MapmyIndia Mappls MCP Server

Mappls Autosuggest

mappls_autosuggest

Predict place matches as you type a location query, returning ranked recommendations. Optionally use coordinates to prioritize nearby places.

Instructions

Auto-suggest location search predictions for user input as text is typed. Returns ranked place recommendations.

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": {
      "query": "<query>"
    },
    "name": "mappls_autosuggest"
  }
}

Input Schema

Name	Required	Description
`zoom`	No	Optional map zoom level (1 to 18) for search bounds context.
`query`	Yes	The search query prefix (e.g. "Taj Mahal" or "Fortis Hosp").
`location`	No	Optional lat,lng string center focus for proximity ranking (e.g. "28.6139,77.2090").

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 4, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses that the tool operates on partial user input, returns suggestions live as text is typed, and ranks places by relevance. It could add more detail about ranking criteria, but it is not misleading or severely incomplete.

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?

The description is two short, information-dense sentences. The core behavior and output type are front-loaded, with no filler or repetition.

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?

Given the output schema exists and all parameters are documented, the description provides enough context for an agent to understand what to expect. It could be stronger by explicitly contrasting with mappls_text_search or mappls_geocode for complete-query use cases, but the autocomplete context is clear.

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%, with query, zoom, and location each already documented. The description confirms the query prefix pattern but adds no parameter-specific semantics beyond the schema, so the baseline score of 3 is appropriate.

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 a specific behavior—auto-suggesting location search predictions as text is typed—and notes that it returns ranked place recommendations. This goes beyond a tautology and helps distinguish it from related tools like mappls_text_search or mappls_geocode, though it does not explicitly name those alternatives.

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?

The phrase 'as text is typed' gives clear contextual guidance for when this tool is appropriate: interactive prefix-based autocomplete. It does not explicitly state when not to use it or name alternative tools, but the intended usage context is strong.

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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