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by hackle-io

experiment-list

Retrieve a paginated list of A/B test experiments with search functionality to filter by name, description, or experiment key, enabling streamlined data access and analysis.

Instructions

Fetches a paginated list of A/B test experiments with search functionality.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNumberNo
pageSizeNo
searchKeywordNoname, description, or experimentKey of an experiment.

Implementation Reference

  • The inline async handler function for the 'experiment-list' tool. Constructs query string with pagination and search parameters, fetches data from Hackle API using WebClient.get, JSON.stringifies the result, and returns it as tool content.
    async ({ pageNumber = 1, pageSize = 100, searchKeyword = '' }) => {
      const qs = stringify(
        {
          pageNumber,
          pageSize,
          searchKeyword,
        },
        { addQueryPrefix: true },
      );
    
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(await WebClient.get(`/api/v1/experiments${qs}`)),
          },
        ],
      };
    },
  • Zod input schema defining optional parameters: pageNumber (default 1), pageSize (default 100), searchKeyword.
    {
      pageNumber: z.number().optional().default(1),
      pageSize: z.number().optional().default(100),
      searchKeyword: z.string().optional().describe('name, description, or experimentKey of an experiment.'),
    },
  • src/index.ts:15-42 (registration)
    MCP server.tool registration for 'experiment-list' tool, specifying name, description, input schema, and inline handler function.
    server.tool(
      'experiment-list',
      'Fetches a paginated list of A/B test experiments with search functionality.',
      {
        pageNumber: z.number().optional().default(1),
        pageSize: z.number().optional().default(100),
        searchKeyword: z.string().optional().describe('name, description, or experimentKey of an experiment.'),
      },
      async ({ pageNumber = 1, pageSize = 100, searchKeyword = '' }) => {
        const qs = stringify(
          {
            pageNumber,
            pageSize,
            searchKeyword,
          },
          { addQueryPrefix: true },
        );
    
        return {
          content: [
            {
              type: 'text',
              text: JSON.stringify(await WebClient.get(`/api/v1/experiments${qs}`)),
            },
          ],
        };
      },
    );
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 mentions pagination and search functionality, which is helpful, but fails to address critical aspects like authentication needs, rate limits, error handling, or the format of returned data. This leaves significant gaps for an agent to understand how to interact with the tool effectively.

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 that front-loads key information (fetches, paginated list, search) without any wasted words. It's appropriately sized for the tool's complexity.

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 (3 parameters, no output schema, no annotations), the description is incomplete. It lacks details on return values, error cases, and full parameter documentation, making it insufficient for an agent to use the tool confidently without additional context.

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?

Schema description coverage is low at 33%, with only 'searchKeyword' documented. The description adds value by implying pagination and search, but it doesn't detail parameter semantics beyond what's in the schema, such as default behaviors or constraints. This meets the baseline for partial coverage but doesn't fully compensate for the gaps.

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 action ('fetches'), resource ('paginated list of A/B test experiments'), and functionality ('with search functionality'), making the purpose evident. However, it doesn't explicitly distinguish this tool from sibling tools like 'experiment-detail' or other list tools, which prevents 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, such as 'experiment-detail' for single experiments or other list tools for different resources. It lacks context on prerequisites or exclusions, leaving usage unclear.

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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