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fork_thesis

Clone public prediction market theses to your account to iterate on existing causal tree analysis. Requires SF API key. Use when building on others' research instead of starting from scratch.

Instructions

Clone a public thesis (by id or slug) into the authenticated user's account, copying its causal tree as a starting point. Side-effectful. Requires SF API key. Use when you want to iterate on someone else's thesis; use create_thesis to start from scratch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
apiKeyYesSF API key (sf_live_...). Required.
idOrSlugYesSource thesis ID (uuid) or public slug. Required.
Behavior4/5

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

No annotations provided, so description carries full burden. Explicitly discloses mutation ('Side-effectful'), authentication requirement ('Requires SF API key'), and data scope ('copying its causal tree as a starting point'). Minor gap: does not describe return value format or error cases when thesis doesn't exist.

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?

Three tight sentences with zero waste: sentence 1 defines action and mechanism, sentence 2 declares behavioral traits and auth, sentence 3 provides usage guidelines. Information is front-loaded with purpose first, followed by constraints and alternatives.

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 2 parameters with full schema coverage and no output schema, description adequately covers operation semantics, side effects, auth, and sibling differentiation. Minor deduction for not describing return structure or failure modes, though absence of output schema reduces obligation to detail return values.

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?

Schema has 100% coverage, establishing baseline 3. Description adds value by explicitly contextualizing parameters within the operation: 'by id or slug' maps to idOrSlug usage, and 'Requires SF API key' emphasizes the apiKey constraint. Provides semantic bridge between parameter names and their functional role.

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?

Description uses specific verb 'Clone' with resource 'public thesis' and scope 'into the authenticated user's account'. Explicitly distinguishes from sibling tool by contrasting with 'create_thesis to start from scratch', clarifying this is for iteration on existing work versus creation.

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?

Explicitly states when to use ('when you want to iterate on someone else's thesis') and names the specific alternative ('use create_thesis to start from scratch'). Provides clear decision criteria for tool selection between the two creation pathways.

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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