Search the repository AI-readable knowledge index for relevant Vibe Coding documentation. Use this before read_doc when the exact document path is unknown.
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": "search_docs"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| input | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that it searches an AI-readable knowledge index, which is useful framing, but says nothing about result format, ranking, result limits, or whether queries are keyword or semantic. For a no-annotation search tool this is adequate but not rich.
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, front-loaded with the core purpose followed by the routing rule. Zero filler.
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 single-param search tool with no output schema, the description is missing what an agent needs to use results correctly: whether output is ranked snippets or paths, and how to frame the query. It covers the 'when' well but not the 'what comes back'.
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?
There is one parameter ('input') with 0% schema description coverage, and the description never explains what that input should be (a query string? keywords?). With low coverage the description must compensate, and it does not.
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 specific verb (Search) and resource (repository AI-readable knowledge index for Vibe Coding documentation), and explicitly distinguishes itself from the sibling read_doc. An agent can tell immediately what this does and how it differs from reading a known path.
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?
Gives explicit when-to-use guidance: 'Use this before read_doc when the exact document path is unknown.' This names the alternative tool and the condition that selects it, leaving nothing to inference.
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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