Autario Data Analytics Platform

get_traction_overview

Read-onlyIdempotent

ADMIN/CURATOR ONLY. Fetch the autario traction overview | ONE report uniting the three real signal sources: real human reach (GA4-humans), the MCP/agent channel (mcp_tool_call volume + success-rate + top tools), the signup funnel (new signups, source/medium/trigger) and the activation half of it (charts created, charts published, publish failures, logins), plus the biggest drop-off in plain language, MCP-calls-per-dataset (what agents pull), top charts by views, top API endpoints (human-only), and per-app usage (web views vs MCP calls, Bubble Or Not explicit). Every page-view/funnel number is HUMAN-ONLY | own-pipeline renders (screenshot worker / chart-gen) and generic bots are classified out (bot_or_own, an excluded-count) and never inflate the headline. A separate llm_crawler section (total + by-crawler family + top pages) answers "do LLMs fetch the page content when they cite us?". 30-day window. Returns ONE JSON snapshot (cached, fast). Requires the connector to be OAuth-authorized as the autario curator account | any other caller gets a permission error. Use when asked "how is autario doing", "show traction", "what is the funnel", "which datasets do agents use", "how many signups", "do LLMs crawl us".

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior; the description adds substantial context beyond that: cached and fast, 30-day window, human-only metrics with own-pipeline and bot exclusion ('bot_or_own'), a separate llm_crawler section, and the OAuth permission error condition. No contradiction with annotations.

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 long but information-dense, and important facts are front-loaded: the access restriction and core purpose appear first. Each clause adds a distinct fact about metrics, exclusions, caching, timeframe, or usage triggers. The dense parentheticals and run-on feel slightly heavy, preventing a perfect score.

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

Completeness5/5

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

For a parameterless tool with no output schema, the description is exceptionally complete: it names the return sections and metrics, the 30-day window, caching behavior, human-only classification, crawler reporting, and authentication requirements. An agent can correctly select and invoke this tool without needing additional context.

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 zero parameters and schema coverage is 100%, so there are no parameter semantics for the description to clarify. The description compensates by explaining what the fixed snapshot contains, which is the relevant semantic context for this parameterless tool.

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 opens with a specific verb and resource, 'Fetch the autario traction overview', and then enumerates distinct metric blocks: human reach, MCP channel, signup funnel, activation, and LLM crawlers. This makes it clearly distinguishable from the other analytics siblings like audience_360 or seo_360.

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?

Provides explicit triggers: 'Use when asked "how is autario doing", "show traction", "what is the funnel"...' It also clearly states the access boundary: ADMIN/CURATOR ONLY, OAuth-authorized as autario curator, any other caller gets a permission error. It does not name sibling alternatives, but the use-case list and permission exclusion offer strong practical 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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TDQS

A3.9/5.0

Disambiguation4/5

Most tools are strongly domain-specific with clear boundaries, especially the 360 reports and dataset/chart CRUD tools. Some overlap exists around driver analysis (find_drivers, what_matters, decompose_drivers) and dataset discovery (search_datasets, discover_by_topic, list_indicators), but the descriptions make the intended use cases mostly distinguishable.

Naming Consistency4/5

The vast majority of tools follow a clear snake_case verb_noun or get_noun pattern, e.g. list_connectors, refresh_connector, query_dataset, delete_dataset. Minor deviations such as calculate, describe, bubble_or_not, what_matters, and the 360-style report names keep it from being perfectly uniform.

Tool Count2/5

48 tools is far beyond the 3-15 range and even beyond the 25-tool threshold for a heavy surface. The platform is broad and the tools are organized into domains, but the sheer number creates a high selection burden for an agent and suggests the server is trying to cover too many workflows in one toolset.

Completeness4/5

The toolset covers dataset lifecycle, chart lifecycle, data discovery, querying, statistics, app context, connectors, and admin reports remarkably well. Notable gaps are the lack of a delete_chart tool and no row-level update/delete for datasets, but agents can generally work around these or treat them as intentional platform constraints.

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