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recall_decisions

Recall concrete technical decisions from past sessions using a semantic query. Returns decision facts with thread IDs to avoid re-deciding already settled issues.

Instructions

Recall concrete technical DECISIONS the user made across ALL past sessions (and why), semantically matched to a query. Use BEFORE re-deciding something the user may have already settled. Returns decision facts, each with the threadId it came from. Requires the user to have distilled some threads.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax facts to return (default 20).
queryYesWhat to recall about (e.g. "auth token refresh", "database migration approach").
projectNoSubstring-match the project path to scope results. Empty = all projects.
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses a prerequisite (requires distilled threads) and mentions the return format includes threadId. However, it does not explicitly state that the operation is read-only or non-destructive, which would be helpful.

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 three sentences long and front-loads the purpose. It is clear and actionable, though the second sentence could be integrated for slightly better flow. Overall, it is concise and well-structured.

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?

For a recall tool with no output schema, the description adequately explains what is returned (decision facts with threadId) and the prerequisite. It could be more complete by noting that results are based on previously distilled threads, but it covers the essential aspects.

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 coverage is 100%, so baseline is 3. The description for the 'query' parameter adds minimal additional meaning beyond the schema ('What to recall about'). The 'limit' and 'project' parameters are not further elaborated in the description, so it does not significantly enhance understanding beyond the 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 it recalls 'concrete technical DECISIONS' across past sessions, semantically matched to a query. It specifies the resource (decisions) and the action (recall), and distinguishes it from sibling tools like 'recall_gotchas' and 'record_decision' through naming and context.

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 says to use this 'BEFORE re-deciding something the user may have already settled,' providing clear when-to-use guidance. It also implies a prerequisite (requires distilled threads). While it doesn't mention alternatives by name, the usage context is clear.

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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