MappaIQ

Set pin logo

set_logo

Destructive

Put a brand logo on pins — the pin renders as a badge with the brand's logo (e.g. the McDonald's arches) instead of a colored dot. Target ONE pin by compId, or MANY by nameContains (case-insensitive match on pin names, e.g. 'mcdonald' hits every McDonald's), optionally scoped to a layer. The brand's website domain is guessed from the brand name; pass domain explicitly when the guess would be wrong (e.g. brand 'Chick-fil-A' → domain 'chick-fil-a.com'). Set remove:true to take logos off instead.

Input Schema

Name	Required	Description
`brand`	No	Brand name or website domain (e.g. 'McDonald's' or 'mcdonalds.com'). Required unless remove is true.
`compId`	No	Target a single pin by ID. Optional.
`domain`	No	Explicit website domain override, e.g. 'chick-fil-a.com'. Optional.
`remove`	No	true = remove logos from the matched pins instead of adding one
`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."
`layerName`	No	Limit nameContains matching to this layer (case-insensitive name match). Optional.
`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.
`nameContains`	No	Bulk: every Point pin whose name contains this (case-insensitive). Optional.
`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

A4.4/5.0

Behavior4/5

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

Annotations cover the write/destructive profile, and the description adds real behavioral context beyond them: the bulk case-insensitive matching semantics, the heuristic that the domain is guessed from the brand name, and the remove:true inverse mode. It omits return behavior and whether changes are reversible, keeping it from a 5.

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-loaded with the effect and the badge rendering, then the targeting modes, then the domain caveat and the remove flag. Dense but every clause carries actionable information; nothing is wasted.

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?

For a 9-parameter mutation tool with no output schema, the description covers targeting, scoping, domain override, and removal adequately, while the three analytics/meta params are fully documented in the schema. It does not state what the call returns (e.g. how many pins were updated), a minor remaining gap.

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 coverage is 100%, so the baseline is 3, but the description adds meaning the schema lacks: how nameContains matching works across pins and layers, and concretely why/when to pass `domain` (Chick-fil-A → chick-fil-a.com). This is genuinely additive over the field descriptions.

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+effect: 'Put a brand logo on pins — the pin renders as a badge ... instead of a colored dot.' This is concrete and distinguishable from siblings like set_callout or add_comp without opening any schema.

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

Usage Guidelines4/5

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

Clearly explains the two targeting modes (one pin by compId vs many by nameContains, optionally scoped to a layer) and when to override the domain guess. It does not name or compare against alternative sibling tools, so it falls short of a 5.

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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