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Store key development artifacts (decisions, bug fixes, patterns) with automatic embedding and deduplication to prevent duplicates.

Instructions

Store a new memory with automatic embedding and deduplication.

Primary write path — call after a significant decision, tricky bug fix, or new pattern discovery. Auto-generates embeddings via Ollama (1024d) or local all-MiniLM-L6-v2 (384d fallback). Detects project scope from CWD. Deduplicates at cosine similarity > 0.82 — boosts existing memory instead of creating duplicates.

Read-only: no. Creates or updates a row in LanceDB. Triggers background consolidation if interval has elapsed (non-blocking, fire-and-forget).

Args: content: Full memory content in Markdown. Required. First 200 chars auto-summarized if summary is omitted. summary: One-line summary shown in recall results. Default: auto-generated. type: 'episodic' (session captures, decay 0.15/day), 'semantic' (extracted knowledge, decay 0.03/day), or 'permanent' (decisions/ conventions, never decays). Default: 'episodic'. scope: Isolation prefix. Auto-detected from project config or CWD path hash. Use 'global' for cross-project memories. weight: Initial importance 0.0–1.0 (default 0.5). Decays exponentially per type's decay_rate. Frequently-accessed memories get counteracting boost. tags: Optional labels for filtering, e.g. ['testing', 'api', 'pitfall']. embedding: Pre-computed vector. Auto-generated from first 2000 chars of content if omitted. Padding/truncation handled automatically. diff_text: Optional unified diff for code-change memories. diff_files: Optional list of changed file paths. error_trace: Optional stack trace for error/bug memories.

Returns: dict: {'id': <memory_id>, 'summary': , 'action': 'created'} On dedup match: action='boosted' with existing ID and similarity score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesFull memory content in Markdown. Required. First 200 chars auto-summarized if summary omitted.
summaryNoOne-line summary for recall display. Auto-generated from first 200 chars of content if omitted.
typeNoMemory type: 'episodic' (session captures, decay 0.15/day), 'semantic' (extracted knowledge, decay 0.03/day), or 'permanent' (conventions/decisions, never decays).episodic
scopeNoScope prefix for isolation. Auto-detected from project config or CWD. Use 'global' for cross-project memories.
weightNoInitial importance 0.0–1.0. Decays exponentially per type. Frequently accessed memories get counteracting boost.
tagsNoOptional labels for filtering, e.g. ['testing', 'api', 'pitfall']. Auto-detected from content if omitted.
embeddingNoPre-computed embedding vector. Auto-generated from first 2000 chars of content via Ollama or local model if omitted.
diff_textNoOptional unified diff text for code-change memories.
diff_filesNoOptional list of changed file paths for code-change memories.
error_traceNoOptional stack trace for error/bug memories.
Behavior5/5

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

With no annotations, the description carries full burden and thoroughly discloses behaviors: write operation, creates or updates rows, triggers non-blocking background consolidation, deduplication at cosine similarity > 0.82, embedding generation via Ollama or local model, and decay rates per memory type.

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?

The description is comprehensive but well-structured, with clear sections and front-loaded purpose. It contains valuable details, though slightly verbose; still, every sentence adds meaning and earns its place.

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?

Given 10 parameters and no output schema, the description explains the return dict and covers complex behaviors like dedup, consolidation, and embedding. It provides enough context for an agent to invoke correctly without gaps.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds significant value beyond schema: details on embedding model (Ollama 1024d, fallback 384d), dedup threshold, decay rates, auto-summarization of first 200 chars, and default behaviors for optional parameters.

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 it stores a new memory with automatic embedding and deduplication, and specifies it's a write path for significant decisions, tricky bug fixes, or new pattern discovery. It effectively distinguishes from sibling tools like recall (read) and delete_memory.

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 explicitly recommends when to use: after significant decisions, bug fixes, or pattern discovery. It implies its write-only nature but does not explicitly state when not to use, though the context is clear given sibling tools. The auto-dedup and background consolidation notes guide usage.

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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