LeadAce

get_eval_data

Retrieve project evaluation stats: response rates, channel performance, sentiment breakdown, and targeting insights to assess outbound campaign effectiveness.

Instructions

Evaluation statistics for a project: response rates, channel performance, sentiment breakdown, discoveryStrategyResponseRate (per discovery strategy; reply metrics count mature sends only while its bounce metrics span all sends — the early source-quality read; the null bucket is prospects without recorded provenance), targeting observation axes (industryResponseRate by coarse bucket / sizeResponseRate by employee band / countryResponseRate — these also count mature sends only, older than the reply-maturity window), freshSignalResponseRate, inquiry-landing outcome counts, respondedMessages, and a data-sufficiency check. Reply rates exclude bounces/auto-replies; per-bucket bounces + bounceRate are a threaded-only lower bound.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "projectId": "<projectId>"
    },
    "name": "get_eval_data"
  }
}

Input Schema

Name	Required	Description	Default
`projectId`	Yes	Project name or ID

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.7.54Aug 20, 2026

TDQS

A3.8/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and discloses important behavioral nuances: mature-send windows, bounce metrics spanning all sends, reply rates excluding bounces/auto-replies, and the threaded-only lower-bound nature of per-bucket bounces. It does not mention return format, permissions, or rate limits, but the counting semantics are unusually well specified.

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

Conciseness3/5

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

The description is dense and runs on as one long sentence with multiple parenthetical asides, making it hard to scan. However, nearly every clause carries substantive information about metric definitions, so the length is not padded with filler.

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?

For a complex metrics tool with no output schema and no annotations, the description covers many metric fields, bucket definitions, and counting caveats, including the maturity-window distinction and null provenance bucket. It stops short of describing the exact return shape or the data-sufficiency check outcome, but overall coverage is strong relative to complexity.

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?

The single parameter projectId is already fully documented in the schema with 100% coverage, so the description does not need to add parameter syntax. The project-scoped context is implicit in the schema and the description adds no extra parameter-specific meaning.

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 opens with 'Evaluation statistics for a project' and enumerates specific metric families (response rates, channel performance, sentiment breakdown, targeting axes), making the tool's resource and output scope clear. It is clearly distinguishable from sibling tools like get_outbound_targets or get_mailbox_health.

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 when project evaluation statistics are needed, but it never explicitly states when to choose this tool over alternatives or lists exclusions. There is no mention of prerequisites, typical scenarios, or when not to use it.

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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