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Voxos-ai-Inc

@voxos-ai/clink-mcp-server

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by Voxos-ai-Inc

finalize_proposal

Close proposal voting and calculate the final outcome using the configured threshold type (majority, two-thirds, unanimous, or quorum).

Instructions

Close voting on a proposal and compute the final result. The result depends on the threshold type (majority, two-thirds, unanimous, quorum).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
proposal_idYesThe proposal ID
total_eligible_votersNoOptional: total eligible voters for quorum calculation. If not provided, quorum is calculated based on votes cast.
Behavior2/5

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 mentions that the result depends on threshold types, but it does not state whether closing is irreversible, what happens to the proposal's status, or whether further votes are rejected. This is a significant gap for a consequential finalizing action.

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 two short sentences that immediately state the action and outcome. Every word earns its place, with no redundancy or fluff.

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?

For a tool with two parameters, no output schema, and no annotations, the description covers the core purpose and threshold dependency. However, it omits important contextual details such as side effects (e.g., proposal becoming immutable, votes no longer accepted) and whether the final result is returned. This is a moderate gap.

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 the schema fully documents both parameters. The description adds no parameter-specific detail beyond what is already in the schema, but it does not need to compensate; the baseline of 3 is appropriate.

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 uses a specific verb ('Close voting') and resource ('a proposal'), clearly distinguishing it from siblings like cast_vote and create_proposal. It also states the outcome ('compute the final result'), which fully conveys the tool's purpose.

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 description clearly implies when to use this tool: when voting on a proposal needs to be closed and a final result computed. It doesn't explicitly mention exclusions or alternatives, but the context is clear enough given the sibling tools.

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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