Autario Data Analytics Platform

seo_360

Read-onlyIdempotent

SEO 360 | the caller's OWN deterministic Search Console ACTION report, computed server-side from their connected GSC data (the exact numbers the user sees in the app | nothing re-derived, nothing estimated). The unit is the (query, page) pair and EVERY row ends in a concrete action, so this is the tool to call when a user asks "what should I write next", "which page should I fix first", "where am I losing clicks", "how are my rankings developing", "which pages are decaying", "how are my Core Web Vitals", "what technical SEO issues does my site have". Sections: page2_gaps (position 8-20 pairs ranked by potential click gain toward the top 3 | the core write-or-improve list), ctr_underperformers (ranks top-10 but the snippet loses the click | title/description work), orphan_demand (queries with demand whose best page is not about them | the page is missing, write it), cannibalization (one query split across pages | consolidate or differentiate), trends (click winners/losers AND position winners/losers vs the previous window, honestly flagged when the previous window is incomplete), rank_tracking (position series of the top + pinned queries with current vs 7d/28d deltas, ranking distribution Top3/4-10/11-20/21+, share-of-voice index, brand vs generic split), decay (the refresh queue: pages losing clicks across consecutive windows, ranked by lost clicks, with an optional EUR translation from the user's own click-value setting), vitals (Core Web Vitals p75 field data from the Chrome UX Report for the top pages, pass/fail per LCP/INP/CLS), audit (bounded own-site crawl snapshot: broken links, redirect chains, title/description issues, noindex/canonical conflicts, orphan pages, new-vs-fixed diff, internal-link opportunities), health (data coverage, staleness, which CTR-benchmark source applies). The CTR benchmark is the median of the caller's OWN data per position bucket, with a documented default curve as fallback per thin bucket. Deeper than audience_360 (which answers "who comes from where"): this one prescribes the next SEO action. Requires the caller's own autario account (API key or OAuth) with a Search Console connection | see get_app_context("seo-360").

Input Schema

TableJSON Schema

Name	Required	Description
`days`	No	Analysis window in days (7-90, default 28), anchored at the newest day of the caller's data. The trend comparison uses the same-length window before it.
`format`	No	Output wire format for this MCP call. Default 'toon' (Token-Oriented Notation, fewest tokens, best for tabular rows). 'compact' = minified JSON. 'json' = pretty JSON for readability. The REST API always returns JSON regardless.
`sections`	No	Which report sections to return. Default ["page2_gaps","health"]. Request only what the question needs (token efficiency); call again for more.

TDQS

A4.8/5.0

Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses that results are computed server-side from connected GSC data, are deterministic, and match the exact numbers in the app. It also notes transparency about incomplete previous windows and bounded crawl scope, plus authentication requirements and a pointer to get_app_context('seo-360'). This adds significant behavioral context.

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 lengthy but well-structured, starting with a core summary and then enumerating sections with parenthetical explanations. Each sentence provides useful detail for a complex tool; however, the length is at the upper limit and could be trimmed without losing much. Therefore a 4.

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?

There is no output schema, so the description must explain what the tool returns. It does this thoroughly by detailing each of the 10 sections and their content, along with data sources and requirements. It covers edge cases (incomplete windows) and authentication, making it complete for an AI agent.

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 schema covers all three parameters with descriptions, so the baseline is 3. The description further explains what each report section contains (e.g., page2_gaps as 'position 8-20 pairs ranked by potential click gain'), which helps in selecting 'sections' values. This adds meaning beyond the bare schema, so a 4 is appropriate.

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 the tool produces a deterministic Search Console action report from the caller's own GSC data, with a specific unit (query, page) and concrete actions. It also distinguishes itself from sibling audience_360, stating 'Deeper than audience_360' and listing specific use-case questions. This makes the purpose unambiguous and differentiates it from siblings.

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 explicitly lists when to use this tool: 'this is the tool to call when a user asks...' for several SEO action questions. It also contrasts with audience_360, indicating which tool answers traffic-source questions. It adds a prerequisite for using the tool (autario account with Search Console connection), which is useful 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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