Propose a stat-doc TABLE → Inbox. csv (header; key% = percent; first column = name) OR schema+rows. Adopting REPLACES same-named.
| Name | Required | Description | Default |
|---|---|---|---|
| csv | No | Header + rows; non-numeric = info | |
| rows | No | [{name, atk: 350, …}] | |
| title | Yes | Table name | |
| schema | No | [{key, mode: flat|pct|text, base, min, max, desc}] | |
| registry | No | true = the shared Stats registry | |
| project_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral traits: proposals go to Inbox, and adopting REPLACES same-named tables. Annotations only include destructiveHint:false, so this adds meaningful context beyond the structured data.
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 compact sentences, front-loaded with the main purpose. Every clause adds value: table type, inbox destination, input options, and replacement behavior.
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 proposal tool with 6 params and no output schema, the description covers the core purpose, input formats, and adoption consequence. It omits project_id and registry details, but those are likely standard context parameters shared across sibling tools.
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 coverage is high (83%), but the description adds important format details: csv header requirements, percent notation, first column as name, and the schema+rows combination. This goes beyond the schema descriptions.
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 verb 'Propose', the resource 'stat-doc TABLE', and the target 'Inbox'. It distinguishes from sibling tools like propose_balance_board by specifying TABLE, making it 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?
Usage is implied by the resource type (table vs. board) but no explicit alternatives or exclusions are mentioned. The description does not say when to use this tool instead of propose_balance_board or other propose_* tools.
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.
Each tool has a clearly distinct purpose, with explicit distinctions between direct actions and proposals via Inbox. The verbs and object types (system, milestone, screen, element, balance) are unique enough that no two tools appear to do the same thing.
Most tool names follow a consistent verb_noun snake_case pattern (get_system, propose_screen, update_element). Minor deviations like 'dedupe', 'search', 'next_task', and 'reorder' are single words or non-verb but remain readable and stylistically compatible.
With 54 tools, this server vastly exceeds the typical MCP scope, hitting the 'extreme mismatch' threshold. Even for a complex domain, the sheer number will overwhelm agents and degrade selection performance.
The tool surface is remarkably complete, covering full lifecycle operations for all major entities, plus import, design generation, drift detection, status reporting, inbox handling, and rejection workflows. No obvious dead ends or missing operations for the stated purpose.