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MCP Datastore Server

by johnreitano

datastore_list_kinds

List all entity kinds (tables) in Google Cloud Datastore using this tool. Enables quick access to available data structures for query and management purposes.

Instructions

List all available entity kinds (tables) in the Datastore

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler for 'datastore_list_kinds': calls DatastoreClient.listKinds() and formats the response as text.
    case 'datastore_list_kinds':
      const kinds = await datastoreClient.listKinds();
      return {
        content: [
          {
            type: 'text',
            text: `Available entity kinds:\n${kinds.map((k: string) => `- ${k}`).join('\n')}`,
          },
        ],
      };
  • DatastoreClient.listKinds(): Queries the '__kind__' namespace to retrieve and return all available entity kinds.
    async listKinds(): Promise<string[]> {
      try {
        const query = this.datastore.createQuery('__kind__').select('__key__');
        const [entities] = await this.datastore.runQuery(query);
        
        return entities.map(entity => entity[this.datastore.KEY].name).filter(Boolean);
      } catch (error) {
        throw new Error(`Failed to list kinds: ${error instanceof Error ? error.message : 'Unknown error'}`);
      }
    }
  • src/index.ts:29-35 (registration)
    Tool registration in ListTools response: defines name, description, and empty input schema.
      name: 'datastore_list_kinds',
      description: 'List all available entity kinds (tables) in the Datastore',
      inputSchema: {
        type: 'object',
        properties: {},
      },
    },
  • Input schema for 'datastore_list_kinds' tool: empty object (no parameters required).
    inputSchema: {
      type: 'object',
      properties: {},
    },
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool lists kinds, but doesn't describe traits like whether it's read-only (implied but not explicit), performance characteristics, error handling, or output format. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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 the core purpose without any wasted words. It's appropriately sized for a simple tool with no parameters, making it easy to parse quickly.

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 simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate but incomplete. It covers the basic purpose but lacks details on behavioral traits and output format, which are important for an agent to use it correctly. It meets the minimum viable threshold but has clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description adds no parameter information, which is appropriate here. Baseline is 4 for zero parameters, as no compensation is needed for schema gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('List') and resource ('all available entity kinds (tables) in the Datastore'), distinguishing it from siblings like datastore_get (retrieve specific entities) and datastore_query (complex queries). It precisely communicates what the tool does without ambiguity.

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 implies usage for discovering available kinds/tables, but provides no explicit guidance on when to use this versus alternatives like datastore_query (which might list kinds indirectly) or prerequisites. It lacks clear when/when-not statements or named alternatives, leaving usage context inferred rather than 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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