Checks what a support agent knew but did not say against its policies. Free tier, no login.
Scored across 1 tool
With only one tool, there is no possibility of confusion between tools. The single tool has a clear and unique purpose.
There is only one tool, so naming is trivially consistent. The name 'disclosure_check' follows a clear verb_noun pattern and is descriptive.
One tool is slightly on the low side, but it's appropriate for the focused audit purpose of checking disclosure gaps. No additional tools are needed for the server's stated function.
The single tool fully covers the server's purpose of checking disclosure gaps in support replies. There are no missing operations within this narrow domain.
Check a draft support reply for disclosure gaps before sending it. Given what the customer said and the reply you are about to send, it flags any should-know policy the customer's situation made relevant that the draft failed to proactively surface (the knew-but-did-not-say gap). Returns a verdict (pass or gap); on a gap, each missed policy and the line that should have been surfaced. It detects and suggests, it does not rewrite.
| Name | Required | Description | Default |
|---|---|---|---|
| draft_reply | Yes | The reply you are about to send. | |
| customer_message | Yes | What the customer said, verbatim. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: it flags missed policies, returns a verdict (pass/gap), detects and suggests but does not rewrite. No contradictions.
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 concise, front-loaded with purpose, and every sentence adds value. No unnecessary words.
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 tool with 2 parameters and no output schema, the description explains the return value (verdict and details) adequately. It is complete.
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 100% and description adds meaning by mapping 'customer_message' and 'draft_reply' to the task. It provides context beyond the schema.
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 'check', the resource 'draft support reply', and the context 'disclosure gaps before sending'. It precisely defines the tool's role.
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 implies usage before sending a reply, but does not explicitly state when not to use or provide alternatives. However, the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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