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jhliberty

Basecamp MCP Server

by jhliberty

get_cards

Retrieve all cards from a specific column in Basecamp by providing the project ID and column ID. This tool supports project management and task tracking through Basecamp integration.

Instructions

Get all cards in a column

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
column_idYesThe column ID
project_idYesThe project ID

Implementation Reference

  • MCP tool handler for 'get_cards': extracts arguments, calls client.getCards(), formats response as JSON text content.
    case 'get_cards': {
      const cards = await client.getCards(typedArgs.project_id, typedArgs.column_id);
      return {
        content: [{
          type: 'text',
          text: JSON.stringify({
            status: 'success',
            cards,
            count: cards.length
          }, null, 2)
        }]
      };
    }
  • Input schema defining required project_id and column_id parameters for the get_cards tool.
    inputSchema: {
      type: 'object',
      properties: {
        project_id: { type: 'string', description: 'The project ID' },
        column_id: { type: 'string', description: 'The column ID' },
      },
      required: ['project_id', 'column_id'],
    },
  • src/index.ts:200-211 (registration)
    Registration of the 'get_cards' tool in the MCP tools list, including name, description, and input schema.
    {
      name: 'get_cards',
      description: 'Get all cards in a column',
      inputSchema: {
        type: 'object',
        properties: {
          project_id: { type: 'string', description: 'The project ID' },
          column_id: { type: 'string', description: 'The column ID' },
        },
        required: ['project_id', 'column_id'],
      },
    },
  • Helper method in BasecampClient that performs the actual API call to retrieve cards from a specific column.
    async getCards(projectId: string, columnId: string): Promise<Card[]> {
      const response = await this.client.get(`/buckets/${projectId}/card_tables/lists/${columnId}/cards.json`);
      return response.data;
    }
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 states 'Get all cards' but does not clarify if this is a read-only operation, whether it requires authentication, how results are returned (e.g., pagination), or any rate limits. The description is minimal and lacks critical behavioral details for a tool with no annotation coverage.

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, direct sentence ('Get all cards in a column') with no unnecessary words. It is front-loaded and efficiently conveys the core action, making it highly concise and well-structured for quick understanding.

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 read operation with 2 required parameters), lack of annotations, and no output schema, the description is incomplete. It does not explain return values, error handling, or behavioral traits, leaving significant gaps for an AI agent to understand how to invoke and interpret results effectively.

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 parameter definitions for 'column_id' and 'project_id'. The description does not add any additional meaning beyond the schema, such as explaining relationships between parameters or usage examples. Since schema coverage is high, the baseline score of 3 is appropriate, as the description neither compensates nor detracts.

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 'Get all cards in a column' clearly states the verb ('Get') and resource ('cards in a column'), making the purpose understandable. However, it does not explicitly distinguish this tool from sibling tools like 'get_card' (singular) or 'get_card_steps', leaving some ambiguity about scope differentiation.

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 does not mention sibling tools like 'get_card' for single cards or 'global_search' for broader queries, nor does it specify prerequisites or exclusions, leaving usage context 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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