Vaultfire MCP Server

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 ratings are 'stored on-chain from real interactions,' which adds context about data source and authenticity, but lacks details on error handling, rate limits, or response format. For a read operation, this is minimally adequate but leaves gaps in understanding operational behavior.

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

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

The description is highly concise and front-loaded, using a single sentence to convey the core purpose and key metrics. Every word earns its place, with no redundant information. It efficiently communicates the tool's function without unnecessary elaboration, making it easy for an agent to parse quickly.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers what data is retrieved and its on-chain nature, but lacks details on output structure, error cases, or integration with sibling tools. Without annotations or output schema, more context on behavioral aspects would improve completeness for agent use.

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

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

Schema description coverage is 100%, so the input schema fully documents the parameters ('address' and 'chain'). The description adds no additional parameter semantics beyond what the schema provides, such as explaining address validation or chain selection implications. Baseline 3 is appropriate as the schema handles the heavy lifting without description enhancement.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: retrieving reputation data for an AI agent, including specific metrics like average rating, feedback counts, and verification percentages. It distinguishes this from sibling tools like 'vaultfire_get_agent' or 'vaultfire_get_street_cred' by focusing on reputation metrics, though it doesn't explicitly contrast with them. The mention of on-chain storage adds useful context but doesn't fully differentiate from alternatives.

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

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 sibling tools like 'vaultfire_get_agent' or 'vaultfire_get_street_cred', leaving the agent to infer usage based on tool names alone. There's no indication of prerequisites, such as needing an agent address, or when this tool is preferred over others for reputation-related queries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.