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gemini-dk

Firebase MCP Server

by gemini-dk

firestore_add_document

Add a document to a Firestore collection by specifying the collection name and document data. This tool enables structured data storage in Firebase's NoSQL database.

Instructions

Add a document to a Firestore collection

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
collectionYesCollection name
dataYesDocument data

Implementation Reference

  • Executes the firestore_add_document tool: adds a document to Firestore collection and returns ID, console URL, and processed data.
    export async function addDocument(collection: string, data: any) {
      try {
        if (!db) {
          return { content: [{ type: 'text', text: 'Firebase is not initialized. SERVICE_ACCOUNT_KEY_PATH environment variable is required.' }], isError: true };
        }
        
        const docRef = await db.collection(collection).add(data);
        const projectId = getProjectId();
        convertTimestampsToISO(data);
        const consoleUrl = `https://console.firebase.google.com/project/${projectId}/firestore/data/${collection}/${docRef.id}`;
        return { content: [{ type: 'text', text: JSON.stringify({ id: docRef.id, url: consoleUrl, document: data }) }] };
      } catch (error) {
        return { content: [{ type: 'text', text: `Error adding document: ${(error as Error).message}` }], isError: true };
      }
    }
  • Input schema for firestore_add_document tool defining collection and data parameters.
    inputSchema: {
      type: 'object',
      properties: {
        collection: {
          type: 'string',
          description: 'Collection name'
        },
        data: {
          type: 'object',
          description: 'Document data'
        }
      },
      required: ['collection', 'data']
    }
  • src/index.ts:37-54 (registration)
    Registration of firestore_add_document tool in ListToolsRequestSchema handler.
    {
      name: 'firestore_add_document',
      description: 'Add a document to a Firestore collection',
      inputSchema: {
        type: 'object',
        properties: {
          collection: {
            type: 'string',
            description: 'Collection name'
          },
          data: {
            type: 'object',
            description: 'Document data'
          }
        },
        required: ['collection', 'data']
      }
    },
  • src/index.ts:229-230 (registration)
    Dispatch to handler in CallToolRequestSchema switch statement.
    case 'firestore_add_document':
      return addDocument(args.collection as string, args.data as object);
  • Helper utility to convert Firestore Timestamps to ISO strings, used in addDocument.
    function convertTimestampsToISO(data: any) {
      for (const key in data) {
        if (data[key] instanceof Timestamp) {
          data[key] = data[key].toDate().toISOString();
        }
      }
      return data;
    }
Behavior2/5

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

With no annotations provided, the description carries full burden but only states the basic action without disclosing behavioral traits. It doesn't mention permissions required, error handling, whether the operation is idempotent, or what happens on success/failure, leaving significant gaps for a mutation tool.

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, clear sentence with zero wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly without unnecessary elaboration.

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?

For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on return values, error conditions, or behavioral context, which are crucial for safe and effective use in an AI agent workflow.

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 100%, so the schema already documents both parameters (collection and data). The description doesn't add any meaning beyond this, such as format examples or constraints, but meets the baseline since the schema does the heavy lifting.

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 ('Add') and resource ('document to a Firestore collection'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like firestore_update_document or firestore_delete_document, 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?

No guidance is provided about when to use this tool versus alternatives like firestore_update_document or firestore_get_document. The description lacks context about prerequisites (e.g., collection existence) or typical use cases, offering minimal usage direction.

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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