Tool Call Strategy

Concept of the skill

Tool-call strategy is the query planner for an agent's external actions. Treat every call as an expensive, stateful evidence-acquisition operation with three costs: latency, tokens, and context pollution.

Coverage

• The three costs of every tool call: token cost (schema overhead and result size), latency cost (round-trip and decision time), context pollution (results persist in attention window)
• The script-vs-call decision gate: deterministic bulk work belongs in a script; reasoning-dependent work belongs in individual tool calls with the agent in the loop
• Tool selection decision tree: file-search vs content-search vs targeted-read vs full-read, harness-native structured tools vs shell fallback, and the harness-agnostic capability map
• Batching independent calls in a single message vs sequential round-trips, and the dependency-detection heuristic
• Redundancy avoidance: the conversation-as-cache mental model, recognising re-reads, re-searches, and re-runs
• Context-efficient patterns: targeted line ranges, bounded verification output, dedicated-tool defaults, provenance notes, and tool-result lifecycle
• Subagent delegation for context protection: when exploration belongs in a disposable subagent context vs the main session
• The poka-yoke principle: design tool usage to prevent mistakes, not just optimise speed
• Cost benchmark heuristics: rough call-count ranges per task type and the "stop and reconsider" red flag

Philosophy of the skill

Every tool call has three simultaneous costs: tokens (schema overhead plus result), latency (network round-trip plus decision time), and context pollution (results persist in the attention window and degrade subsequent reasoning). Agents that issue 12 calls where 3 would suffice are not merely slower — they are measurably less accurate, because noise accumulated in the context window pushes useful signal further from the attention window.