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nataliemona

Mode Analytics MCP Server

by nataliemona

create_report

Create a workspace-visible Mode Analytics report in a specified space, with optional initial SQL queries, then check queries_added and queries_failed.

Instructions

Create a new report in a space, optionally with initial SQL queries.

This is a write operation — it creates a report visible to the workspace. If any queries fail to add, the report is still created; inspect queries_added and queries_failed in the result.

Args: space_token: Space to create the report in (use list_spaces to find it). name: Report name. description: Optional report description. queries: Optional list of queries to add. Each dict should have: - raw_query (str): The SQL text. - data_source_id (int): Data source ID (use list_data_sources). - name (str, optional): Query name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
queriesNo
descriptionNo
space_tokenYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and mostly does so: it flags the operation as a write visible to the workspace and discloses the important partial-failure semantic (report is created even if queries fail, inspect queries_added/queries_failed). It omits permission/auth requirements and any idempotency or rate-limit behavior, so it is not exhaustive.

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 write/visibility warning and failure semantics are front-loaded before the Args block, and each parameter line is terse. Slightly verbose overall but no sentence is filler.

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?

For a create tool with an output schema present, the description gives what the schema cannot: the write side effects, partial-failure behavior, and full parameter semantics. The remaining gap is permissions/preconditions for creating in a workspace space.

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 description coverage is 0%, so the description must compensate, and it does: every one of the 4 parameters is documented, including the optionality of description and queries and the internal structure of each query dict (raw_query, data_source_id, name). This meaningfully exceeds what the bare schema conveys.

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 first sentence names a specific verb and resource ('Create a new report in a space') and adds a scope qualifier ('optionally with initial SQL queries'). The word 'new' cleanly separates it from the sibling update_report, so an agent can route without opening the schema.

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

Usage Guidelines4/5

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

It tells the agent how to satisfy the required inputs by pointing at siblings ('use list_spaces to find it', 'use list_data_sources'). It does not state when to prefer add_query over passing queries here, or any exclusion, so it stops short of explicit when/when-not routing.

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