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MCP FishBase Server

by lundgrenalex

get_distribution

Retrieve geographic distribution and occurrence data for marine species using scientific names. Access FishBase marine biology records to identify where species are found globally.

Instructions

Get distribution/occurrence information for a species

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
species_nameYesScientific name of the species

Implementation Reference

  • src/index.ts:82-95 (registration)
    Registration of the 'get_distribution' tool in the MCP server's ListToolsRequestHandler, including name, description, and input schema.
    {
      name: "get_distribution",
      description: "Get distribution/occurrence information for a species",
      inputSchema: {
        type: "object",
        properties: {
          species_name: {
            type: "string",
            description: "Scientific name of the species",
          },
        },
        required: ["species_name"],
      },
    },
  • MCP CallToolRequestHandler case for 'get_distribution' that calls the FishBaseAPI.getDistribution method and returns JSON response.
    case "get_distribution":
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(
              await fishbaseAPI.getDistribution(args.species_name as string),
              null,
              2
            ),
          },
        ],
      };
  • Core implementation of getDistribution in FishBaseAPI: retrieves species SpecCode, queries occurrence table, filters by SpecCode, handles errors.
    async getDistribution(speciesName: string): Promise<any[]> {
      try {
        const speciesData = await this.getSpecies(speciesName);
        if (speciesData.length === 0) {
          throw new Error(`Species not found: ${speciesName}`);
        }
        
        const specCode = speciesData[0].SpecCode;
        const distData = await this.queryTable('occurrence');
        
        return distData.filter((row: any) => row.SpecCode === specCode);
      } catch (error) {
        throw new Error(`Failed to get distribution data: ${error}`);
      }
    }
  • Input schema definition for the 'get_distribution' tool, specifying required 'species_name' parameter.
    inputSchema: {
      type: "object",
      properties: {
        species_name: {
          type: "string",
          description: "Scientific name of the species",
        },
      },
      required: ["species_name"],
    },
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 for behavioral disclosure. While 'Get' implies a read operation, the description doesn't specify whether this requires authentication, has rate limits, returns structured or unstructured data, provides pagination for large result sets, or indicates what happens with invalid species names. For a tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

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 perfectly concise - a single sentence that directly states the tool's purpose with zero wasted words. It's front-loaded with the core functionality and doesn't include unnecessary elaboration. Every word earns its place in this minimal but complete statement of purpose.

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 lack of annotations and output schema, the description is insufficiently complete. For a tool that presumably returns distribution data (which could be complex - geographic ranges, occurrence points, temporal patterns), the description doesn't indicate what format the information comes in, whether it includes metadata, or how comprehensive the results are. The agent has no guidance on what to expect from this tool's output.

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 doesn't add any parameter-specific information beyond what's already in the schema. With 100% schema description coverage (the single parameter 'species_name' has a clear description in the schema), the baseline score of 3 is appropriate. The description doesn't elaborate on format expectations (e.g., 'Homo sapiens' vs 'homo sapiens'), validation behavior, or provide usage examples.

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 distribution/occurrence information for a species' - a specific verb ('Get') and resource ('distribution/occurrence information') with a clear target ('for a species'). It distinguishes from some siblings like 'get_ecology' or 'get_morphology' by focusing on distribution rather than other biological aspects. However, it doesn't explicitly differentiate from 'get_species' or 'search_species' which might also provide distribution data.

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. With siblings like 'get_species', 'search_species', and 'get_ecology' available, there's no indication whether this tool provides more detailed distribution data, different data formats, or serves a distinct use case. The agent must infer usage from the tool name alone.

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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