CRM Connector

Propose: Mark a conversation

propose_update_conversation

AI Agents (inside one system): Use when the person asks for this in a system whose AI agent may change records: A conversation marked read or unread, or starred, by the id list_related_records gives it. Give one item per record, at most 25. Nothing is written yet: the answer shows, per record, what it holds now and would hold after, plus a proposal id. Show it to the person, say so first when something would be sent to the people in their system, ask whether to go ahead, and only then call apply_proposal; when needsApproval is true, share the approve link instead. Not for reading (use search_records or get_record).

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": {
      "system": "<system>",
      "items": "<items>"
    },
    "name": "propose_update_conversation"
  }
}

Input Schema

Name	Required	Description	Default
`items`	Yes	One object per record, at most 25.
`system`	Yes	Which AI agent: the system's id from get_ai_agent_options, or the name of the system or its connection. Ask the person which AI agent they mean unless they named it in this conversation; never pick one yourself.

Output Schema

Name	Required	Description	Default
`notice`	No
`changes`	Yes
`systemId`	Yes
`warnings`	Yes
`expiresAt`	Yes
`approveUrl`	Yes
`proposalId`	Yes
`systemName`	Yes
`needsApproval`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 5, 2026

changedInput schema / properties / system / description

Previous value: -"Which AI agent: the system's id from get_ai_agents_info, or the name of the system or its connection. Ask the person which AI agent they mean unless they named it in this conversation; never pick one yourself."New value: +"Which AI agent: the system's id from get_ai_agent_options, or the name of the system or its connection. Ask the person which AI agent they mean unless they named it in this conversation; never pick one yourself."

AddedNov 5, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=false, openWorldHint=true), it discloses that nothing is written at proposal time, that the response contains per-record before/after state plus a proposal id, and that a side effect can reach people outside the system. It also explains the needsApproval branch and the required handoff to apply_proposal. This is exactly the behavioral context annotations cannot carry.

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?

Length is reasonable for the workflow it encodes and the usage condition is front-loaded, but the text is a single dense block with an awkward opening clause ('A conversation marked read or unread, or starred, by the id list_related_records gives it'). Breaking the approval flow into discrete steps would read far better for an agent parsing it.

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?

With an output schema handling return shape and annotations handling safety, the description supplies the missing pieces: the propose-then-approve workflow, the external-send warning, and the apply_proposal handoff. An agent has everything needed to call this correctly and know what to do with the result.

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%, so the baseline is 3. The description does little beyond the schema: 'one item per record, at most 25' restates minItems/maxItems and the array description, and the list_related_records provenance for conversation ids is already in the schema. The system-selection guidance lives in the schema rather than the description.

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?

The description names the concrete effects (mark a conversation read/unread or starred) and how records are identified (ids from list_related_records), and the title supplies the 'Propose:' verb. It also explicitly excludes reading, naming get_record and search_records instead. The opening clause is grammatically tangled, which slightly blunts an otherwise precise statement of purpose.

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?

It gives an explicit trigger ('Use when the person asks for this in a system whose AI agent may change records'), an explicit exclusion ('Not for reading'), and named alternatives (search_records, get_record). It goes further and prescribes the downstream flow: show the result to the person, flag sends to people in their system, ask for approval, then call apply_proposal — and share the approve link instead when needsApproval is true.

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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