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serpmantics

by io.github.AmauryDvl

create_guides

create_guides

Create new guides

Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional source field (default google).

How to request each guide type:

  1. Google SERP guide (1 credit per guide): omit source, or pass source: "google". Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: source: "google_ai_overview" builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; source: "google_ai_overview_citations" builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"}

  2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. source: "chatgpt". The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"}

  3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the _citations suffix, e.g. source: "chatgpt_citations". The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"}

Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer <engine>_citations: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (<engine> alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick <engine>_citations.

The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langYesLanguage code for the guides
sourceNoTarget engine AND analysis mode the guide optimizes for. One guide = one source. `google` analyzes the Google SERP (1 credit per guide). Google's AI answers have the same two modes as AI engines : `google_ai_overview` analyzes the TEXT of the AI answers (AI Overview, completed with AI Mode) ; `google_ai_overview_citations` analyzes the content of the web SOURCES those answers cite, the recommended mode for GEO. 1 credit per guide in both modes, same language/country parameters as `google`. For AI engines (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek), pick the mode: `<engine>` analyzes the AI's generated ANSWER for the query; `<engine>_citations` analyzes the content of the web SOURCES the AI cites, which is the recommended mode for GEO (become one of the cited sources). Both AI modes cost 4 credits per guide. Omit for the default `google`.
queriesYesArray of queries to create guides for

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral transparency. It explains credit costs per guide type, the mapping of sources to analysis modes, and the effect of each mode. However, it does not describe what the tool returns after creation (e.g., guide IDs or objects), which is a notable omission for a creation tool.

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 appropriately sized given the complexity of the tool (multiple source modes, credit costs, recommendations). It is well-structured with numbered steps, examples, and a dedicated recommendation section. Every sentence adds value, and the structure aids readability.

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

Completeness3/5

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

The description covers the tool's purpose, parameters, and usage thoroughly, but lacks information about the return value or error handling. With no output schema and no annotations, the description should have addressed what the user can expect after creation (e.g., a list of guide IDs). This gap reduces completeness.

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 input schema has 100% description coverage, so the baseline is 3. The description adds significant value beyond the schema, especially for the 'source' parameter, with examples, credit costs, and recommendations for each enum value. It also clarifies default behavior when 'source' is omitted. This extra context justifies a higher score.

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 creates new guides based on queries, with specific verb 'create' and resource 'guides'. It distinguishes between different guide types (Google SERP, Google AI Overview, LLM Answer, LLM Citations) and explicitly notes each guide targets exactly one engine and analysis mode. This differentiates it from sibling tools like create_eeat or create_intent, which focus on different entities.

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?

The description provides detailed instructions on when to use each source value with examples, including a recommendation section ('Which AI mode to pick?'). It explains that to optimize for multiple engines, one guide per source should be created. However, it does not explicitly compare this tool to sibling tools like create_outline or create_meta, but their purposes are distinct enough.

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

B3.4/5.0
Disambiguation4/5

Most tools target distinct resources (guides, eeat, intent, internal_links, meta, outline, score, credits, tokens). However, some pairs like create_eeat and create_eeat_competitors are very similar and could cause confusion, though their descriptions clarify the difference.

Naming Consistency4/5

Tools consistently use a verb_noun pattern (create_, get_, delete_). Minor inconsistencies include 'get_aissistant_tokens' (typo) and 'get_credit_ledger' (singular vs plural), but overall the pattern is clear.

Tool Count4/5

28 tools is on the higher end but justifiable for a comprehensive SEO analysis platform covering guides, multiple AI features, EEAT, credits, and tokens. A few tools could be merged (e.g., get_credits and get_aissistant_tokens), but the count is still reasonable.

Completeness4/5

The tool surface covers CRUD for guides and most AI features, plus token/credit management. A notable gap is the lack of a get_score tool after create_score, and some features like updating EEAT analysis are absent. Overall, the core workflows are supported.

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