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knowledge_search

Search accumulated session knowledge to find past work, decisions, and context using keywords, categories, or free text. Retrieves relevant historical data automatically extracted from saved session ledgers and handoffs.

Instructions

Search accumulated knowledge across all sessions by keywords, category, or free text. The knowledge base grows automatically as sessions are saved — keywords are extracted from every ledger and handoff entry. Use this to find related past work, decisions, and context from previous sessions.

Categories available: debugging, architecture, deployment, testing, configuration, api-integration, data-migration, security, performance, documentation, ai-ml, ui-frontend, resume

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNoOptional project filter. If omitted, searches across all projects.
queryYesFree-text search query. Searched against session summaries using full-text search.
categoryNoOptional category filter (e.g. 'debugging', 'architecture', 'ai-ml'). Filters results to sessions in this category.
limitNoMaximum results to return (default: 10, max: 50).
enable_traceNoIf true, returns a separate MEMORY TRACE content block with search strategy, latency breakdown, and scoring metadata for explainability. Default: false.
activationNoConfiguration for ACT-R inspired Spreading Activation. Use this to find structurally related memories beyond direct semantic/keyword hits.
Behavior3/5

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

No annotations provided, so description carries full burden. It explains how the knowledge base is populated (automatic extraction from saved sessions) and mentions the enable_trace feature for metadata. However, lacks disclosure on safety (read-only vs destructive), rate limits, or what happens when queries match nothing.

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?

Three dense sentences followed by a structured category list. Front-loaded with action verb. Every sentence earns its place: defines operation, explains data source mechanism, and states use case. No redundancy or fluff.

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

Completeness3/5

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

Adequate for basic usage but gaps remain given complexity. No output schema is provided, yet the description doesn't explain return format (e.g., what constitutes a 'result'). The complex ACT-R spreading activation feature is documented in the schema but not mentioned in the main description, which could overlook this distinctive capability.

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% (baseline 3). Description adds significant value by enumerating all valid category options (debugging, architecture, etc.) since they are not defined as schema enums. It also contextualizes the query parameter by explaining the full-text search mechanism against session summaries.

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?

Excellent specificity with verb 'search' and resource 'accumulated knowledge across all sessions'. Explicitly distinguishes from session-specific siblings by emphasizing 'across all sessions' and 'previous sessions'. Also explains the underlying mechanism (keywords extracted from ledgers/handoffs) which clarifies scope.

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?

Provides clear when-to-use ('find related past work, decisions, and context from previous sessions') and implies scope distinction from session-level tools via 'across all sessions'. Lacks explicit when-not-to-use or named sibling alternatives (e.g., contrast with session_search_memory).

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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