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memorybank_search

Search code repositories using natural language queries. Retrieve relevant code snippets with vector similarity matching.

Instructions

Busca código relevante mediante búsqueda semántica vectorial. Usa esta herramienta SIEMPRE que necesites información sobre el código. El projectId es OBLIGATORIO

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topKNoNúmero máximo de resultados a retornar
queryYesConsulta semántica: describe qué estás buscando en lenguaje natural (ej: 'función de autenticación', '¿cómo se validan los emails?')
minScoreNoPuntuación mínima de similitud (0-1). por defecto usa 0.4 y basado en el resultado ajusta el valor
projectIdYesIdentificador del proyecto donde buscar (OBLIGATORIO). Debe coincidir con el usado al indexar
filterByFileNoFiltrar resultados por patrón de ruta de archivo (ej: 'auth/', 'utils.ts')
filterByLanguageNoFiltrar resultados por lenguaje de programación (ej: 'typescript', 'python')
Behavior2/5

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

No annotations provided, so description carries full burden. It only states it's a semantic search, but lacks details on whether it reads/writes, rate limits, prerequisites (e.g., index must exist), or result format. Minimal behavioral context beyond schema.

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?

Two sentences, front-loaded with purpose. Each sentence is meaningful. Could be slightly improved by adding a note on indexing prerequisite, but still concise.

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?

For a tool with 6 params and no output schema, the description should explain return values and usage context (e.g., 'results include chunk and score'). Does not mention output or indexing dependency, leaving gaps.

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% with good parameter descriptions. The description adds only that projectId is mandatory (already in schema). No additional meaning beyond baseline.

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 searches code via semantic vector search, using the verb 'busca' and specifying the resource. It distinguishes itself from sibling tools like memorybank_index_code or memorybank_analyze_coverage.

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 says 'Usa esta herramienta SIEMPRE que necesites información sobre el código' (always use when you need code info), providing strong when-to-use guidance. Does not specify when not to use, but given context of siblings, it's 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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