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Store Memory

memorix_store

Store and index observations like code decisions, bug fixes, and insights across projects. Classify by type, track progress, and persist memories locally to share knowledge across multiple IDEs.

Instructions

Store a new observation/memory. Automatically indexed for search. Use type to classify: gotcha (🔴 critical pitfall), decision (🟤 architecture choice), problem-solution (🟡 bug fix), how-it-works (🔵 explanation), what-changed (🟢 change), discovery (🟣 insight), why-it-exists (🟠 rationale), trade-off (⚖️ compromise), session-request (🎯 original goal). Stored memories persist across sessions and are shared with other IDEs (Cursor, Windsurf, Claude Code, Codex, Copilot, Kiro, Antigravity, Trae) via the same local data directory.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityNameYesThe entity this observation belongs to (e.g., "auth-module", "port-config")
typeYesObservation type for classification
titleYesShort descriptive title (~5-10 words)
narrativeYesFull description of the observation
factsNoStructured facts (e.g., "Default timeout: 60s")
filesModifiedNoFiles involved
conceptsNoRelated concepts/keywords
topicKeyNoOptional topic identifier for upserts (e.g., "architecture/auth-model"). If an observation with the same topicKey already exists in this project, it will be UPDATED instead of creating a new one. Use memorix_suggest_topic_key to generate a stable key. Good for evolving decisions, architecture docs, etc.
progressNoProgress tracking for task/feature observations
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behaviors: automatic indexing for search, persistence across sessions, and sharing across multiple IDEs via local data directory. It also hints at the 'topicKey' parameter's update behavior (upserts). It could improve by mentioning any rate limits, storage limits, or error conditions.

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 appropriately sized and front-loaded, starting with the core purpose. The second sentence adds indexing, and the third provides crucial usage guidance for the 'type' parameter. The final sentence covers persistence and sharing. Some redundancy exists (e.g., 'type' details are partly in the schema), but overall it's efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (9 parameters, nested objects) and lack of annotations/output schema, the description does well by covering purpose, indexing, persistence, sharing, and key parameter guidance. It could be more complete by explaining return values (since no output schema) or error handling, but it's largely adequate for a storage tool.

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 the schema already documents all 9 parameters thoroughly. The description adds minimal value beyond the schema: it elaborates on the 'type' parameter with emoji examples and mentions the 'topicKey' for upserts. However, it doesn't provide additional context for other parameters like 'entityName' or 'progress'.

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 the verb ('Store') and resource ('a new observation/memory'), specifying it's for creating new entries. It distinguishes from siblings like 'memorix_search' (for retrieval) and 'memorix_consolidate' (for merging) by focusing on initial storage with automatic indexing.

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 provides clear context for when to use this tool: for storing new observations/memories, with guidance on using the 'type' parameter for classification. It implies usage for persistent, shared storage across sessions and IDEs. However, it doesn't explicitly state when NOT to use it or mention alternatives like 'memorix_detail' for viewing or 'memorix_update' if it existed.

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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