Get trust-ranked recommendations for a use case or category. Use when a user asks 'what should I use for X?' Ranking: trust reviews (70%) + recency (15%) + momentum (10%) + views (5%).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of recommendations (1-20) | |
| query | No | Natural language description of what you need | |
| category | No | Filter by category slug |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description reveals the ranking algorithm (trust reviews 70%, recency 15%, momentum 10%, views 5%), which is useful behavioral context. It does not contradict the annotations and adds insight into how results are ordered.
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 compact: three sentences cover purpose, usage, and ranking logic without redundancy. Important information is front-loaded and every sentence earns its place.
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?
With an output schema present, return values are covered. The description covers purpose, a usage trigger, and ranking behavior, leaving little missing. It does not explicitly mention optionality of all parameters, but the schema covers that, so the description is complete enough for effective tool selection.
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 each parameter is already documented. The description adds minimal semantic value by mapping 'use case' to the query parameter and 'category' to the category parameter, but this is largely implicit in the schema descriptions themselves. Baseline of 3 is appropriate.
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 'Get trust-ranked recommendations for a use case or category', which is a specific verb+resource. It also differs from sibling tools like search or get by emphasizing recommendations and includes a ranking formula, making its purpose unambiguous.
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 line 'Use when a user asks "what should I use for X?"' provides an explicit trigger condition for when to invoke the tool. It does not, however, mention when not to use it or point to alternatives like nanmesh.entity.search, so it lacks full when-not guidance.
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.
