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get_stats

Retrieve summary statistics from Pi-hole including total queries, blocked queries, percentage blocked, unique domains, and clients.

Instructions

Get summary statistics (total queries, blocked queries, blocking percentage, unique domains, clients).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The `get_stats` tool handler: an async function registered via @mcp.tool() that fetches summary statistics from the Pi-hole API at /stats/summary.
    async def get_stats() -> dict:
        """Get summary statistics (total queries, blocked queries, blocking percentage, unique domains, clients)."""
        return await client.get("/stats/summary")
  • The `register` function in stats.py uses @mcp.tool() decorator to register `get_stats` as an MCP tool. This module is invoked by tools/__init__.py's register_all function.
    def register(mcp: FastMCP, client: PiholeClient) -> int:
        @mcp.tool()
        async def get_stats() -> dict:
            """Get summary statistics (total queries, blocked queries, blocking percentage, unique domains, clients)."""
            return await client.get("/stats/summary")
  • The `register_all` function iterates over tool modules (including stats) and calls their `register` method to wire up all tools on the FastMCP instance.
    def register_all(mcp: FastMCP, client: PiholeClient) -> int:
        """Register every tool module against the FastMCP instance. Returns tool count."""
        count = 0
        for module in (stats, queries, blocking, domains, local_dns, maintenance):
            count += module.register(mcp, client)
  • The `get` method on PiholeClient, which `get_stats` calls via `client.get('/stats/summary')`, delegates to the `request` method with method='GET'.
    async def get(self, path: str, *, params: dict[str, Any] | None = None) -> Any:
        return await self.request("GET", path, params=params)
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description must carry the full burden. It only lists output fields and does not indicate whether the operation is read-only, cached, or expensive. This lack of behavioral context is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that efficiently communicates the purpose and key outputs. No unnecessary words or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the description lists several output fields, it lacks details about performance, whether data is real-time, or any edge cases. An output schema is present but not described. The completeness is adequate but not thorough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are no parameters, and schema coverage is 100%. The description logically implies no input is needed, so the description adds appropriate value for a parameterless tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets summary statistics and enumerates the specific metrics (total queries, blocked queries, blocking percentage, unique domains, clients). It is concise and distinguishes itself from siblings by being an aggregate stats tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives (e.g., get_query_log, get_top_blocked). There is no mention of prerequisites or context, making it less helpful for an AI agent to decide.

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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