Skip to main content
Glama

identify_at_point

Retrieve attributes at a specific WGS84 longitude/latitude across one or more layers. Pinpoint what exists at any location by querying a selected source.

Instructions

What is at a point: attributes at the given lon/lat (WGS84) for one or more layers of a source.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYes
lonYes
layersNo
sourceYes
toleranceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions WGS84 and the return of attributes, implying a read-like operation, but it does not explain tolerance behavior, layer filtering semantics, error conditions, or response structure. This is insufficient for an unpsecified geospatial operation.

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 description is concise and front-loaded with the core question, followed by the precise coordinate reference and layer scope. It contains no filler, though its brevity leaves substantial room for richer content.

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

Completeness2/5

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

With 5 parameters, no annotations, and no schema descriptions, the description omits important details like tolerance meaning, layers default behavior, and expected output beyond generic 'attributes'. The presence of an output schema helps but does not make up for missing parameter and usage context in the description.

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 0%, so the description is the only param guidance. It clarifies that lon/lat are in WGS84 and that it applies to one or more layers of a source, which partially covers source, lon, lat, and layers. However, it entirely ignores tolerance and does not explain how layers are specified or filtered, so the compensation is incomplete.

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 clearly states the operation: it returns attributes for a given lon/lat in WGS84 for one or more layers of a source. This distinguishes it from sibling tools like describe_layer or list_services by tying the operation to a spatial point, though it does not explicitly differentiate from query_layer.

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?

No guidance is given on when to use this tool instead of query_layer or other siblings. There are no explicit use cases, exclusions, or alternative tool references, leaving the agent to infer when identify_at_point is the right choice.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/davidcervigonluna/IGN-IGME-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server