CallRail MCP

by pghdma

Overview Schema Source Related Servers Discussions Admin

Python

Remote

call_stats

Server-side call aggregation via CallRail's /calls/summary.json.

Instructions

Server-side call aggregation via CallRail's /calls/summary.json.

One request instead of paginating every call. Prefer this over call_summary (which fetches and counts calls client-side) when you only need grouped totals. call_summary remains useful for metrics this endpoint doesn't expose (first-time vs repeat split, per-source-name breakdown, exact duration sums).

Args: group_by: Dimension to group by. One of: 'source', 'keywords', 'campaign', 'referrer', 'landing_page', 'company'. days: Lookback (default 30). Ignored if start_date given. start_date / end_date: 'YYYY-MM-DD'. Explicit dates win. company_id: Filter to one company. fields: Comma-separated metrics, e.g. 'total_calls,missed_calls,answered_calls,first_time_callers, average_duration,leads'. Default: total_calls only. account_id: Auto-resolves if omitted.

Returns: JSON string with start_date, end_date, time_zone, total_results and grouped_results[] ({key, }).

Input Schema

TableJSON Schema

Name	Required	Default
`days`	No
`fields`	No
`end_date`	No
`group_by`	No	source
`account_id`	No
`company_id`	No
`start_date`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.0.4Jul 6, 2026

TDQS

A5/5.0

Behavior5/5

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

With no annotations, the description fully carries the behavioral burden. It explains the server-side aggregation model, one-request behavior, default lookback, date precedence, field defaults, account auto-resolution, and the exact JSON return shape. This is unusually transparent for a read-style aggregation endpoint.

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 dense but well-organized with clear Args and Returns sections. Every parameter has a purpose, and no filler sentences exist. Given seven parameters and the need to explain alternative selection, the length is justified.

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?

The tool is fully documented for safe, correct invocation: parameter semantics, default behavior, sibling-tool differentiation, and return structure are all present. The only omitted details are operational concerns like auth or rate limits, which are not required for an agent to select and call this tool correctly.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must fully document parameters. It does: group_by enumerates valid values, days and fields have defaults, start_date/end_date get format and precedence rules, company_id is explained, and account_id's auto-resolution is noted. This compensates completely for the empty schema descriptions.

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 this tool performs server-side call aggregation via CallRail's /calls/summary.json endpoint and returns grouped totals in a single request. It also distinguishes itself from call_summary, making its purpose and scope unmistakable.

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

Usage Guidelines5/5

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

Explicitly tells the agent when to prefer this tool over call_summary ('when you only need grouped totals') and when call_summary is still useful. This gives actionable routing guidance beyond a generic tool description.

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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