MapmyIndia Mappls MCP Server

Mappls Text Search

mappls_text_search

Find places in India using natural language text queries. Misspelled search terms are auto-corrected to return matching place entities, such as landmarks, businesses, and locations.

Instructions

Perform smart text search across place entities with auto-correction for spelling variations.

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_text_search"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes	Full text search string (e.g. "Coffee shops near Indiranagar Bangalore").
`location`	No	Optional lat,lng string for location biasing (e.g. "12.9716,77.5946").

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 4, 2026

TDQS

B3.3/5.0

Behavior3/5

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

With no annotations, the description carries the behavioral burden, and it does add one useful trait: auto-correction for spelling variations. It also implies a read-only search over place entities, but it does not disclose ordering, result limits, fallback behavior, or how the optional location bias affects ranking, so transparency is only partial.

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 one sentence with no filler or repetition; it front-loads the core action and includes the key behavioral feature. Every word adds value, making it an example of appropriate conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is sufficient for understanding the basic operation, and the schema plus output schema cover parameters and return structure. However, with eight sibling tools and no mention of how this differs from mappls_autosuggest or mappls_geocode, the definition lacks enough contextual grounding for reliable tool selection.

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 the schema fully documents both 'query' and 'location'; the description does not need to repeat parameter details. Baseline 3 applies because the description adds no parameter-specific semantics beyond the general notion of text search already captured by the schema.

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 states a clear action ('perform smart text search') and a clear resource ('place entities'), adding a distinctive feature ('auto-correction for spelling variations'). It does not explicitly differentiate from siblings like mappls_autosuggest or mappls_search_nearby, so it falls short of a 5, but the core purpose is unambiguous.

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

Usage Guidelines2/5

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

There is no guidance about when to use this tool versus alternatives such as mappls_autosuggest, mappls_geocode, or mappls_search_nearby. 'Perform smart text search' restates the function rather than providing selection criteria or exclusion conditions, leaving the agent to infer usage context from the tool name and schema.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server