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callsense-mcp

callsense-mcp

by dsichz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

team_report

Read-onlyIdempotent

Generates an evidence-based team report on scored sales calls with rule rates, rep tables, at-risk and follow-up calls; filter by rep and since date.

Instructions

Team view over scored calls: rate per rule, a table per rep, calls at risk with the quotes behind them, calls to follow up, and unscored calls. Every count lists the call ids it counts. Filter by rep and by date with since=YYYY-MM-DD.

Tool call template

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": "team_report"
  }
}

Input Schema

NameRequiredDescriptionDefault
repNo
sinceNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare the full safety profile (readOnly, idempotent, non-destructive), so the description is free to add behavioral context instead. It does: every count lists the call ids it counts, and the report is scoped to scored calls plus unscored-call visibility. This is meaningful disclosure beyond annotations, though pagination/size behavior is unaddressed.

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?

Content summary is front-loaded in the first sentence, with the filter detail last. Two sentences, no filler, dense but readable. Slightly list-heavy, but every clause describes actual report content.

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?

With an output schema present, return structure needn't be spelled out, annotations cover safety, and a 2-param read-only report is low-complexity. The only real gap is the semantics of the 'rep' parameter, so completeness is high but not perfect.

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

Parameters3/5

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

Schema description coverage is 0%, so the description carries the burden. It compensates for 'since' by giving the format (since=YYYY-MM-DD) and states both params act as filters, but it says nothing about what 'rep' accepts (name, email, id), leaving one of two params underspecified.

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

Purpose4/5

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

States a specific resource (a team-level report over scored calls) and enumerates its exact contents: rate per rule, per-rep table, at-risk calls with quotes, follow-ups, unscored calls. This distinguishes it well from list_calls, get_call, and score_call. It stops short of naming a sibling directly, so it lands at 4 rather than 5.

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?

Usage is implied by the enumerated contents (an agent can infer this is the aggregate 'team overview' tool rather than a single-call or write tool). However, no explicit when-to-use/when-not guidance or named alternatives are given, and the sibling tools are never referenced.

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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