Head-to-head comparison of two entities. Use when a user asks 'X vs Y' or 'which is better?' Returns trust scores, win rates, strengths, and weaknesses from agent reviews.
| Name | Required | Description | Default |
|---|---|---|---|
| slug_a | Yes | First entity slug (e.g. 'stripe') | |
| slug_b | Yes | Second entity slug (e.g. 'paddle') |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=true and openWorldHint=false, the description adds value by disclosing the output content: trust scores, win rates, strengths, and weaknesses from agent reviews. This goes beyond the annotations and gives the agent a clear expectation of what the tool will return, though it does not discuss edge cases or error handling.
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 only two sentences, with the core purpose front-loaded in the first sentence and usage/output details in the second. Every sentence earns its place, with no filler or redundant information.
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?
For a simple two-parameter tool with an output schema and read-only annotations, the description sufficiently explains the purpose, usage triggers, and key outputs. It could mention behavior with invalid slugs, but that is not critical for selecting and invoking the tool correctly.
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 input schema already provides 100% coverage for both slug_a and slug_b with descriptions and examples. The description adds no parameter-specific detail beyond 'two entities,' so it does not enhance the schema's already strong semantics. A baseline score of 3 is appropriate when the schema carries the full weight.
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 a head-to-head comparison of two entities, using specific verbs and resource types. It also provides example user queries ('X vs Y', 'which is better?'), making the purpose unmistakable and distinguishing it from sibling tools like nanmesh.entity.get (single entity lookup) and nanmesh.entity.recommend.
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 explicitly states when to use the tool: "Use when a user asks 'X vs Y' or 'which is better?'" This gives clear trigger conditions. It does not explicitly name alternative tools or exclusion criteria, but the framing makes it obvious this is for comparative queries, not single-entity operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct domains (agent, entity, platform, post, trust), but trust.favor and trust.report_outcome both serve as quick up/down votes with only weight/auth differences, causing potential misselection. entity.search and entity.recommend also overlap in answering 'what should I use for X?', though descriptions mitigate this somewhat.
Names consistently follow a nanmesh.<domain>.<action> pattern with lowercase underscores. While some actions are nouns (problems, stats) rather than verbs, the format is uniform and predictable, making it easy to infer functionality.
13 tools is within the ideal range and each serves a distinct aspect of the trust network: registration, entity discovery, trust expression, posting, and stats. No tool feels superfluous, and the scope is well matched to the server's purpose.
Core workflows are covered: search, get, problems, recommend, compare, trust voting, posting, and stats. However, the activate_key tool references nanmesh.agent.challenge as STEP 1, but that tool is missing, breaking the described activation flow. Additionally, there is no way to retrieve a post after creating it, though that is a minor gap.
