bounding_boxBounding box (SW/NE corners) around a center point at a given radius. unit = km (default) or mi.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | ||
| lon | Yes | ||
| unit | No | km (default) or mi. | |
| radius | Yes | Radius from the center. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "lat": 40.7128,
+ "lon": -74.006,
+ "radius": 10
+ },
+ {
+ "lat": 51.5074,
+ "lon": -0.1278,
+ "radius": 5,
+ "unit": "mi"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds the behavioral detail that the tool returns SW/NE corners of the bounding box. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that front-loads the purpose and includes key parameter details. No redundant words, highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Annotations are comprehensive, and the tool's output is implied by 'SW/NE corners'. The description is sufficient for a simple geometric computation tool, though it could explicitly state the return format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 4 parameters with 50% description coverage (unit and radius have descriptions). The description adds context by clarifying the output format (SW/NE corners) and the default unit, complementing the schema. However, it does not add new semantic constraints beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool computes a bounding box (SW/NE corners) from a center point and radius. It distinguishes from sibling spatial tools like centroid or midpoint by specifying the output geometry and parameters.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides the default unit (km) and alternative (mi), but no explicit guidance on when to use bounding_box vs other sibling tools. Usage context is implied through the description of the tool's function, but no exclusions or alternative recommendations are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded are nearly identical; bet_research and polymarket_edges both analyze Polymarket markets). Descriptions are detailed but the sheer number of similar tools creates ambiguity for agents.
Most tools follow a verb_noun pattern (e.g., list_subscriptions, create_subscription), but a few use noun_verb (bet_research) or are standalone nouns (centroid, midpoint). Overall, the pattern is fairly consistent despite minor deviations.
35 tools is on the high side for an MCP server. The scope is broad (data access, prediction markets, geospatial, memory, subscriptions), so each tool earns its place, but the number is borderline for coherence.
The tool set covers a wide range of functionalities: data querying, entity profiles, comparisons, prediction market analysis, geospatial, memory, subscriptions, etc. Minor gaps exist (no batch operations or data export), but the core workflows are well-supported.