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icon_autocomplete

Generate icon search term suggestions to help users discover related keywords and refine their icon searches within the Noun Project MCP Server.

Instructions

Get autocomplete suggestions for icon search terms. Useful for helping users discover related terms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesPartial search term to get suggestions for
limitNoMaximum number of suggestions to return

Implementation Reference

  • Core implementation of the icon_autocomplete tool: constructs query params and makes authenticated GET request to Noun Project /v2/icon/autocomplete endpoint.
    async autocomplete(params: AutocompleteParams) {
      const queryParams = new URLSearchParams({
        query: params.query,
        ...(params.limit ? { limit: String(params.limit) } : {}),
      });
    
      const url = `${BASE_URL}/v2/icon/autocomplete?${queryParams}`;
      const headers = this.oauth.getHeaders(url);
    
      const response = await this.client.get('/v2/icon/autocomplete', {
        params: Object.fromEntries(queryParams),
        headers,
      });
    
      return response.data;
    }
  • Tool definition including name, description, and input schema for icon_autocomplete, part of the exported TOOLS array used for MCP tool listing.
    {
      name: 'icon_autocomplete',
      description:
        'Get autocomplete suggestions for icon search terms. Useful for helping users discover related terms.',
      inputSchema: {
        type: 'object',
        properties: {
          query: {
            type: 'string',
            description: 'Partial search term to get suggestions for',
          },
          limit: {
            type: 'number',
            description: 'Maximum number of suggestions to return',
          },
        },
        required: ['query'],
      },
    },
  • MCP CallToolRequest handler switch case for icon_autocomplete: delegates to api.autocomplete and formats response as MCP content.
    case 'icon_autocomplete': {
      const result = await api.autocomplete(args as any);
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(result, null, 2),
          },
        ],
      };
    }
  • TypeScript interface defining input parameters for the autocomplete method.
    export interface AutocompleteParams {
      query: string;
      limit?: number;
    }
  • src/index.ts:42-46 (registration)
    MCP ListToolsRequest handler that returns the TOOLS array, registering icon_autocomplete among others.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return {
        tools: TOOLS,
      };
    });
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 mentions the tool provides 'suggestions' and is 'useful for helping users discover related terms,' which implies a read-only, non-destructive operation. However, it doesn't disclose key behavioral traits such as rate limits, authentication needs, response format, or error handling, which are important for an agent to use it correctly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, consisting of two sentences that directly state the tool's purpose and utility. There's no unnecessary information or repetition, making it efficient. However, it could be slightly improved by integrating usage guidance more seamlessly, but overall it's well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the basic purpose and utility, but without annotations or an output schema, it lacks details on behavioral traits and return values, which could hinder an agent's ability to use it effectively in all contexts.

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 clear documentation for both parameters ('query' and 'limit'), so the schema does the heavy lifting. The description doesn't add any meaningful parameter semantics beyond what's in the schema, such as examples or constraints, but it doesn't need to compensate for gaps, so a baseline score of 3 is appropriate.

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 a specific verb ('Get autocomplete suggestions') and resource ('icon search terms'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate this from sibling tools like 'search_icons' or 'get_icon', which might offer similar search-related functionality, so it doesn't reach the highest score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides implied usage guidance by stating it's 'useful for helping users discover related terms,' suggesting it should be used for search assistance. However, it lacks explicit guidance on when to use this tool versus alternatives like 'search_icons' or 'get_icon,' and doesn't specify any exclusions or prerequisites, leaving some ambiguity.

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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