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LuciferForge

agent-safety-mcp

by LuciferForge

trace_save

Save AI agent interaction traces to disk for audit trails, creating JSON and Markdown files to document safety monitoring activities.

Instructions

Save the current trace to disk as JSON and Markdown files.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Implementation of the trace_save tool which saves the current tracer trace to JSON and Markdown files and returns a summary.
    @mcp.tool()
    def trace_save() -> dict:
        """Save the current trace to disk as JSON and Markdown files."""
        tracer = _get_tracer()
        json_path = tracer.save()
        md_path = tracer.save_markdown()
        return {
            "saved": True,
            "json": str(json_path),
            "markdown": str(md_path),
            "summary": tracer.summary(),
        }
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses the output formats (JSON and Markdown) and disk write behavior, but omits safety-critical details like file overwrite behavior, required disk permissions, or what the operation returns upon completion.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with zero waste. Every word contributes essential information: the action, target, destination, and file formats.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (no parameters) and lack of output schema, the description adequately covers the core functionality. It could be improved by indicating where files are saved or what success feedback looks like, but it is sufficient for basic invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema contains zero parameters. Per the baseline rules for zero-parameter tools, this earns a default score of 4. The description correctly implies no configuration is needed by omitting any parameter references.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description provides a specific verb (Save), resource (trace), destination (disk), and output formats (JSON and Markdown). It clearly distinguishes from siblings like trace_start, trace_step, and trace_summary by emphasizing the persistence aspect.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context (saving a trace that presumably exists), but lacks explicit guidance on when to use this versus trace_summary or prerequisites such as having an active trace in progress.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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