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Load past decisions, preferences, and project knowledge relevant to your current task. Call at session start or when switching topics to retrieve broad context instead of specific facts.

Instructions

Load relevant memories for the current task, designed for session bootstrapping. This is a read-only operation identical to recall internally, but optimized for broad context loading rather than specific questions. Call context at the start of every conversation, passing a description of what you are working on, to retrieve past decisions, preferences, and project knowledge. Also call when switching topics mid-session. Use context (not recall) for "what do I need to know about X?" and recall for "what specifically was decided about Y?". Returns up to max_memories results ranked by relevance. Costs 1 operation. Returns empty list (not error) if no relevant memories exist.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
current_contextYesDescription of what you are currently working on. Be specific: 'refactoring the authentication middleware in the Express API' retrieves better context than 'working on auth'. This is the search query for memory retrieval.
agent_idNoAgent instance identifier. Must match the agent_id used when storing memories. Default: 'default'.default
user_idNoUser identifier. When provided, also retrieves user-scoped memories shared by other agents.
max_memoriesNoMaximum memories to return, 1-20. Default 5. Use 10-15 at session start for broad context loading, 3-5 for topic switches.
Behavior5/5

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

No annotations provided, but description fully covers behavior: read-only operation, costs 1 operation, returns up to max_memories, empty list if no relevant memories, and internal similarity to recall. No contradictions.

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?

Description is front-loaded with key purpose and usage, then provides additional details. Each sentence adds value, but it could be slightly more concise. Still 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?

No output schema, but description covers return behavior (up to max_memories, empty list not error). For a read-only retrieval tool with good parameter guidance, this is sufficiently complete.

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 value beyond schema: examples for specific context query, usage suggestions for max_memories (e.g., 10-15 at start).

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?

Description clearly states it loads relevant memories for session bootstrapping, distinguishes from recall by noting it's optimized for broad context vs specific questions. Verb 'load' plus resource 'memories' is specific.

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 instructs to call at start of every conversation and when switching topics. Contrasts with recall for specific queries, providing clear when-to-use and when-not-to-use guidance.

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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