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link_decision

Connect decisions to codebase elements like services, files, or contracts using typed relationships such as motivates, constrains, documents, or implements.

Instructions

Link an existing decision to a service, file, symbol, contract, or domain with a typed relationship (motivates, constrains, documents, implements).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
decision_idYesID of the decision to link from
entity_typeYesType of entity being linked
entity_idYesID of the entity
link_kindYes"motivates" = decision prompted this entity to exist, "constrains" = decision limits how this entity may evolve, "documents" = decision explains this entity, "implements" = entity is the concrete realization of the decision
Behavior2/5

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

With no annotations provided, the description carries full burden but lacks behavioral details. It states the action ('Link') but doesn't disclose permissions needed, whether the link is reversible, error conditions, or side effects. This is inadequate for a mutation tool with zero 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, efficient sentence that front-loads the core action and key details. Every word earns its place with no redundancy or unnecessary elaboration, making it easy to parse quickly.

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

Completeness2/5

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

For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after linking (e.g., confirmation message, error responses), or address potential conflicts or dependencies, leaving significant gaps for an AI agent.

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 parameters are well-documented in the schema. The description adds minimal value by listing entity types and link kinds, but doesn't provide additional context like examples or edge cases beyond what the schema already specifies.

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 specific action ('Link an existing decision') with the target resources ('service, file, symbol, contract, or domain') and relationship types. It distinguishes this tool from siblings like 'record_decision' (which creates decisions) and 'get_decisions_for' (which retrieves decisions), establishing a unique purpose.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., requiring an existing decision ID), exclusions, or comparisons to sibling tools like 'bind_contract' or 'set_service_mapping', leaving usage context unclear.

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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