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find_complexity_hints

Identifies methods with performance risk patterns such as O(n²), recursion, or n+1 queries, sorted by call frequency to prioritize hotspots.

Instructions

Return methods with complexity hints, sorted by CALLS in-degree descending.

Detects static complexity patterns stored on Method nodes during indexing:
O(n2)-candidate, O(n2)-list-scan, recursive, n+1-risk, unbounded-query.

Args:
    repo_name:    Repository to query.
    min_severity: Severity filter:
                  ``"low"``    — include all hints
                  ``"medium"`` — exclude hints that are ONLY ``recursive``
                  ``"high"``   — only include hints containing ``O(n2)`` or ``n+1-risk``

Returns:
    List of dicts: ``method_fqn``, ``file_path``, ``line_start``,
    ``complexity_hint``, ``call_degree``.
    Sorted by ``call_degree`` descending (most-called methods first).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_nameYes
min_severityNomedium

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, but the description discloses the sorting order, the list of complexity patterns detected, and the effect of min_severity. It is transparent about the tool's behavior as a read-only query.

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?

The description is succinct, well-structured with summary, pattern list, args, and returns. Every sentence is purposeful.

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 the tool has 2 parameters and an output schema described, the description covers purpose, parameters, output format, and behavior. It lacks edge case handling but is sufficient for most queries.

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 0%, but the description adds detailed semantics for min_severity with filtering logic, and describes repo_name minimally. This compensates well for the lack of schema descriptions.

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 returns methods with complexity hints sorted by call in-degree, distinguishing it from sibling analysis tools that focus on different patterns like callers, callees, or taint flows.

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 usage context via the min_severity parameter behavior, but does not explicitly contrast with sibling tools; however, the naming and description make the use case 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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