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mattjegan

eBird MCP Server

by mattjegan

get_checklists_on_date

Retrieve bird observation checklists submitted on a specific date and location from the eBird database.

Instructions

Get checklists submitted on a specific date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
region_codeYesCountry, subnational1, subnational2, or location code
yearYesYear
monthYesMonth
dayYesDay of month
sort_keyNoSort by observation or submission dateobs_dt
max_resultsNoNumber of checklists to return

Implementation Reference

  • The handler function for the 'get_checklists_on_date' tool. It constructs the eBird API endpoint using the provided region and date parameters, calls the shared makeRequest helper, and formats the response as MCP content.
    async (args) => {
      const result = await makeRequest(
        `/product/lists/${args.region_code}/${args.year}/${args.month}/${args.day}`,
        { sortKey: args.sort_key, maxResults: args.max_results }
      );
      return { content: [{ type: "text", text: JSON.stringify(result, null, 2) }] };
    }
  • Zod schema defining the input parameters for the tool: region_code, year, month, day, sort_key, and max_results.
    {
      region_code: z.string().describe("Country, subnational1, subnational2, or location code"),
      year: z.number().describe("Year"),
      month: z.number().min(1).max(12).describe("Month"),
      day: z.number().min(1).max(31).describe("Day of month"),
      sort_key: z.enum(["obs_dt", "creation_dt"]).default("obs_dt").describe("Sort by observation or submission date"),
      max_results: z.number().min(1).max(200).default(10).describe("Number of checklists to return"),
    },
  • src/index.ts:320-338 (registration)
    The server.tool registration call that defines and registers the 'get_checklists_on_date' tool with its description, input schema, and inline handler function.
    server.tool(
      "get_checklists_on_date",
      "Get checklists submitted on a specific date.",
      {
        region_code: z.string().describe("Country, subnational1, subnational2, or location code"),
        year: z.number().describe("Year"),
        month: z.number().min(1).max(12).describe("Month"),
        day: z.number().min(1).max(31).describe("Day of month"),
        sort_key: z.enum(["obs_dt", "creation_dt"]).default("obs_dt").describe("Sort by observation or submission date"),
        max_results: z.number().min(1).max(200).default(10).describe("Number of checklists to return"),
      },
      async (args) => {
        const result = await makeRequest(
          `/product/lists/${args.region_code}/${args.year}/${args.month}/${args.day}`,
          { sortKey: args.sort_key, maxResults: args.max_results }
        );
        return { content: [{ type: "text", text: JSON.stringify(result, null, 2) }] };
      }
    );
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves checklists but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, error handling, or the format of returned data. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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, clear sentence that directly states the tool's purpose without any fluff or redundancy. It's front-loaded and efficiently communicates the core functionality, making it easy to understand at a glance.

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 the complexity of 6 parameters (4 required), no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (e.g., checklist format, pagination), behavioral traits, or usage context. For a data retrieval tool with multiple inputs, more detail is needed to ensure proper agent invocation.

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?

The description mentions 'a specific date,' which aligns with the year, month, and day parameters in the schema. However, with 100% schema description coverage, the schema already fully documents all parameters, including region_code, sort_key, and max_results. The description adds minimal value beyond what the schema provides, meeting the baseline score for high coverage.

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 checklists submitted on a specific date.' It specifies the verb ('Get') and resource ('checklists'), and indicates the scope ('submitted on a specific date'). However, it doesn't explicitly differentiate from sibling tools like 'get_recent_checklists' or 'get_checklist', which might have overlapping functionality, so it doesn't reach a perfect score.

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. It doesn't mention sibling tools like 'get_recent_checklists' or 'get_checklist', nor does it specify prerequisites or exclusions. The user is left to infer usage based on the purpose alone, which is insufficient for clear decision-making.

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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