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Tool Patterns & Chaining Reference (Monty)

Key difference from Hyperlight

Monty calls tools as regular Python functions — no call_tool() wrapper:

# Monty (this skill)
content = view(path="README.md")
files = glob(pattern="**/*.py")

# Hyperlight (hyperlight-codeact skill)
content = call_tool("view", path="README.md")
files = call_tool("glob", pattern="**/*.py")

CLI Tool Mapping

Copilot CLI Monty function Host implementation
view view(path=..., view_range=...) Path.read_text(), line slicing
create create(path=..., file_text=...) Path.write_text() (fails if exists)
edit edit(path=..., old_str=..., new_str=...) String replace (exactly 1 match)
glob glob(pattern=..., paths=...) Path.glob()
grep grep(pattern=..., paths=..., glob=...) rg subprocess (needs ripgrep)
bash bash(command=..., timeout=...) subprocess.run(shell=True)
sql sql(query=..., db_path=...) sqlite3 module
web_fetch web_fetch(url=..., method=...) curl subprocess
github_api github_api(endpoint=..., method=..., body=...) gh api subprocess

Always available

tool description
view Read file contents or list directory
create Create a new file (fails if exists)
edit Surgical string replacement
glob Find files by glob pattern
bash Run shell commands (high risk)
sql Execute SQLite queries

Conditionally available

tool requires description
grep rg (ripgrep) Search file contents
web_fetch curl Fetch URLs
github_api gh CLI GitHub REST API

Monty-specific notes

  • Use chr(10) for newline character (backslash escapes in some contexts differ)
  • Use string concatenation + or f-strings: f"count: {n}"
  • No classes, no match statements, no third-party imports
  • Supported stdlib: json, re, datetime, sys, os, typing, asyncio
  • Sub-microsecond startup vs ~680ms for Hyperlight

Chaining Patterns

Sequential: search -> read -> analyze

for f in glob(pattern="**/*.py", paths="src"):
    content = view(path=f)
    lines = content.split(chr(10))
    todos = [l for l in lines if "TODO" in l]
    if todos:
        print(f + ": " + str(len(todos)) + " TODOs")

Fan-out / fan-in

repos = ["repo-a", "repo-b", "repo-c"]
all_issues = []
for repo in repos:
    raw = github_api(endpoint="/repos/myorg/" + repo + "/issues")
    all_issues.extend(json.loads(raw))
print("Total issues: " + str(len(all_issues)))

Data pipeline with SQL

sql(query="CREATE TABLE IF NOT EXISTS files (name TEXT, lines INT)")

for f in glob(pattern="**/*.py", paths="src"):
    content = view(path=f)
    n = len(content.split(chr(10)))
    sql(query="INSERT INTO files VALUES ('" + f + "', " + str(n) + ")")

top = sql(query="SELECT name, lines FROM files ORDER BY lines DESC LIMIT 5")
for row in top:
    print(row["name"] + ": " + str(row["lines"]) + " lines")

Error-tolerant batch

files = glob(pattern="*.json", paths="config")
for f in files:
    try:
        raw = view(path=f)
        data = json.loads(raw)
        print(f + ": OK")
    except Exception as e:
        print(f + ": ERROR - " + str(e))

Custom Tool Definitions

Add to the manifest JSON:

{
  "name": "count_lines",
  "description": "Count lines in a file",
  "parameters": {"path": {"type": "string", "required": true}},
  "implementation": {"type": "shell", "command_template": "wc -l < {path}"}
}