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MCP FishBase Server

by lundgrenalex

common_to_scientific

Convert fish common names to scientific names using FishBase marine biology data for accurate species identification and research.

Instructions

Convert common name to scientific name

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
common_nameYesCommon name of the fish

Implementation Reference

  • Handler for the 'common_to_scientific' tool in the MCP CallToolRequestSchema. It invokes the FishBaseAPI method and returns the result as JSON text content.
    case "common_to_scientific":
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(
              await fishbaseAPI.commonToScientific(args.common_name as string),
              null,
              2
            ),
          },
        ],
      };
  • Input schema definition for the 'common_to_scientific' tool, specifying that 'common_name' (string) is required.
    inputSchema: {
      type: "object",
      properties: {
        common_name: {
          type: "string",
          description: "Common name of the fish",
        },
      },
      required: ["common_name"],
    },
  • src/index.ts:124-137 (registration)
    Registration of the 'common_to_scientific' tool in the MCP server's ListTools response, including name, description, and input schema.
    {
      name: "common_to_scientific",
      description: "Convert common name to scientific name",
      inputSchema: {
        type: "object",
        properties: {
          common_name: {
            type: "string",
            description: "Common name of the fish",
          },
        },
        required: ["common_name"],
      },
    },
  • Helper method in FishBaseAPI that implements the core logic: queries the 'species' table and filters rows where FBname contains the given common name, returning up to 10 matches.
    async commonToScientific(commonName: string): Promise<SpeciesData[]> {
      try {
        const speciesData = await this.queryTable('species');
        
        const filtered = speciesData.filter((row: any) => 
          row.FBname?.toLowerCase().includes(commonName.toLowerCase())
        );
    
        return filtered.slice(0, 10);
      } catch (error) {
        throw new Error(`Failed to convert common name: ${error}`);
      }
    }
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the conversion action but doesn't describe key behaviors: whether it's case-sensitive, handles partial matches, returns errors for invalid names, or provides additional metadata. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it operates.

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 extremely concise with a single, clear sentence that directly states the tool's purpose. There is zero wasted language or redundancy, and it's front-loaded with the core functionality. Every word earns its place in conveying the essential action.

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 tool's complexity (a conversion function with no annotations and no output schema), the description is incomplete. It doesn't explain what the output looks like (e.g., scientific name format, error responses), behavioral traits, or how it interacts with sibling tools. For a tool that performs a specific transformation, more context is needed to guide effective use.

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 schema description coverage is 100%, with the single parameter 'common_name' fully documented in the schema as 'Common name of the fish'. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Given the high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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 function as converting common names to scientific names, using specific verbs ('convert') and resources ('common name', 'scientific name'). It distinguishes itself from siblings like 'validate_species_name' or 'search_species' by focusing on name conversion rather than validation or broader searching. However, it doesn't explicitly mention the biological domain (fish) that the input schema implies.

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 'validate_species_name' (which might check name validity) or 'search_species' (which might return broader information), nor does it specify prerequisites such as needing a valid common name. Usage is implied but not explicitly defined.

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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