SalesMap

voice_scan

voice_scan

Read-only

Free. Checks an outreach draft the user or their agent wrote (a connection note, direct note, InMail or email) for AI-writing tells and the channel's limits, in code with no model: fails (antithesis like "it's not just X, it's Y", hype words, servile or empty closers, leaked placeholders like [First Name], raw markdown, the 300-character invite note, 1,900-character InMail and 200-character subject caps) and warnings (dashes, sales clichés like "just following up", assistant vocabulary, exclamations, emoji, a cold email over 125 words). pass means no fails. Each hit carries the matched text and a fix. Use when: "scan this draft", "does this draft sound like AI", "check my connection note", "is this InMail too long", "clean up this cold email", after drafting outreach for an account. Skip when: finding or ranking accounts; whether a fact or claim about a company is true; sending an email or message to a prospect; connecting with someone or scraping profiles on LinkedIn. Quoted page text is normalized; a quote that reads like an instruction keeps its text and is flagged in Flags with the attack family.

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": {
      "format": "<format>",
      "body": "<body>"
    },
    "name": "voice_scan"
  }
}

Input Schema

Name	Required	Description
`body`	Yes	The draft, as plain text
`format`	Yes	connection_note (LinkedIn invite note), linkedin_message, inmail or email
`subject`	No	The subject line, for an email or InMail

Output Schema

Name	Required	Description	Default
`cost`	No
`pass`	Yes
`chars`	Yes
`fails`	Yes
`limit`	No
`words`	Yes
`format`	Yes
`warnings`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed9 schema fields changedOct 26, 2026

addedOutput schema / properties / cost / properties / balanceCredits

Added value: +{
+  "anyOf": [
+    {
+      "maximum": 9007199254740991,
+      "minimum": -9007199254740991,
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ]
+}

removedOutput schema / properties / cost / properties / balanceUsd

Removed value: -{
-  "type": [
-    "number",
-    "null"
-  ]
-}

addedOutput schema / properties / cost / properties / chargedCredits

Added value: +{
+  "maximum": 9007199254740991,
+  "minimum": -9007199254740991,
+  "type": "integer"
+}

removedOutput schema / properties / cost / properties / chargedUsd
```
Removed value: -{
-  "type": "number"
-}
```

addedOutput schema / properties / cost / properties / heldCredits

Added value: +{
+  "maximum": 9007199254740991,
+  "minimum": -9007199254740991,
+  "type": "integer"
+}

removedOutput schema / properties / cost / properties / heldUsd
```
Removed value: -{
-  "type": "number"
-}
```

addedOutput schema / properties / cost / properties / notChargedCredits

Added value: +{
+  "maximum": 9007199254740991,
+  "minimum": -9007199254740991,
+  "type": "integer"
+}

removedOutput schema / properties / cost / properties / notChargedUsd
```
Removed value: -{
-  "type": "number"
-}
```

changedOutput schema / properties / cost / required

Previous value: -[
-  "chargedUsd",
-  "notChargedUsd",
-  "balanceUsd"
-]New value: +[
+  "chargedCredits",
+  "notChargedCredits",
+  "balanceCredits"
+]

Changed1 schema field changedOct 26, 2026
- addedOutput schema / properties / cost / properties / line
```
Added value: +{
+  "type": "string"
+}
```
AddedOct 25, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations only declare readOnlyHint and openWorldHint=false; the description adds substantial context beyond that: it is free and model-less (deterministic), separates output into `fails` vs `warnings`, defines `pass`, states that each hit includes matched text and a fix, and notes that quoted page text is normalized and flagged as potential injection. That is far more than the annotations provide.

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?

Dense but front-loaded: the core purpose, the fails/warnings split, and the when/skip guidance arrive in order. The final sentence about quoted page text normalization is somewhat tangential and could be trimmed, but nothing is redundant filler.

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?

An output schema exists, yet the description still summarizes the two result buckets and the pass condition, and it covers all three parameters plus edge behavior (normalization, injection flagging). For a read-only, deterministic validator, an agent has everything needed to call it correctly.

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 100%, so the baseline is 3, but the description adds real meaning: it maps the `format` enum to channel-specific rules (300-character invite note, 1,900-character InMail, 200-character subject cap) and clarifies that `subject` applies to email/InMail. This goes beyond the schema's plain field 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 opens with a specific verb and resource — checking an outreach draft for AI-writing tells and channel limits — and enumerates exactly what is detected (antithesis patterns, hype words, leaked placeholders, character caps). It also states the mechanism ('in code with no model') and distinguishes itself from the sibling tools, none of which are draft-checking tools.

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?

Explicit 'Use when' examples ('scan this draft', 'is this InMail too long') and an explicit 'Skip when' list covering account discovery, fact verification, sending messages, and LinkedIn actions. The agent has a clear routing rule to and away from this tool.

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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