MappaIQ

Contact a Marketplace broker

express_interest

Send a lead to a Marketplace listing's broker (from search_public_listings) — use it when the user wants to inquire about or be contacted about a public listing. The broker is emailed the user's message and how to reach them back, attributed to MappaIQ. Always confirm the message with the user before sending. Counts as 1 against the Marketplace limit. If the broker has no email on file, nothing is sent and their phone is returned instead.

Input Schema

Name	Required	Description
`id`	Yes	Listing id from search_public_listings.
`contact`	No	How the broker should reach the user back: their name and an email or phone. Defaults to the connected account's email if omitted.
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`message`	Yes	What to tell the broker — the user's interest, timeline, budget, or question. Max 1500 characters.
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 17, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Goes well beyond the annotations (readOnlyHint=false, destructiveHint=false) by disclosing exactly what happens: the broker is emailed the user's message and contact info attributed to MappaIQ, it consumes 1 Marketplace credit, and if the broker has no email on file nothing is sent and a phone number is returned instead. That quota and fallback behavior is precisely the kind of 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.

Conciseness5/5

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

Front-loads the core action and the usage trigger, then layers the behavioral caveats (attribution, confirmation requirement, credit cost, fallback) in descending priority. Every sentence carries distinct information with no 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?

A mutating, credit-consuming tool with 100% schema coverage and no output schema. The description supplies the side effects, the confirmation requirement, the quota impact, and the alternate return path, leaving no functional gap an agent would need to call it correctly.

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 all six parameters (including the verbose context, llm_model, and conversation_id guidance) are already documented in the input schema. The description adds only marginal parameter meaning by naming search_public_listings as the source of the id, which the schema also states. 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?

States a specific verb and resource: 'Send a lead to a Marketplace listing's broker.' It also anchors the resource to its source ('from search_public_listings'), so an agent can distinguish this contact/broker action from the many listing and comp tools that share the namespace.

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?

Gives an explicit triggering condition — 'use it when the user wants to inquire about or be contacted about a public listing' — plus a hard procedural prerequisite: 'Always confirm the message with the user before sending.' No alternative tool performs this action, so the absence of an alternatives clause is not a gap.

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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