Understanding needle.Field Constraints in the Needle Agent Framework

needle.Field constraints are validation rules defined in cactus-compute/needle that enforce type safety, value ranges, pattern matching, and structural requirements on tool arguments before execution.

The cactus-compute/needle repository provides a lightweight agent framework where tools declare their input schemas using Field objects from needle/agent/tools.py. These constraints serve as the contract between agent planners and tool implementations, ensuring that every function call receives properly typed and validated data. By defining constraints directly on Field instances, developers create self-documenting interfaces that fail fast when inputs violate specification.

Core Constraint Types

In needle/agent/tools.py, the Field class accepts multiple parameters that define validation boundaries. Each parameter addresses a specific aspect of data integrity.

Type Declaration and Documentation

• type – Declares the expected Python type (str, int, float, bool, list, dict). The framework validates that incoming values match this type before passing them to the tool function.
• description – Human-readable text explaining the field's purpose. This appears in generated documentation and UI prompts to guide users.

Optionality and Default Values

• required – Boolean flag indicating whether the field must be supplied. When True, the framework rejects any invocation missing this argument.
• default – Value used when required is False and the caller provides no input. This ensures tools always receive a valid value even for optional parameters.

Value Constraints and Enumeration

• enum – A list of allowed literal values. The framework rejects any input not explicitly included in this enumeration.
• pattern – Regular expression string that inputs must match. This applies specifically to string fields requiring format validation (e.g., URLs, identifiers).

Numeric and Length Boundaries

• min and max – Numeric bounds for int or float fields, ensuring values fall within a specified inclusive range.
• min_length and max_length – Length limits for strings or collections, preventing overly short or long inputs.

Complex Data Structures

• items – For list-type fields, this accepts another Field instance defining the type and constraints for each element in the collection. This enables validation of homogeneous lists with specific requirements per item.

Defining Tool Schemas with Field Constraints

Tools in the Needle framework declare their argument schemas as dictionaries mapping parameter names to Field objects. The following example from the codebase demonstrates a fetch tool with multiple constraint types:

from needle.agent.tools import Tool, Field

fetch_tool = Tool(
    name="fetch",
    description="Retrieve the contents of a remote URL.",
    args={
        "url": Field(
            type=str,
            description="The HTTP/HTTPS URL to retrieve.",
            required=True,
            pattern=r"^https?://.*$",
        ),
        "timeout": Field(
            type=int,
            description="Maximum seconds to wait before aborting.",
            required=False,
            default=30,
            min=1,
            max=120,
        ),
        "method": Field(
            type=str,
            description="HTTP method to use.",
            required=False,
            default="GET",
            enum=["GET", "POST", "HEAD"],
        ),
    },
)

In this definition, url must match the HTTPS pattern, timeout defaults to 30 seconds but cannot exceed 120, and method restricts inputs to three specific HTTP verbs.

Runtime Validation Behavior

When an agent invokes a tool, the Needle framework automatically validates incoming arguments against the defined Field constraints. This validation occurs before the tool's execution logic runs, raising informative errors if any constraint is violated.

The following examples demonstrate both successful validation and constraint violations:

# Valid invocation - matches all constraints

fetch_tool.run({"url": "https://example.com", "timeout": 10})

# Invalid invocation - raises validation error due to pattern mismatch

fetch_tool.run({"url": "ftp://example.com"})  # ValidationError: pattern mismatch

# Invalid invocation - exceeds max constraint

fetch_tool.run({"url": "https://example.com", "timeout": 200})  # ValidationError: max exceeded

Handling Nested Constraints with Items

For tools accepting lists, the items constraint enables validation of individual elements. This is implemented by passing a Field instance that defines the schema for each list item:

# Field expecting a list of positive integers

positive_ints = Field(
    type=list,
    description="List of IDs that must be positive.",
    items=Field(type=int, min=1),
)

# Tool using the nested constraint

delete_items_tool = Tool(
    name="delete_items",
    description="Delete a batch of items identified by IDs.",
    args={"ids": positive_ints},
)

According to the source code in needle/agent/tools.py, the validation logic iterates through list elements when items is specified, applying the nested field constraints to each entry individually.

Summary

• needle.Field constraints are defined in needle/agent/tools.py and specify validation rules for tool inputs.
• Constraint types include type, description, required, default, enum, pattern, min/max, min_length/max_length, and items.
• Automatic validation occurs at runtime before tool execution, raising errors for any constraint violations.
• Nested validation is supported via the items parameter, allowing lists to contain validated elements.
• Pattern matching and enumeration provide strict string validation, while numeric bounds and length limits ensure data fits operational requirements.

Frequently Asked Questions

What happens if a required field is missing during tool invocation?

The framework raises a validation error indicating which required parameter is absent. This check occurs in the validation layer of needle/agent/tools.py before the tool's business logic executes, preventing partial or malformed data from reaching the tool function.

Can needle.Field validate nested dictionary structures?

While the raw analysis primarily demonstrates items for list validation, the type=dict constraint validates that an input is a dictionary object. Complex nested validation would typically be handled by defining separate tool schemas or using structured types, though the core Field implementation focuses on per-field constraints rather than deep object schema validation.

How does pattern validation differ from enum constraints in needle.Field?

Pattern constraints use regular expressions to validate that string inputs match a specific format (e.g., URLs, email addresses), allowing infinite valid values that share a structure. Enum constraints restrict inputs to a finite, explicit list of allowed literal values, useful for method names or status codes where only specific options are valid.

Where is the Field class and validation logic implemented?

The Field class and its associated validation routines are implemented in needle/agent/tools.py within the cactus-compute/needle repository. This file defines how constraints are stored on field instances and how the framework validates incoming arguments against those constraints during tool execution.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}