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search_history

Find past decisions by keyword using BM25 relevance ranking, returning content, context, timestamp, and normalized score. Avoid paging through query_history by searching topics directly.

Instructions

Keyword search over the decision history with BM25 relevance ranking (SQLite FTS5). Each result includes the decision content, context label, timestamp, and a score normalized to [0, 1] where 1 is the best match in the result set. Use this to find past decisions by topic instead of paging through query_history.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return. Defaults to 10.
queryYesKeywords to search for, e.g. 'authentication jwt'.
min_scoreNoMinimum normalized relevance score between 0 and 1. Defaults to 0.
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses non-obvious behavior: rank algorithm (BM25/FTS5), result fields (content, context label, timestamp, score), and score normalization to [0,1]. It doesn't mention permissions or explicit read-only status, but 'search' implies a safe read operation; additional detail on output and scoring is strong.

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?

Two concise sentences, front-loaded with the core action and method, then result details and usage guidance. Every sentence earns its place with no redundancy.

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 there is no output schema, the description appropriately explains return content and score semantics. It covers ranking behavior, result fields, and score normalization, which is complete for a straightforward search tool with 3 simple parameters.

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%, with clear parameter descriptions for query, limit, and min_score. The tool description adds no extra parameter-specific guidance beyond reinforcing 'keyword search' and normalized score, which is sufficient but not additive.

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 performs keyword search over decision history using BM25 relevance ranking (SQLite FTS5), specifying both the verb and resource. It also distinguishes from sibling query_history by noting it is an alternative for finding past decisions by topic.

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?

Explicitly states when to use: 'Use this to find past decisions by topic instead of paging through query_history.' Names the alternative tool query_history, giving clear context, but does not list exclusion conditions or other alternatives, stopping short of a full 5.

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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