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search_best_practices

Retrieve best practices for code patterns and recommendations by searching across all guides or within a specific guide using natural language queries.

Instructions

Semantic search for best practices across all guides or within a specific guide.

Use this tool when:

  • The user asks a conceptual question: "How to hash passwords?"

  • You need to find code patterns or recommendations

  • You want to deep dive into a specific guide (provide guide_id)

IMPORTANT: Always formulate queries in English for optimal search quality, even if the user's question is in another language.

Args: query: Natural language search query in English. Be specific and descriptive. guide_id: Optional guide ID to search within (e.g., 'python/django-security'). If provided, searches only within this guide (returns top 3 sections). If not provided, searches across all guides (returns top 5 results). namespace: Filter by technology (e.g., 'python', 'go'). Ignored if guide_id is set.

Returns: Relevant chunks with content and source information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
guide_idNo
namespaceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations provided, but the description thoroughly explains behavior: semantic search, result count differences based on guide_id, namespace filtering, and return type. No 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?

Well-organized with bullet points and sections. Every sentence adds value; no redundant or filler content.

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 the tool's complexity (3 params, search function) and the presence of an output schema, the description covers all necessary context: purpose, when to use, parameter details, behavioral nuances.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description compensates fully by explaining each parameter's type, constraints, and effect on results (e.g., guide_id top 3 vs top 5, namespace ignored if guide_id set).

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 'Semantic search for best practices across all guides or within a specific guide' using a specific verb and resource. It implicitly distinguishes from siblings by focusing on best practices search.

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 lists three 'Use this tool when' scenarios and a language guideline. However, it does not explicitly state when not to use it or directly compare to alternative siblings.

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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