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Add many pins

bulk_add_comps

Add many comps in one call instead of calling add_comp repeatedly — use this for CSV-style bulk imports (tens to thousands of rows). Pass an array of comp objects; each one accepts the same fields as add_comp, INCLUDING customFields (a {column: value} map for any extra spreadsheet columns) and externalId. Give each row an externalId (e.g. its source Record #) so an interrupted import can be safely re-sent — rows whose externalId already exists in the layer are updated in place, not duplicated. latitude+longitude OR address (geocoded automatically; a row whose address is not found fails with a reason). Rows are comps unless asListings is true — never mark rows as listings yourself; set asListings only when the Broker says these are their listings. Split datasets over 1000 rows into multiple calls. Returns per-row results mapping each input index/externalId to its comp id and status (inserted/updated/failed), and with asListings how many are on the Marketplace and which fields keep the rest off.

Input Schema

NameRequiredDescriptionDefault
compsYesUp to 1000 comps per call.
contextYesExplain 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."
layerIdNoDefault layer ID for comps that don't specify their own layerId.
llm_modelYesThe 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.
offerTypeNoWith asListings: the Offer type for every row. A row's own offerType wins.
asListingsNotrue only when the Broker said these rows are their listings: every row becomes a Listing (Active, confirmed today, the caller as Listing broker). Default false: rows are comps and are never inferred as listings.
assetClassNoWith asListings: the Asset class for every row. A row's own assetClass wins.
conversation_idNoPass 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.
upsertOnExternalIdNoDefault true. When a row's externalId already exists in the target layer, update it instead of inserting a duplicate. Set false to always insert.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=false and destructiveHint=false. The description adds substantial behavior: upsert semantics via externalId, that matching rows are 'updated in place, not duplicated', that missing addresses 'fail with a reason', automatic geocoding, the 1000-row limit, and Marketplace/field behavior under asListings. These are exactly the mutation traits annotations 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?

The purpose and the add_comp alternative are front-loaded, and each sentence carries operational content. It is a dense single block for a genuinely complex 9-parameter tool, so length is justified, though the long asListings/customFields clauses could be split for readability.

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?

There is no output schema, and the description compensates by describing the return shape (per-row results mapping index/externalId to comp id and status: inserted/updated/failed). Combined with failure modes, limits, and upsert behavior, an agent has everything needed to call this 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 description coverage is 100%, so baseline is 3, but the description adds real value beyond the schema: it explains the externalId contract (e.g. 'its source Record #') as the idempotency key, the latitude+longitude OR address either/or rule, and the guidance that asListings should only be set on the Broker's instruction. It complements rather than merely repeats the schema.

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+resource ('Add many comps') and immediately distinguishes itself from the sibling add_comp by framing itself as the alternative to calling it repeatedly. An agent can tell exactly what this does and when it supersedes add_comp.

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 when-to-use (CSV-style bulk imports, tens to thousands of rows), a named alternative (add_comp), a hard threshold ('split datasets over 1000 rows'), and a clear exclusion on asListings ('never mark rows as listings yourself; set asListings only when the Broker says these are their listings'). This is as complete as usage guidance gets.

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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