CRM Connector

Propose: Turn a lead into a quote, job or contact

propose_convert_lead

AI Agents (inside one system): Use when the person asks for this in a system whose AI agent may change records: A lead made into a new estimate or a new job, or into a contact, in the person's system, which then counts as converted; it cannot be turned back, and only the person can approve 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_convert_lead"
  }
}

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

A4.6/5.0

Behavior5/5

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

Beyond the annotations it discloses that nothing is written yet, that the answer contains per-record before/after state plus a proposal id, that the resulting conversion 'cannot be turned back', that only the person can approve it, and that outbound sends to third parties must be surfaced first. That is exactly the mutation-safety context an agent needs and the annotations alone cannot convey.

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?

Front-loaded and dense — the effect, the batching limit, the proposal response, the approval handshake and the anti-pattern all land in one tight paragraph with no filler. The unusual 'AI Agents (inside one system):' opener and the run-on accumulation of clauses cost it a point versus a cleaner structure.

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?

For a proposal-style mutation with an output schema, this is complete: it explains the batch cap, the irreversibility, the approval gate, the returned proposal id, and the handoff to apply_proposal. An agent could call it, present the result, and choose the right next tool without further information.

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 'system', 'items', 'id', 'into' and 'type' are already documented, and the description largely restates them ('Give one item per record, at most 25'). It does reinforce the critical behavioural constraint of never choosing the AI agent itself, but adds little syntax or format meaning beyond the schema. Baseline 3 is appropriate.

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 names a specific verb and resource constellation — 'A lead made into a new estimate or a new job, or into a contact' — which maps exactly onto the 'into' enum. It also distinguishes itself from the read siblings ('Not for reading (use search_records or get_record)') and from its application step ('only then call apply_proposal').

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 when-to-use condition ('Use when the person asks for this in a system whose AI agent may change records'), explicit exclusions ('Not for reading'), and the exact downstream sequence: show the diff, flag outbound sends, ask the person, then call apply_proposal or share the approve link when needsApproval is true. Almost nothing is left to inference.

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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