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search_patterns

Read-onlyIdempotent

Search anti-patterns and success-patterns by keyword to learn what to avoid and what works before submitting a PR.

Instructions

Search anti-patterns and success-patterns by keyword.

Use this to find relevant patterns before submitting a PR. Anti-patterns show what NOT to do, success-patterns show what works.

Args: query: Search keyword (e.g. "duplicate PR", "missing tests", "out of scope", "breaking change") pattern_type: Filter type — "all" (default), "anti-pattern", or "success-pattern" limit: Max results to return (default 10)

Returns: list of dicts with keys: key, title, symptom, fix_action, source_pr, type, file

Example: search_patterns("duplicate PR") → finds anti-patterns about duplicate submissions search_patterns("timeout", "anti-pattern") → finds timeout-related failure patterns

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
pattern_typeNoall
Behavior3/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds behavioral context (search, filter, return format) but doesn't introduce new traits beyond what annotations imply.

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 well-structured with sections for purpose, usage, args, returns, and examples. It is efficient, though slightly verbose with the example lines; still, every sentence adds value.

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?

Given no output schema, the description explains the return format (list of dicts with specific keys) and provides examples. The tool's complexity is low, and the description is adequate for correct invocation.

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?

Despite 0% schema coverage, the description fully explains each parameter with examples: query (with keyword examples), pattern_type (options: all, anti-pattern, success-pattern), limit (default 10). This adds significant meaning 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 'Search anti-patterns and success-patterns by keyword,' providing a specific verb and resource. It distinguishes itself from sibling tools like list_open_prs and get_case_study by focusing on patterns.

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 'Use this to find relevant patterns before submitting a PR,' giving clear context of when to use. It also clarifies the two types of patterns but doesn't explicitly state when not to use, though the context is sufficient.

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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