, explains no-args behavior (concatenated files, frontmatter stripped, potentially large). No contradiction with annotations.

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?

Well-organized: opens with core purpose, then parameter behavior, then usage guidance, then return value. Each sentence adds value without redundancy. Efficient and scannable.

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?

Covers all necessary aspects: behavior with different args, empty result, large output, discovery of valid parameters, and recommendations. No gaps remain given the tool's simplicity and annotations.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. Description adds meaning by explaining how file and section combine (file only vs file+section) and gives an example call. However, it doesn't go beyond what the schema already implies, so a 4 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 clearly states 'Read semantic memory from About Me/ files' and details behavior for different argument combinations. It distinguishes from sibling tools by recommending vault_read_note for non-memory notes.

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?

Explicitly states 'When to use' and advises calling vault_list_memory_files first to discover valid file/section names. Provides alternatives (vault_read_note) and context for when to use without arguments.

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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