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lundgrenalex

MCP FishBase Server

by lundgrenalex

validate_species_name

Verify and correct scientific species names using FishBase marine biology data to ensure accurate species identification and data consistency.

Instructions

Validate and correct species scientific names

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
species_nameYesScientific name to validate

Implementation Reference

  • The core handler function implementing the validation logic: checks for exact species match using getSpecies, falls back to search for suggestions.
    async validateSpeciesName(speciesName: string): Promise<{ 
      valid: boolean; 
      suggestions?: string[]; 
      match?: SpeciesData 
    }> {
      try {
        const exactMatch = await this.getSpecies(speciesName);
        if (exactMatch.length > 0) {
          return { valid: true, match: exactMatch[0] };
        }
    
        const searchResults = await this.searchSpecies(speciesName, 5);
        return {
          valid: false,
          suggestions: searchResults.map(s => `${s.Genus} ${s.Species}`),
        };
      } catch (error) {
        throw new Error(`Failed to validate species name: ${error}`);
      }
    }
  • src/index.ts:110-123 (registration)
    Tool registration in the MCP ListTools response, defining name, description, and input schema.
    {
      name: "validate_species_name",
      description: "Validate and correct species scientific names",
      inputSchema: {
        type: "object",
        properties: {
          species_name: {
            type: "string",
            description: "Scientific name to validate",
          },
        },
        required: ["species_name"],
      },
    },
  • JSON schema defining the input parameters for the tool.
    inputSchema: {
      type: "object",
      properties: {
        species_name: {
          type: "string",
          description: "Scientific name to validate",
        },
      },
      required: ["species_name"],
    },
  • Dispatch handler in MCP CallToolRequestHandler that calls the validateSpeciesName method with parsed arguments and formats response.
    case "validate_species_name":
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(
              await fishbaseAPI.validateSpeciesName(args.species_name as string),
              null,
              2
            ),
          },
        ],
      };
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 tool 'validate[s] and correct[s]' but doesn't explain what validation entails (e.g., checks for taxonomic rules, format), how corrections are applied (e.g., suggests alternatives, auto-fixes), or behavioral traits like error handling, rate limits, or permissions required. This leaves significant gaps for a tool that modifies input.

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 with zero wasted words. It is front-loaded with the core purpose ('Validate and correct species scientific names'), making it easy to parse. Every word earns its place by directly contributing to understanding the tool's function.

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 (involving validation and correction of scientific names) and lack of annotations and output schema, the description is incomplete. It doesn't cover what validation checks are performed, how corrections are returned, error cases, or output format. For a tool that likely returns corrected names or validation results, this omission is significant.

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 input schema has 100% description coverage, with the single parameter 'species_name' documented as 'Scientific name to validate'. The description adds no additional meaning beyond this, such as format examples (e.g., binomial nomenclature) or correction specifics. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.

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 with specific verbs ('validate and correct') and identifies the resource ('species scientific names'). It distinguishes itself from siblings like 'common_to_scientific' (conversion) or 'get_species' (retrieval) by focusing on validation/correction. However, it doesn't explicitly differentiate from all siblings (e.g., 'search_species' might involve validation).

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 prerequisites (e.g., input format expectations), exclusions, or comparisons to siblings like 'common_to_scientific' for name conversion or 'search_species' for validation during search. Usage is implied from the purpose but not explicitly stated.

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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