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pghdma

CallRail MCP

compare_periods

Compare two N-day windows to detect per-company and agency-wide changes in call volume and minutes, revealing growth, shrinkage, and billing anomalies.

Instructions

Compare current N-day window vs the previous N-day window.

Returns per-company minute / call deltas + agency-wide totals. Useful for "is Malick growing?", "did we lose Stewart traffic this month?", catching invoice surprises before they hit.

Args: days: Window length on each side (default 30 = roughly one cycle). Cap: 365 (don't ask for "5-year delta" — likely a typo). account_id: Auto-resolves if omitted.

Returns: A breakdown showing current vs previous totals, % deltas, and per-company growth/shrink. Sorted by absolute minute change.

Implementation: pulls call data for both windows in one tool call. Tracker counts use current-snapshot for both periods (CallRail doesn't expose historical tracker counts) — only minute deltas reflect actual period-over-period change.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
account_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully discloses behavior: implementation details (single call for both windows), tracker count limitation, and the 365-day cap. This offers rich context for agent decision-making.

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

Conciseness4/5

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

The description is well-structured with sections (purpose, args, returns, implementation) and no wasted sentences. Slightly verbose but justified by the need to compensate for missing annotations.

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

Completeness5/5

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

Given the tool's complexity and no annotations, the description is comprehensive: covers input, output, edge cases (cap), and behavioral caveats. Output schema existence reduces need for return field details.

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?

Schema coverage is 0%, so description compensates by explaining days (with default and cap) and account_id (auto-resolve). It adds meaning beyond the schema, though could detail account_id resolution further.

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 compares two N-day windows, listing specific outputs like per-company minute/call deltas and agency totals. It distinguishes from siblings (no other tool does period comparison) and uses concrete examples.

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

Usage Guidelines4/5

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

Examples like 'is Malick growing?' imply usage scenarios, but no explicit when-not-to-use or alternatives are provided. However, the unique functionality makes the context clear.

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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