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recall_for_task

Retrieve reusable task memories before starting coding work. Load project-scoped memories first, then optional global lessons, using debug mode for errors.

Instructions

Retrieve reusable task memories before starting substantive coding work. Use this at the beginning of implementation, debugging, migration, configuration, investigation, refactor, or optimization tasks to load project-scoped memories first and optional global lessons second. Use mode="debug" when the user reports an error, failing command, regression, or incident; include error_signatures and related_files when available. Use search_knowledge instead for ad hoc semantic note search that is not tied to the current task. This tool is read-only and returns ranked memories plus optional related-note context without writing anything.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoUse task for normal work and debug when the task starts from an error, failing test, or incident.task
taskYesCurrent task context used to retrieve relevant project and global memories.
optionsNoOptional limits and relation expansion controls for recall results.
Behavior5/5

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

No annotations provided, so description carries full burden. It states the tool is read-only, returns ranked memories plus optional related-note context, and writes nothing. This sufficiently discloses behavioral traits without contradictions.

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?

Single paragraph that is front-loaded with purpose and well-structured. Every sentence adds value; no wasted words. Efficiently covers usage, mode, and exclusions.

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 complexity (3 parameters, nested object, no output schema), description explains return values (ranked memories plus optional context) and main use cases. Lacks details on exact output format but sufficient for agent understanding.

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 baseline is 3. The description adds some context (e.g., use debug mode for errors) but mainly restates schema information. No significant additional semantics beyond schema.

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 tool retrieves reusable task memories before starting substantive coding work, with specific verb ('Retrieve') and resource ('reusable task memories'). It distinguishes from sibling tool search_knowledge by specifying when to use that alternative.

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?

Provides explicit guidance on when to use (at beginning of implementation, debugging, etc.) and when not to (ad hoc note search via search_knowledge). Also gives mode-specific instructions (debug mode for errors) and lists optional parameters for 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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