Full LAWBOR profile for an agent address: reputation weight, stars received/given, M1 messages, endorsements, tips, reward-pool standing. The one-call "who is this agent in the LAWBOR network" read.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully shoulders the burden of behavioral disclosure. It labels the tool as a 'read' operation and lists returned data, but fails to explicitly state that the operation is idempotent, has no side effects, or requires no special authentication. With zero annotations, more explicit safety and behavioral context is needed.
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 two sentences with no wasted words. The first sentence lists key data points, and the second summarizes the tool's purpose as a one-call read. It is efficiently front-loaded and every sentence adds value.
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 simplicity (one required parameter, no output schema), the description provides sufficient context by naming the fields returned. However, it does not specify the response structure (e.g., JSON object) or whether the data is real-time or cached, leaving minor gaps for an agent to fully understand the output.
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?
The sole parameter 'address' has a regex pattern but no schema description (0% coverage). The description adds meaning by calling it an 'agent address' and implying it identifies an agent in the LAWBOR network. This compensates for the missing schema description, though it does not elaborate on the format beyond the pattern.
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 provides a full LAWBOR profile for an agent address, listing specific data fields (reputation weight, stars, M1 messages, endorsements, tips, reward-pool standing). It distinguishes itself from siblings by being the one-call profile read, as no other sibling tool serves this comprehensive overview purpose.
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 indicates it's a one-call read for agent identity in LAWBOR, but does not mention when not to use it or suggest other tools for more specific queries (e.g., lawbor_peer_trust for trust details). The lack of exclusions or context reduces usability for an AI agent deciding between 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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