eBird MCP Server

get_regional_statistics

Retrieve bird observation statistics for a specific region and date, including checklist counts, species counts, and contributor numbers from eBird data.

Instructions

Get statistics for a region on a specific date (checklist count, species count, contributor count).

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": {
      "region_code": "<region_code>",
      "year": "<year>",
      "month": "<month>",
      "day": "<day>"
    },
    "name": "get_regional_statistics"
  }
}

Input Schema

Name	Required	Description
`region_code`	Yes	Country, subnational1, subnational2, or location code
`year`	Yes	Year
`month`	Yes	Month
`day`	Yes	Day of month

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 4, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It states the tool retrieves statistics but doesn't disclose behavioral traits such as whether it's a read-only operation, potential rate limits, authentication requirements, or what happens with invalid dates/regions. The description is minimal and lacks necessary operational context.

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 a single, efficient sentence that front-loads the core purpose and includes specific metric examples. Every word earns its place with zero wasted text, making it easy to parse quickly.

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?

Given no annotations and no output schema, the description is incomplete for a tool with four required parameters. It doesn't explain what the statistics output looks like, how they're aggregated, or any error conditions. For a data retrieval tool, this leaves significant gaps in understanding its behavior and results.

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 all four parameters. The description adds no additional parameter semantics beyond implying that 'region_code' and date parameters are used to fetch statistics. This meets the baseline for high schema coverage but doesn't enhance understanding.

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 tool's purpose: 'Get statistics for a region on a specific date' with specific metrics (checklist count, species count, contributor count). It distinguishes from siblings by focusing on aggregated statistics rather than raw observations or region metadata, though it doesn't explicitly name alternatives.

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?

The description provides no guidance on when to use this tool versus alternatives like 'get_region_info' (which might provide metadata) or 'get_checklists_on_date' (which might provide raw data). It mentions what the tool does but offers no context about appropriate use cases or exclusions.

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