Read evidence levels and validation standards: proposed, accepted provisional, contested, validated within scope, rejected. Validation is never automatic.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "validation_standards"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes this is a safe read, so the description only needs to add context. It contributes one useful behavioral trait — that validation status is never assigned automatically — but says nothing about whether the output is a static reference list or filtered by workspace, or about ordering and size.
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 short sentences, front-loaded with the verb and resource, with the level names listed compactly. The trailing policy sentence is short and earns its place as the one non-obvious fact; nothing is padded.
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 zero-parameter read tool with no output schema, the description covers the domain of the response (the enumerated levels) but not its shape — whether each level comes with definitions, criteria, or counts. That is a real gap given the absence of an output schema, though the list of level names gives the agent a decent mental model.
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 tool takes no parameters (schema coverage 100%, empty properties object), which is the baseline-4 case. The description correctly implies a parameterless lookup of the full standards set rather than a filtered query, consistent with the empty 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 gives a specific verb and resource ('Read evidence levels and validation standards') and enumerates the exact level names (proposed, accepted provisional, contested, validated within scope, rejected), so an agent knows what taxonomy it retrieves. It does not distinguish itself from the sibling 'validated_knowledge', whose name suggests overlapping territory, so it stops short of a 5.
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?
There is no statement of when to call this versus 'validated_knowledge' or 'knowledge_catalog', nor any precondition such as calling it before proposing or reviewing knowledge. 'Validation is never automatic' reads as a policy fact about the domain rather than guidance on invocation, leaving the agent to guess the trigger.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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