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seandkendall

productivity-mcp

by seandkendall

ping

Probe configured accounts to return connectivity status and latency, allowing the LLM to self-check account health.

Instructions

Probe every configured account (or just the named one). Returns per- account {name, kind, ok, latency_ms, error?} so the LLM can self-check.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accountNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main 'ping' tool handler — decorated with @mcp.tool() and @_logged. Probes every configured email/calendar account (or a specific one) and returns per-account status including latency.
    @mcp.tool()
    @_logged
    def ping(account: str | None = None) -> list[dict[str, Any]]:
        """Probe every configured account (or just the named one). Returns per-
        account {name, kind, ok, latency_ms, error?} so the LLM can self-check."""
        import time as _t
    
        results: list[dict[str, Any]] = []
        providers: list[tuple[str, Any]] = []
        if account is None or account in _email_providers:
            for n, p in _email_providers.items():
                if account is None or n == account:
                    providers.append((f"email:{n}", p))
        if account is None or account in _calendar_providers:
            for n, p in _calendar_providers.items():
                if account is None or n == account:
                    providers.append((f"calendar:{n}", p))
        for label, p in providers:
            t0 = _t.time()
            entry: dict[str, Any] = {"name": label, "kind": p.__class__.__name__, "ok": True}
            try:
                p.ping()
            except Exception as exc:
                entry["ok"] = False
                entry["error"] = f"{type(exc).__name__}: {exc}"
            entry["latency_ms"] = int((_t.time() - t0) * 1000)
            results.append(entry)
        return results
  • Registration of the 'ping' tool via the @mcp.tool() decorator on line 771, which registers it with the FastMCP server instance.
    @mcp.tool()
  • Default ping implementation on EmailProvider base class — delegates to self.list_folders() as a health check.
    def ping(self) -> None:
        self.list_folders()
  • Default ping implementation on CalendarProvider base class — delegates to self.list_calendars() as a health check.
    def ping(self) -> None:
        self.list_calendars()
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses the return structure (name, kind, ok, latency_ms, error?) and the ability to probe a single or all accounts. However, it does not explicitly state that the operation is safe/read-only or mention any potential side effects.

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 front-loads the action and resource, immediately conveying the tool's purpose. Every part adds value, with no wasted words.

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

Completeness4/5

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

Given the tool has only one optional parameter and an output schema exists (though not detailed in description), the description covers the core functionality and return format well. It could mention the output schema more explicitly, but the provided return structure is sufficient for an LLM to understand the result.

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

Parameters2/5

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

Schema coverage is 0%, but the description mentions 'or just the named one' hinting at the account parameter. It does not explain the type, default (null), or behavior when omitted. The parameter is optional, but the description lacks detail beyond a vague reference.

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 probes configured accounts (or a named one) and returns per-account diagnostic data (ok, latency_ms) for LLM self-check. The verb 'Probe' and resource 'every configured account' are specific and distinguish it from sibling tools that perform email/calendar operations.

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

Usage Guidelines3/5

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

The description implies usage for connectivity checks via 'so the LLM can self-check', but does not explicitly state when to use vs. alternatives or provide exclusion criteria. The context of sibling tools suggests it's for diagnostics, but no direct guidance.

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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