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databar-ai

Databar MCP Server

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by databar-ai

add_table_enrichment

Add an enrichment to a table by mapping enrichment parameters to table columns, using static values or column references that resolve per row.

Instructions

Add an enrichment to a table with a parameter-to-column mapping.

IMPORTANT — mapping format: Each key is an enrichment 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. IMPORTANT: simple values can embed column references using {column_internal_name} syntax (e.g. "Find the industry of {column1}"). The internal_name for each column is shown by get_table_columns. At runtime, these placeholders are replaced with actual column values per row. You can also use human-readable column names (e.g. {Company Website}) — the server will auto-resolve them to internal names. Use {?column_name} to mark a column reference as optional (row won't fail if the column is empty).

WORKFLOW:

  1. Call get_enrichment_details to see the parameter names.

  2. Call get_table_columns to see available column names and their internal_names.

  3. Build the mapping using column names (not UUIDs). For text/textarea parameters that should incorporate column data, use "simple" type with {column_internal_name} placeholders in the value.

  4. The returned enrichment_id from this call is the TABLE-ENRICHMENT id — use it with run_table_enrichment (NOT the original enrichment_id).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_uuidYesThe UUID of the table
enrichment_idYesThe enrichment ID to add (from search_enrichments or get_enrichment_details)
mappingYesParameter-to-column mapping. Keys = enrichment param names. Values = { type: "mapping", value: "column-name" } or { type: "simple", value: "static-value" }. Simple values can embed {column_internal_name} placeholders (e.g. "Research {column1}") that are resolved per row at runtime. You can use human-readable column names in placeholders — the server resolves them.
launch_strategyNoWhen to trigger: 'run_on_click' (manual) or 'run_on_update' (auto on row change). Default: 'run_on_click'.
Behavior4/5

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

No annotations are provided, so the description carries full burden. It transparently explains the mapping types, how simple values embed column references using {column_internal_name} syntax, optional references with {?column_name}, and that the returned id is a table-enrichment id. It lacks explicit statements on destructive behavior or error handling, but overall is highly transparent.

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?

The description is fairly long but well-structured with bold headers for important notes and a numbered workflow. Every sentence adds value. Minor redundancy could be trimmed (e.g., repeating column reference info), but overall it's justified given the tool's complexity.

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 no output schema and no annotations, the description is comprehensive: it covers mapping format, workflow, references to related tools (get_enrichment_details, get_table_columns, run_table_enrichment), and explains the returned id. Missing details on error handling or default behavior for missing columns, but still very complete for this complexity level.

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

Parameters5/5

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

Schema coverage is 100%, so the baseline is 3. However, the description adds substantial meaning beyond the schema, especially for the mapping parameter, explaining the two value types, column reference syntax, and the workflow. It also clarifies that enrichment_id is for the enrichment to add, and the launch_strategy enum values.

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 'Add an enrichment to a table with a parameter-to-column mapping,' providing a specific verb, resource, and scope. It also distinguishes from sibling tools by referencing the enrichment workflow and pointing to run_table_enrichment.

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?

The description gives an explicit step-by-step workflow (call get_enrichment_details, get_table_columns, build mapping) and explains when to use this tool versus alternatives (e.g., the returned enrichment_id is for run_table_enrichment, not the original). It also provides detailed mapping format guidance.

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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