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download_comses_model

Download and safely extract a COMSES model archive. Resolves version, checks size, enforces byte cap, and prevents path traversal.

Instructions

Download and safely extract a COMSES model archive.

Standalone "fetch but don't open" tool. Most AI flows should use open_comses_model instead — it subsumes this tool and also loads NetLogo models into the workspace.

Safety guarantees:

  • version="latest" is resolved to a concrete version BEFORE any cache path is computed. Cache dirs are named by the resolved version.

  • HEAD request screens oversize archives before streaming.

  • Stream enforces the byte cap mid-download; overruns abort and delete the partial file.

  • Zip members are validated against path traversal before extraction.

  • Uncompressed total is checked against 2 × cap to reject zip bombs.

  • Extract happens in a temp directory; only a successful extract is moved atomically into the cache. A .comses_complete marker is written on success; future calls only trust cached dirs with the marker.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_mbNoSize cap in MB. Pass 0 or omit to use the `COMSES_MAX_DOWNLOAD_MB` env var (default 50 MB).
versionNoVersion string (e.g. "1.2.0") or "latest".latest
identifierYesFull model UUID (from `search_comses`).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It extensively details safety guarantees: version resolution before cache path, HEAD request for size, stream byte cap, zip traversal validation, uncompressed size check, atomic move with marker. This goes well beyond basic transparency.

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

Conciseness4/5

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

The description is well-structured with a clear purpose statement, usage guidance, and bulleted safety guarantees. It is front-loaded but somewhat lengthy; however, the detail is justified by the tool's complexity. A slight reduction would not harm clarity.

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 the presence of an output schema (not needing return value explanation), the description covers all necessary aspects: purpose, usage guidance, parameter context implicitly, and extensive behavioral transparency. It is complete for the tool's complexity level.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. The description adds no additional semantics beyond what the schema provides for the three parameters (identifier, max_mb, version). The safety details relate to behavior, not parameter meaning.

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 clearly states 'Download and safely extract a COMSES model archive' and explicitly distinguishes itself from sibling 'open_comses_model' by noting it is a standalone 'fetch but don't open' tool. This provides a specific verb+resource and differentiates from alternatives.

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 guides the agent: 'Most AI flows should use open_comses_model instead — it subsumes this tool and also loads NetLogo models into the workspace.' This provides clear when-to-use and alternative guidance.

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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