Nuqti — Ahrefs-alternative SEO data

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds material context beyond them: the data source (Nuqti link index) and the exact composition of the result, which matters because there is no output schema. It does not mention data freshness, coverage limits, or auth, keeping it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, both earning their place: the first is a dense but readable inventory of returned metrics, the second gives the usage trigger. Front-loaded with the resource, no padding.

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

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

With no output schema, the description carries the burden of explaining returns and does so by enumerating the metrics, and the one required parameter is fully documented. Missing are notes on freshness/coverage of the index estimate or any caveats about range interpretation.

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

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

Schema coverage is 100% and the single 'domain' parameter is already documented with an example ('example.com'), so the schema does the heavy lifting. The description's phrase 'of a website' only loosely reinforces the param and adds no format rules. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource (backlink profile of a website) and enumerates exactly what the summary contains: referring domain/link counts, whole-web estimate with range, dofollow/nofollow split, top anchors and TLD mix. It clearly distinguishes itself from a raw list like referring_domains by promising aggregates, though it never names that sibling explicitly.

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

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

Provides an explicit trigger phrasing ('Use for "how many backlinks does X have"'), which is a clear when-to-use signal. It stops short of stating when not to use it or pointing to alternatives such as referring_domains for the actual domain list.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description goes further by disclosing the data window (since 2018), sampling granularity (one point per quarter), and derived fields (gained/lost/net over 12 months plus trend) — substantial behavioral context beyond the annotations, though nothing on freshness/latency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences: the first defines the data and its resolution, the second gives the interpretive use. Nothing redundant, and the most decision-relevant facts (metric, coverage, granularity) are front-loaded.

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

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

With no output schema, the description carries the burden of explaining return content, and it does: history depth, point cadence, the headline metric, and the 12-month gained/lost/net summary. For a single-parameter read tool with annotations, nothing needed for correct invocation is missing.

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

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

Only one parameter with 100% schema description coverage (including an 'e.g. example.com' example), so the schema does the heavy lifting. The description adds no format, normalization, or subdomain handling detail, which is the baseline-3 outcome when coverage is high.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific metric (count of websites linking to a domain) and its temporal shape (multi-year history since 2018, quarterly points), which cleanly distinguishes it from siblings like referring_domains and backlinks_summary that report current snapshots. The verb+resource pair is unambiguous.

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

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

The closing clause 'Use to see if a site is growing or losing authority' gives an implied use case, but there is no explicit when-not guidance and no alternative tool named (e.g., use referring_domains for the current list). Adequate but leaves routing to inference.

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

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

Annotations already declare readOnlyHint and openWorldHint, so safety is covered. The description adds genuinely new behavior beyond that: the data is cached (freshness caveat) and free (no cost/quota concern), which affects how an agent should interpret and re-request results.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One dense sentence with the core deliverable front-loaded and the cost/freshness note at the end. The mid-sentence list of returned metrics is long but every item earns its place as return-value disclosure in the absence of an output schema.

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

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

With no output schema, the description compensates by enumerating the returned fields, and annotations cover the read-only safety profile. The only gap is the absence of any mention of the country parameter's effect on the returned traffic/keyword data.

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

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

Schema coverage is 100%, so both parameters are already documented in the schema. The description says 'any website' but never mentions the country/market parameter, so it adds no meaning beyond the schema — the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource and deliverable ('SEO snapshot of any website') and enumerates the actual outputs: Authority score, organic traffic, ranking keywords, competitors, backlinks. It distinguishes itself implicitly from keyword_overview by being domain-scoped, though it never names the sibling to make the contrast explicit.

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

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

Usage is only implied — an agent needing a domain-level SEO picture will infer this tool, and 'Free, cached data' hints that no quota is consumed. There is no statement of when to prefer this over keyword_overview or run_seo_tool, and no prerequisites.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds value beyond that by disclosing the return format ('full SEO data as text'), which matters since there is no output schema. It does not cover pagination or error conditions, but for a simple read that is a minor gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence with no wasted words; the action, the input source, and the return type are all front-loaded. Nothing could be removed without losing information.

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

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

For a one-parameter read tool with full schema coverage and annotations covering the safety profile, the description supplies purpose, input origin, and return format, which is sufficient to invoke it correctly. Only minor details (output shape specifics) are absent, and no output schema exists to carry them.

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

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

Schema description coverage is 100%, and the schema already documents the single 'id' parameter with an example format ('domain:example.com'). The description only echoes that the id comes from search, adding little beyond the schema, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action (open one result) and resource (a search result by id) and clarifies what comes back (full SEO data as text). It ties itself to the sibling 'search' as the source of the id, which meaningfully distinguishes it from the list/search tools, though it doesn't contrast against the other detail-oriented siblings like domain_overview.

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

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

It clearly places the tool in a workflow: use it on an id returned by search to expand a single result. There is no explicit 'when not to use this' or comparison against siblings such as domain_overview, but the prerequisite context is unambiguous.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety and external-data profile are covered. The description adds genuine context beyond that: the data source (what people really type into Google), the shape of returned items (matching terms and questions with volume and difficulty), and language support (English or Arabic). It stops short of pagination/rate-limit detail, but the added value is real.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose and output characteristics, ending with the follow-up action. No redundancy and nothing wasted.

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

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 carries the return-value burden and does so: it describes the two result types (terms and questions) and the metrics attached (volume, difficulty). Combined with full schema coverage and clear annotations, an agent has everything needed to call it correctly.

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

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

Schema coverage is 100%, so the schema already documents limit, country, and keyword, giving a baseline of 3. The description adds meaning the schema omits: the seed keyword may be English or Arabic, which tells the agent about accepted input values beyond the schema's generic 'Seed keyword' text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('keyword ideas for a seed keyword') and characterizes the output precisely: matching terms and questions from real Google queries, each with estimated monthly volume and difficulty. It distinguishes itself from the sibling keyword_overview by positioning ideas as the exploratory step that feeds into keyword_overview.

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

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

Explicitly names the context ('use for content planning') and the alternative plus the condition that routes to it ('then call keyword_overview on the best ones'). The agent knows both when to use this tool and what to do next.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds no caveats beyond the payload list — no mention of data freshness, credit/rate cost, or that results reflect live Google data. It is accurate and non-contradictory, but thin on traits the annotations do not supply.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler; every clause names a distinct returned metric. The dense comma list is slightly harder to scan than bullets would be, but nothing is wasted.

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

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

With no output schema and no output annotations, the description carries the return-value burden and does it thoroughly by enumerating the metric set, including the 0-100 difficulty scale and 12-month trend. It is only incomplete on market scoping and any limits on how many related keywords/PAA questions come back.

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

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

Schema description coverage is 50%: the country enum is documented in-schema, while keyword is undocumented but self-evident. The description never mentions the country/market parameter, so it does not compensate for the coverage gap — it only implies that a keyword is the input via the metric list.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource (keyword) and enumerates the exact metric set returned — search volume, difficulty, CPC, intent, trend, top-10 SERP, PAA, related keywords — so the agent knows precisely what it gets. It does not, however, contrast itself with the sibling domain_overview, which covers a superficially similar research surface.

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

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

There is no when-to-use, when-not-to-use, or alternative-routing guidance at all. The agent can infer that this is for keyword research rather than domain research, but nothing in the text confirms that inference or names a competing tool.

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

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds that each entry includes required inputs, which is useful given there is no output schema, but it says nothing about format, size, or whether results are static or dynamic.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that names the resource before the enumeration. The long parenthetical catalog is dense but every item earns its place as a description of coverage; the trailing ellipsis is slightly loose.

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

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

For a zero-parameter listing tool with no output schema and only a readOnly annotation, the description covers what is returned (tool names plus required inputs) at an adequate level. Only the exact return structure remains unspecified, which is a minor gap.

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

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

The tool takes zero parameters, which is the baseline-4 case. The description notes that listed tools come 'with the input each one needs,' which is a small bonus but there are no parameters to document.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Lists) and resource (Nuqti's free technical tools) and enumerates the tool categories it covers, so an agent knows exactly what it returns. It does not explicitly differentiate itself from siblings like run_seo_tool, though the catalog-vs-execute distinction is implied.

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

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

The phrase 'with the input each one needs' hints that this is a discovery step preceding run_seo_tool, but there is no explicit when-to-use statement or naming of the alternative. Usage is only implied.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds genuine behavioral context beyond that: results are ordered 'strongest first' and each row carries authority, link counts, dofollow counts, and first/last seen dates, which tells the agent what it will receive without an output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no filler, with the core definition front-loaded and the use case trailing. Slightly dense in the first sentence's field enumeration, but every element earns its place.

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

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

With no output schema, the description usefully enumerates the returned fields and their ordering, which is exactly the gap it needs to fill. It stops short of describing pagination limits or behavior at large result sets, but that is a minor omission for a simple two-parameter read tool.

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

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

Schema coverage is 100% for both parameters, so the schema already documents 'domain' and the 1-100 limit. The description adds no new syntax or constraint detail beyond the ordering hint; baseline 3 is correct when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('list of websites that link to a domain') and names the canonical term 'referring domains' in parentheses, so the tool's identity is unambiguous. However, it never distinguishes itself from the highly confusable sibling backlinks_summary, leaving the agent to infer the boundary.

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

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

'Use for link-building research and competitor backlink analysis' gives an implied scenario, but there is no when-not guidance and no alternative named despite backlinks_summary and domain_overview being obvious candidates for the same intent. Usage is suggested rather than routed.

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

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds only that it 'Returns the full JSON result' — no output schema exists, so this is the sole return-value clue, and it is vague about structure, size, or failure modes (e.g. invalid slug, network errors).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the action and the slug source. The inline slug list is long but functions as useful illustration; it is not wasted space given the 40-value enum.

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

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

For a read-only, open-world dispatcher over 40 tools with no output schema, the definition covers the essential flow (fetch slugs, then run one). The gap is that the single return-value sentence is too thin to set expectations about JSON shape or per-tool differences.

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

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

At 67% schema description coverage the schema already documents q and q2. The description's slug examples hint at what q should be for each tool but never maps them to q vs q2, so it adds only marginal meaning beyond the schema and the enum list.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Run one free Nuqti tool by slug') and names the sibling that provides the needed input ('get slugs from list_seo_tools'). An agent can distinguish this from list_seo_tools/domain_overview/keyword_overview 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.

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

Explicitly tells the agent where to obtain valid slugs and constrains to one tool per call, which is real routing guidance. It does not state when NOT to use it or when a dedicated sibling (e.g. domain_overview) is preferable to the corresponding slug.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful behavioral context not in the schema or annotations: the tool returns result *ids* rather than full data, and those ids are meant to be opened with fetch. No permission or rate-limit details, but for a read-only search that is minor.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with no filler, front-loading what the tool is and how to use it, then the return-handoff to fetch. Every clause earns its place.

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

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

With no output schema, the description correctly compensates by explaining the return shape (result ids) and the next step (fetch). For a one-parameter read-only search that is close to complete, though it omits any note on result volume or how to refine an ambiguous query.

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

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

Schema coverage is 100%, so the single 'query' parameter is already documented as 'A domain or a keyword'. The description paraphrases this and adds a format example ('example.com'), which is a marginal gain. Baseline 3 is appropriate when the schema does the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Search Nuqti's SEO data') and clarifies the two input modes (domain or keyword). It also hints at its place in the sibling workflow by saying it 'returns result ids to open with fetch', but it does not explicitly differentiate itself from domain_overview or keyword_overview.

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

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

Gives clear context for use: supply a domain or a keyword, then hand the returned ids to fetch. This establishes the search-then-fetch workflow. It stops short of any exclusion guidance (e.g., when to prefer the more specific domain_overview or keyword_ideas tools).

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