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Mrshahidali420

Google Search Console MCP Server

gsc_find_unindexed

Uncover which URLs are missing from Google's index and the reason for each. Uses sitemaps or stored URLs to sample, inspect, and report unindexed pages with actionable codes.

Instructions

Which of a property's URLs are not in Google's index, and why.

source chooses where candidate URLs come from: "sitemap" fetches and parses the property's registered sitemaps fresh, "store" uses only URLs already seen, "both" (the default) unions them.

limit caps how many URLs are INSPECTED, not how many are returned — inspection spends a daily budget, so a cap that only trimmed the output would pay full price for an answer it threw away. Which URLs a capped run reaches follows the store's url ordering (alphabetical), not staleness: a capped run is a sample, not a worst-first sweep. The result reports candidates_total, inspected and limited so you can tell a truncated answer from a complete one.

limit is not the only thing that can cut a run short. Inspection quota is per property and roughly eleven slots a day, so on any site larger than that the run reaches the gate and stops: inspected is what was handed to the API, checked is what actually reached it, and skipped_quota lists the URLs accounting for the difference. Report both numbers rather than inspected alone — a run that answered for three of forty URLs is not a survey of the property, and the remaining URLs are answerable tomorrow at no extra cost.

Only URLs whose last inspection is older than inspection_ttl_days are re-inspected; the rest are reported from their stored status. A second call the same day therefore costs no budget and still answers in full. "fresh": true on a row means only that THIS run did not inspect it — usually because it was within the TTL, but also when limit cut the run short before reaching it. It is not a promise that the stored status is within the TTL.

Each unindexed row carries reason (one of ten codes), action, submitting_helps and needs_site_access. Act on submitting_helps before calling gsc_request_indexing: submitting a 404, a redirect, a noindex, or a page Google crawled and declined wastes an unrecoverable quota slot.

URLs whose state this run did not establish are in undetermined, never in unindexed. Read each one's status before wording the answer: it separates two cases a reader acts on differently. "We looked and could not tell" covers a failed inspection and a result the burst re-verify pass could not confirm. "skipped_quota" is the other case and means the opposite: we never got to look, because the property's daily budget ran out first. That is not a problem with the URL, needs no investigation, and is answered by running again tomorrow — say so rather than reporting it as a fault.

A refusal is {"ok": false, "error": <code>, "fix": <what to do>}, plus status when Search Console refused the call and detail (an exception type name) when the failure was unexpected.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
siteYes
limitNo
sourceNoboth
Behavior5/5

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 disclosure. It richly discloses many non-obvious behaviors: `limit` caps inspected URLs not returned, quota is ~11 inspections per day per property, `fresh: true` only means this run didn't inspect the URL, and the distinction between `undetermined` and `skipped_quota`. It also details the error response format. This far exceeds typical tool descriptions.

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

Conciseness5/5

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

The description is long but every sentence earns its place by explaining a nuance or an edge case that affects the agent's decision-making. It is well-structured: starts with a one-sentence purpose, then parameter semantics, then behavioral caveats, then output and error formats. No filler or redundancy; the length is justified by the complexity of the tool's behavior.

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 return values and it does so comprehensively: `candidates_total`, `inspected`, `limited`, `fresh`, `reason`, `action`, `submitting_helps`, `undetermined`, `status`, `skipped_quota`, and the error response format. It also places the tool in context with sibling tools (e.g., `gsc_request_indexing`) and addresses quota/timing implications, making it complete for an AI agent to invoke and interpret results.

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. It explains `source` (sitemap vs store vs both) and `limit` (caps inspections, not output, affects sampling) in depth. Although `site` is not explicitly described, the description repeatedly references 'the property' and 'registered sitemaps', making it clear that `site` identifies the property. This is sufficient semantic clarification for all parameters.

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 line states precisely what the tool does: finding which URLs of a property are not in Google's index and why. It specifies the resource (property URLs) and the action (identifying unindexed ones), clearly distinguishing it from sibling tools like gsc_request_indexing (which requests indexing) and gsc_performance (which measures performance).

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 provides explicit guidance on using the tool's output: it advises acting on `submitting_helps` before calling `gsc_request_indexing` and discusses when submission is a waste. It also explains the `source` parameter choices and the meaning of `limit`, including when a run is a sample rather than a full survey. This directly addresses when to use the tool and how to interpret its results vs. alternatives.

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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