Skip to main content
Glama
databar-ai

Databar MCP Server

Official
by databar-ai

add_table_exporter

Add an exporter to a table by mapping exporter parameters to table columns or static values, enabling per-row data enrichment.

Instructions

Add an exporter (CRM/destination) to a table with a parameter-to-column mapping.

IMPORTANT — mapping format: Each key is an exporter parameter name. Each value is one of: • { "type": "mapping", "value": "" } — read value from a table column per row. Use the human-readable column name (e.g. "email"). The server accepts column names directly. • { "type": "simple", "value": "" } — pass the same hardcoded value for every row.

WORKFLOW:

  1. Call get_exporter_details to see the parameter names.

  2. Call get_table_columns to see available column names.

  3. Build the mapping using column names (not UUIDs).

  4. The returned exporter_id from this call is the TABLE-EXPORTER id — use it with run_table_exporter (NOT the original exporter_id).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_uuidYesThe UUID of the table
exporter_idYesThe exporter ID to add (from search_exporters or get_exporter_details)
mappingYesParameter-to-column mapping. Keys = exporter param names. Values = { type: "mapping", value: "column-name" } or { type: "simple", value: "static-value" }
launch_strategyNoWhen to trigger: 'run_on_click' (manual) or 'run_on_update' (auto on row change). Default: 'run_on_click'.
authorizationNoID of the API key / OAuth connection to use. Required for exporters that need user authorization. If omitted, the system auto-selects the first available key.
custom_body_templateNoCustom JSON body template. Column values are referenced via {column_internal_name} placeholders. When provided, mapping is ignored.
Behavior4/5

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

With no annotations, description discloses mapping format, column names vs UUIDs, custom_body_template overriding mapping, and authorization auto-select. Missing side effects or reversibility, but covers core behavior well.

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?

Description is well-structured with an 'IMPORTANT' section and workflow list. It is front-loaded but could be slightly more concise; overall efficient.

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

Completeness4/5

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

Given 6 params and no output schema, description explains mapping and workflow well. Lacks error handling and return value details, but enough for most use cases.

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?

Schema coverage is 100% but description adds value: explains mapping format in detail, clarifies that column names are human-readable, specifies authorization ID type, and notes that custom_body_template uses internal column names.

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?

Description clearly states 'Add an exporter (CRM/destination) to a table' with a specific verb and resource. It distinguishes from siblings like add_table_enrichment and add_table_waterfall by focusing on exporters.

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

Usage Guidelines5/5

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

Explicit workflow: call get_exporter_details, then get_table_columns, then build mapping. Also notes that returned exporter_id is for run_table_exporter, not the original. This is strong when-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/databar-ai/databar-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server