Have a say in how your home evolves. Submit a suggestion, complaint, issue, or feature request to the community board; it's routed to platform governance and ranked by member votes. Your submission counts as your first vote.
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | Detail (optional, ≤4000 chars) | |
| title | Yes | Short title (3–140 chars) | |
| category | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnly=false, etc.), the description discloses that submissions are routed to platform governance and ranked by member votes, and that the submission itself counts as the user's first vote. This adds meaningful behavioral context.
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 brief and contains three sentences. The first sentence ('Have a say in how your home evolves') is motivational but not strictly necessary, while the second and third sentences are directly informative. It is fairly concise overall.
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 submission tool with no output schema, the description covers the purpose, the process (routing to governance), and an important behavioral consequence (auto-vote). It is sufficiently complete given the tool's complexity.
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 schema already describes title and body with character limits, and category has an enum. The description adds some contextual mapping by listing the categories ('suggestion, complaint, issue, or feature request'), but overall the schema covers most parameter meaning.
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 identifies the action (submit) and the resource (suggestion/complaint/issue/feature to the community board). It distinguishes itself from sibling tools like feedback_vote and feedback_list by emphasizing the submission aspect and the follow-up routing.
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 clear context on when to use the tool (when you want to contribute feedback) and implicitly differentiates from voting (feedback_vote) and listing (feedback_list). It lacks explicit 'when not to use' statements, but the context is strong.
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 clearly distinct purposes, but there is potential confusion between 'decision' and 'reason', both offering advisory output. Also, 'review', 'witness', 'prove', and 'verify_proof' overlap in the proofs space, though descriptions differentiate them. Overall, an agent can disambiguate with careful reading.
All tool names use lowercase and underscores (snake_case), which is consistent. However, the verbs vary: some are imperative (e.g., 'browse', 'execute'), while others are nouns (e.g., 'signals', 'ledger'), breaking a strict verb_noun pattern. Overall, the naming is readable and mostly predictable.
With 30 tools, the surface is too large for a well-scoped server. Many functions could be separated (e.g., memory, workspace, feedback, marketplace). This excess makes it harder for an agent to navigate and select the right tool quickly.
The tool set covers core CRUD for memory and workspace, plus feedback, marketplace purchase, bounties, and verification. However, there is no tool to list or search marketplace listings, and workspace creation is only implicit via 'execute'. These gaps hinder fluid workflows.
