tool-patterns.md

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 simple f-strings: f"count: {n}"
• No f-string format specs — f"{x:<10}", f"{x:>5}", f"{x:.2f}" all error
• No str.format() — "{:<10}".format(x) errors
• No os.path or os.walk — use glob() to find files, view() to read them
• For tabular output, use manual padding:

  def pad(s, w):
      s = str(s)
      return s + " " * max(0, w - len(s))

• No classes, no match statements, no third-party imports
• Supported stdlib: json, re, datetime, sys, os.environ (no os.path), typing, asyncio
• Sub-microsecond startup vs ~680ms for Hyperlight

Return types (critical — wrong assumptions cause retries)

• glob(pattern=...) → list[str] e.g. ["src/app.py", "src/utils.py"]
• view(path=...) → str (full file content)
• bash(command=...) → dict with stdout, stderr, returncode
• mcp_call(server=..., tool=..., ...) → str

Chaining Patterns

Sequential: search -> read -> analyze

# Do everything in one program — no scouting needed
for f in glob(pattern="**/*.py", paths="src"):
    try:
        content = view(path=f)
    except Exception as e:
        print(f + ": ERROR - " + str(e))
        continue
    lines = content.split(chr(10))
    todos = [l for l in lines if "TODO" in l]
    if todos:
        clean = f.replace("./", "")
        print(clean + ": " + str(len(todos)) + " TODOs")

Cross-file import analysis

import re
files = glob(pattern="src/**/*.py")
deps = {}
for f in files:
    clean = f.replace("./", "")
    try:
        content = view(path=f)
    except Exception:
        continue
    imports = []
    for line in content.split(chr(10)):
        if line.startswith("from src.") or line.startswith("import src."):
            m = re.match(r"(?:from|import)\s+(src\.\S+)", line)
            if m:
                imports.append(m.group(1))
    if imports:
        deps[clean] = imports
for mod, imps in deps.items():
    print(mod + " -> " + ", ".join(imps))

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))

MCP server calls

When .mcp.json is present, mcp_call bridges to MCP servers:

# Search Microsoft docs
result = mcp_call(server="microsoft-docs", tool="microsoft_docs_search",
                  query="Azure Functions")
print(result[:200])

# Chain: search docs then fetch a page
import json as _json
hits = _json.loads(mcp_call(server="microsoft-docs",
                           tool="microsoft_docs_search",
                           query="Azure Functions"))
url = hits["results"][0]["url"]
page = mcp_call(server="microsoft-docs", tool="microsoft_docs_fetch", url=url)
print(page[:500])

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}"}
}