mcp_eval
MCP (Model Context Protocol) evaluation and scoring. Price: $2.00/call (x402, USDC on Base).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The task, question, or input for this AI service. |
MCP (Model Context Protocol) evaluation and scoring. Price: $2.00/call (x402, USDC on Base).
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The task, question, or input for this AI service. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden, and it does disclose a real constraining trait: a $2.00/call charge via x402/USDC on Base, which is genuine operational context. However, it says nothing about what the evaluation returns, latency, failure modes, or whether payment failure blocks the call.
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?
Two tight sentences that are front-loaded with the function, then the cost. Nothing is padded, though the cost sentence does more work for billing than for tool selection.
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 no annotations and no output schema, the description should explain what is evaluated and roughly what comes back; instead it leaves the entire return contract and evaluation domain unspecified. The pricing disclosure is the only substantive content beyond the name.
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% on the single 'prompt' parameter, so the baseline is 3. The description adds no format, length, or content guidance beyond what the schema already provides.
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?
States a verb-plus-resource phrase ('MCP evaluation and scoring'), but 'MCP' is never disambiguated – it could mean scoring MCP servers, tool definitions, or something else entirely, and the single generic 'prompt' parameter offers no clue. It is distinguishable from siblings by name only, not by described purpose.
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?
No when-to-use, when-not-to-use, or alternative guidance appears; the second sentence is purely commercial. An agent gets no signal on when this tool is the right choice over the twenty-two sibling AI services.
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.