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simulation_report_tool

Generate a comprehensive HTML report summarizing all traffic simulation scenarios with KPIs, comparisons, and congestion analysis from a SQLite database.

Instructions

Generate a comprehensive HTML simulation analysis report from SQLite database.

This is the FINAL deliverable of a simulation analysis session.
It summarizes ALL scenarios in the database with KPIs, comparisons, congestion analysis, and emissions.

The report is a standalone dark-themed HTML file that can be:
- Viewed in the web interface (click from file tree)
- Opened in any browser
- Shared as a file

IMPORTANT — Before calling this tool:
1. Query the database (read_query) to understand the simulation results
2. Write an executive_summary (1-2 paragraphs, English, professional tone) that covers:
   - Context: what area was studied and what problem was investigated
   - Key findings: most significant results from scenario comparison
   - Risks/concerns: any metrics that worsened or areas of concern
   - Recommendation: what action should urban stakeholders take based on the analysis
   Focus on insights useful for urban decision-makers (policymakers, planners, city officials).
   Do NOT list raw numbers — interpret them.

Args:
    db_path: Path to SQLite database file
    executive_summary: LLM-generated executive summary for urban stakeholders (English, 1-2 paragraphs)
    output_dir: Output directory for report files

Returns:
    Dict with report_file path and metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
db_pathYes
output_dirNooutput/reports
executive_summaryNo
Behavior5/5

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

With no annotations, the description fully explains the tool's behavior: it generates a standalone dark-themed HTML file with KPIs, comparisons, congestion analysis, and emissions. It also describes output usage (view in web interface, browser, share). No contradictions.

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 well-structured with a title, overview, bullet-point prerequisites, and args/returns section. It is concise yet comprehensive, with no redundant information. The most important information is front-loaded.

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

Completeness5/5

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

Given no output schema, the description explains the return type (dict with file path and metadata) and the report's nature (HTML, dark-themed). It covers all aspects: input requirements, output, and file characteristics. The tool is a simple reporting tool, and the description is complete.

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

Parameters5/5

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

Despite 0% schema coverage, the description includes an 'Args' section with clear explanations for each parameter: db_path (path to database), executive_summary (LLM-generated summary with specific content), output_dir (output directory with default). This adds significant meaning beyond schema names.

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 states it generates a comprehensive HTML simulation analysis report from SQLite database, which is a specific verb+resource. It distinguishes itself from sibling tools like visualize_* and analysis tools by being the final deliverable summarizing all scenarios.

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

Usage Guidelines5/5

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

The description explicitly provides an 'IMPORTANT' section detailing prerequisites: query the database and write an executive summary before calling the tool. This clearly guides when and how to use the tool, making it the final step in analysis.

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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