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detect_format

Detect which supported agent-file format the content is (verdigraph_genome, claude_project_export, openai_assistant, prompt_list) before building.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It only states 'detect' without mentioning whether the tool is read-only, modifies state, or has side effects. This is inadequate for a tool with no annotation coverage.

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 sentence that front-loads the action and lists examples. It is concise with no superfluous words.

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

Completeness3/5

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

The description covers the core purpose and usage timing, but lacks additional context such as error handling, prerequisites (e.g., content must be from an agent file), or behavior guarantees (e.g., no mutations). The presence of an output schema partially compensates for missing return value details.

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

Parameters2/5

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

With 0% schema description coverage, the description should add meaning to the 'content' parameter. It only uses the parameter name without specifying expected format, structure, or examples, providing minimal value beyond the schema.

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 the verb 'detect', the resource 'content format', and lists four specific formats. It distinguishes from sibling tools (build_brain, describe_agent, verify_brain) by focusing on detection rather than construction, description, or verification.

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

Usage Guidelines4/5

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

The phrase 'before building' provides clear context that this tool is a preparatory step. However, it does not explicitly specify when not to use it or describe alternatives, though the usage context is strong enough for typical use.

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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TDQS

A4/5.0
Disambiguation5/5

Each tool has a distinct purpose: building a brain, detecting format, describing agent capabilities, and verifying claims. No overlapping functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (build_brain, describe_agent, detect_format, verify_brain).

Tool Count5/5

Four tools is well-scoped for the domain of agent file compilation and verification. Each tool earns its place with a distinct action.

Completeness5/5

The tool surface covers detection, compilation, description, and verification of agent files, forming a complete lifecycle for the domain.