Boardroom Revenue Control

AI-agent conversations

agent_conversations

Read-onlyIdempotent

Conversations the AI agent handled, with intent, language, message count and the deal they bound to. Contacts masked. [scope: agent:read]

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum number of rows to return.
`since`	No	today \| week \| month \| 7d \| ISO timestamp (Asia/Dubai days).

Output Schema

Name	Required	Description
`ok`	No	False when the call was refused or failed; see error.
`error`	No	Machine-readable refusal code (a string), present only when ok is false.
`message`	No	Human-readable explanation of a refusal (a string).

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 9, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "error": {
+      "description": "Machine-readable refusal code (a string), present only when ok is false."
+    },
+    "message": {
+      "description": "Human-readable explanation of a refusal (a string)."
+    },
+    "ok": {
+      "description": "False when the call was refused or failed; see error.",
+      "type": "boolean"
+    }
+  },
+  "type": "object"
+}

Changed1 schema field changedOct 9, 2026
- addedInput schema / properties / limit / description
```
Added value: +"Maximum number of rows to return."
```
First observedOct 9, 2026

TDQS

A3.6/5.0

Behavior4/5

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

Beyond the read-only/idempotent annotations, the description discloses two useful behavioral facts: contacts are masked (data redaction) and the call requires the agent:read scope. It does not cover result limits or ordering, but the core behavior is well conveyed.

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?

Two short, front-loaded sentences with zero filler: the first states what it returns, the second flags masking and required scope. Nothing redundant.

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 values need not be explained, and the description covers scope, masking, and fields. The only real gap is routing guidance versus the singular agent_get_conversation sibling.

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 100%, including the enum-like format for 'since' and the 1–200 bounds on 'limit', so the schema carries parameter meaning. The description adds nothing about parameter semantics, making 3 the correct baseline.

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 the resource precisely (conversations the AI agent handled) and enumerates the returned dimensions (intent, language, message count, bound deal), so an agent knows it is a listing of agent-handled conversations. It does not distinguish itself from the sibling agent_get_conversation, which retrieves a single conversation.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no conditions or exclusions, and no mention of the obvious alternative agent_get_conversation for a specific conversation. The 'since' parameter implies recency filtering but the description never says when this tool is the right choice.

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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