Evolving Tool User Creator

Evolving Tool User to Tool Creator (UCT Framework)

This skill enables Claude to go beyond using existing tools by creating new reusable tools on-the-fly during problem-solving. Based on the UCT (User-to-Creator Transformation) framework, Claude harvests its own reasoning traces—the step-by-step logic it produces while solving a problem—and distills them into tested, reusable code artifacts (functions, scripts, CLI utilities). A memory consolidation mechanism maintains this growing tool library: merging duplicates, deprecating broken tools, and ensuring high reuse rates across future tasks.

When to Use

• When the user solves a multi-step computational or data-processing problem and wants the solution captured as a reusable function or script
• When the user repeatedly encounters similar sub-problems (e.g., parsing specific formats, running domain-specific calculations) and wants Claude to auto-generate a helper library
• When building an agent pipeline that should improve itself over time without retraining—accumulating a library of tested utilities
• When the user asks Claude to solve a problem and no existing tool fits, requiring on-the-spot tool creation with validation
• When refactoring a one-off script into a maintained, tested utility that can be invoked in future sessions
• When orchestrating a multi-domain reasoning task (math, science, data analysis) where specialized micro-tools would accelerate subsequent steps

Key Technique

The UCT framework reframes the agent's relationship with tools. Traditional tool-integrated reasoning treats tools as a fixed inventory—the agent picks from what exists. UCT instead treats every reasoning trace as a potential tool blueprint. When Claude works through a complex problem (geometric calculation, data transformation, API orchestration), the intermediate steps encode implicit problem-solving capabilities. UCT harvests these into explicit, executable code.

The framework operates in three coupled phases. The Online Task Loop uses ReAct-style reasoning where, at each step, the agent decides whether to use an existing tool, create a new one, or reason further. When creation is triggered, a Build Loop enters an isolated environment: it generates both the implementation code and test scripts simultaneously, then iterates using sandbox execution feedback and critic review until the tool passes. Finally, an Offline Memory Consolidation phase periodically cleans the library—merging similar tools, eliminating duplicates, and deprecating tools with high failure rates or low reuse.

The critical insight is the dual-verification gate in the Build Loop. Tools are not accepted based on generation alone. They must pass runtime sandbox tests AND a critic review that checks for edge cases, correctness, and API contract adherence. This prevents erroneous tool outputs from poisoning downstream reasoning—a key failure mode in naive tool-creation approaches. The result: 93%+ reuse rates and 20-23% performance gains on math and science benchmarks.

Step-by-Step Workflow

1. Receive the task and assess the tool gap. Before jumping to a solution, inventory what existing tools/functions are available. Identify sub-problems where no current tool fits—these are tool-creation candidates. Ask: "Would a reusable function here save effort on this or future tasks?"

2. Solve the task using ReAct-style reasoning. Work through the problem step-by-step, explicitly documenting each reasoning step and intermediate computation. Keep the reasoning trace detailed—it becomes the blueprint for tool extraction.

3. Identify reusable sub-capabilities in the reasoning trace. Scan the trace for steps that are (a) self-contained, (b) parameterizable, and (c) likely to recur. Examples: a coordinate geometry calculation, a specific data parsing routine, a validation check pattern.

4. Generate a build ticket for each tool candidate. Write a concise specification: function name, purpose, input parameters with types, expected output, and 2-3 concrete test cases derived from the problem you just solved.

5. Implement the tool as executable code with co-generated tests. Write the function AND a test script in the same pass. The function should be pure where possible (no hidden state), well-typed, and documented with a one-line docstring.

6. Run sandbox validation. Execute the test script. If tests fail, analyze the failure, apply critic feedback (check edge cases, boundary conditions, type mismatches), and iterate. Do not accept a tool that fails any test case.

7. Register the tool in the library with metadata. Store the tool with: name, category, description, usage signature, creation context (what problem spawned it), and initial usage count of 1.

8. Resume the original task using the newly created tool. Invoke the tool to complete the step that triggered its creation. Verify the tool's output matches expectations in context.

9. Consolidate the library periodically. After completing a session or batch of tasks, review the tool library: merge tools with overlapping functionality, flag tools that have never been reused, and deprecate tools that produced errors in subsequent use.

10. Retrieve before creating on subsequent tasks. For every new sub-problem, search the existing library first (by category and description match). Only trigger the Build Loop if no existing tool fits. Log each retrieval to track reuse rates.

Concrete Examples

Example 1: Extracting a geometry utility from a math problem

User: "I need to calculate the area of intersection between two circles given their centers and radii. I'll have many similar problems."

Approach:

1. Solve the intersection area problem step-by-step, documenting the reasoning trace (lens area formula, cases for containment and disjoint circles)
2. Identify the reusable sub-capability: circle_intersection_area(x1, y1, r1, x2, y2, r2) -> float
3. Generate build ticket with test cases from the solved problem
4. Implement and test:

# Tool: circle_intersection_area
# Category: geometry/area
# Created from: circle intersection reasoning trace

import math

def circle_intersection_area(x1: float, y1: float, r1: float,
                              x2: float, y2: float, r2: float) -> float:
    """Compute area of intersection of two circles."""
    d = math.hypot(x2 - x1, y2 - y1)
    if d >= r1 + r2:
        return 0.0  # disjoint
    if d + min(r1, r2) <= max(r1, r2):
        return math.pi * min(r1, r2) ** 2  # one inside the other
    a = (r1**2 - r2**2 + d**2) / (2 * d)
    h = math.sqrt(r1**2 - a**2)
    sector1 = r1**2 * math.acos(a / r1)
    sector2 = r2**2 * math.acos((d - a) / r2)
    triangle = d * h
    return sector1 + sector2 - triangle

# Co-generated tests
assert circle_intersection_area(0, 0, 1, 10, 0, 1) == 0.0  # disjoint
assert abs(circle_intersection_area(0, 0, 5, 1, 0, 2) - math.pi * 4) < 1e-6  # contained
assert circle_intersection_area(0, 0, 1, 1, 0, 1) > 0  # overlapping

5. Tests pass. Register tool. Use it for the user's subsequent circle problems without re-deriving the formula.

Example 2: Building a data parsing tool during CSV analysis

User: "Parse this messy CSV where some rows have quoted fields with commas inside, some have missing columns, and dates are in mixed formats. I'll process 50 similar files."

Approach:

1. Manually reason through parsing the first file, identifying edge cases
2. Extract reusable sub-capabilities: robust_csv_parse(filepath, date_columns) -> DataFrame and normalize_date(date_str) -> datetime
3. Build both tools with tests derived from the actual edge cases encountered

# Tool: normalize_date
# Category: parsing/datetime
# Created from: mixed-format CSV parsing trace

from datetime import datetime

FORMATS = ["%Y-%m-%d", "%m/%d/%Y", "%d-%b-%Y", "%B %d, %Y", "%Y%m%d"]

def normalize_date(date_str: str) -> datetime:
    """Parse a date string in common formats into a datetime object."""
    date_str = date_str.strip()
    for fmt in FORMATS:
        try:
            return datetime.strptime(date_str, fmt)
        except ValueError:
            continue
    raise ValueError(f"Unrecognized date format: {date_str!r}")

# Tests
assert normalize_date("2025-03-15").day == 15
assert normalize_date("03/15/2025").month == 3
assert normalize_date("15-Mar-2025").year == 2025

4. Use the tools to process all 50 files. On file #12, normalize_date fails on a new format ("March 15th, 2025"). Self-update: add the format to the tool, re-run tests, re-register.

Example 3: Agent pipeline that accumulates tools across tasks

User: "I'm building an agent that answers science questions. Start with this physics problem, but design it so the agent gets better over time."

Approach:

1. Solve the physics problem (projectile motion). Reasoning trace includes kinematic equations.
2. Extract tool: projectile_range(v0, angle_deg, g=9.81) -> float
3. Next problem arrives (optics—Snell's law). No existing tool fits. Create: snells_law(n1, theta1, n2) -> float
4. Third problem (projectile on an inclined plane). Retrieve projectile_range from library, find it needs extension. Self-update the tool to accept an incline_deg parameter. Run existing + new tests.
5. Library after 3 tasks: 2 physics tools with 4 total invocations, high reuse signal.

Consolidation check:

Tool Library Status:
- projectile_range   | physics/kinematics | uses: 3 | failures: 0 | status: active
- snells_law         | physics/optics     | uses: 1 | failures: 0 | status: active
- (no merges needed, no deprecations)

Best Practices

Do:

• Always co-generate tests alongside the tool implementation—never register an untested tool
• Keep tools pure and parameterized: no hardcoded values, no hidden dependencies on global state
• Prefer small, composable tools over monolithic ones—a normalize_date + parse_csv_row is better than a single do_everything function
• Track usage metadata (invocation count, failure count) to drive consolidation decisions
• When a tool fails during reuse, fix it in-place and re-run all existing tests (regression check) before re-registering

Avoid:

• Creating tools for one-off calculations that will never recur—not every reasoning step deserves to be a tool
• Skipping the sandbox validation step under time pressure; unverified tools poison downstream reasoning
• Letting the tool library grow unbounded—run consolidation after every 10-15 tools are added
• Creating tools with vague names or missing type annotations; retrievability depends on clear metadata

Error Handling

Limitations

• Single-session context: Without persistent storage, the tool library resets between sessions. To realize the full UCT benefit, the user must maintain the tool files externally (e.g., in a project directory) and re-load them in subsequent sessions.
• Tool quality ceiling: The generated tools are only as correct as the reasoning trace they derive from. If the initial reasoning is flawed, the tool encodes that flaw. The dual-verification gate (tests + critic) mitigates but does not eliminate this risk.
• Not suited for highly creative or ambiguous tasks: UCT works best for well-defined, parameterizable sub-problems (math, parsing, data transforms). Open-ended design tasks or subjective evaluations resist tool extraction.
• Overhead on simple tasks: For trivial one-off calculations, the Build Loop overhead (writing tests, validating, registering) exceeds the benefit. Apply tool creation selectively.
• Library maintenance requires discipline: Without periodic consolidation, the library becomes cluttered with near-duplicate or stale tools, degrading retrieval quality.

Reference

Paper: Evolving from Tool User to Creator via Training-Free Experience Reuse in Multimodal Reasoning — Shen et al., 2026. Look for: Algorithm 1 (Online Task Loop), Algorithm 2 (Build Loop with dual verification), and Table 2 (ablation showing each component's contribution). The memory consolidation formalization in Section 3.3 provides the theoretical grounding for the library maintenance protocol.