harness-optimizer

name	harness-optimizer
description	Optimize an AI agent's harness for MCP-Atlas benchmark. Use when analyzing execution traces, diagnosing failures, and proposing improved prompts, skills, or harness code.

Harness Optimizer

You are optimizing an AI agent's harness for MCP-Atlas — a benchmark of tool-calling tasks where the agent uses Model Context Protocol (MCP) servers to answer questions by querying APIs, databases, filesystems, and web services.

Objective

Maximize the agent's coverage score on the benchmark. The agent receives a task prompt and a set of MCP tools, makes tool calls to gather information, and produces a final answer. A judge LLM scores the answer against ground-truth claims (fulfilled=1.0, partially_fulfilled=0.5, not_fulfilled=0.0). The harness controls what the agent sees (prompts, strategies) and how it behaves before each task.

What You Can Modify

• prompts/system.md — the agent's system prompt
• skills/*/SKILL.md — on-demand skill library (YAML frontmatter + markdown body)
• harness.py — scaffolding code with three hook points:
  • build_system_prompt(base_prompt, skills, task_prompt) -> str — override prompt assembly
  • build_user_prompt(task_id, task_input) -> str | None — customize per-task prompt
  • pre_solve(task_metadata) -> dict — preprocess task metadata before the agent loop
• tools/ — tool implementations
• memory/*.jsonl — episodic memory entries

What You Must NOT Do

• Do not modify anything under evolution/ (read-only archive)
• Do not hardcode task-specific answers or task IDs into any file
• Do not break the agent's MCP tool interface

The Candidate Archive

evolution/candidates/ is your primary knowledge source. Each subdirectory contains:

cycle_NNN_cand_M/
├── snapshot/        # Complete workspace files at time of proposal
├── scores.json      # {score, cost, selected, pareto_optimal, ...}
└── traces/          # Symlink to observation batch (full execution traces)

Browse it freely:

ls evolution/candidates/
cat evolution/candidates/*/scores.json | jq '.score'
grep -r "pattern" evolution/candidates/*/snapshot/
diff evolution/candidates/cycle_001_cand_0/snapshot/harness.py \
     evolution/candidates/cycle_002_cand_0/snapshot/harness.py
cat evolution/candidates/<name>/traces/*.jsonl | jq '.task_id, .success, .score'

Execution traces (JSONL) contain for each task: task_id, task_input, success, score, feedback_detail, and full conversation (every message + tool call).

How to Work

1. Diagnose — Browse evolution/candidates/. Compare high-scoring vs low-scoring candidates' code and traces. Use grep, jq, diff selectively. You do NOT need to read everything.
2. Hypothesize — Form specific hypotheses about what causes failures and what patterns lead to higher scores. Look for recurring failure modes in traces.
3. Propose — Write new workspace files. You may:
   • Make targeted edits to the current workspace
   • Copy a high-scoring prior candidate's code and improve on it
   • Write completely new files from scratch Choose whichever approach your diagnosis suggests will work best.

Tips for MCP-Atlas

• Tasks span calendars, databases (MongoDB), filesystems, git repos, web search, geographic/OSM queries, Wikipedia lookups, and multi-step API orchestration
• Common failure modes: incomplete answers (missing sub-parts of compound questions), not retrying failed tool calls with different parameters, guessing instead of querying tools, not exploring filesystem/repos before answering
• The pre_solve hook can normalize task metadata (e.g. fix tool format issues)
• Multi-part questions need systematic decomposition — count required facts, verify each
• Geographic questions need routing tools for distances, not coordinate estimation
• The agent has access to 20 MCP servers — encourage it to explore broadly