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Mrshahidali420

Google Search Console MCP Server

gsc_doctor

Diagnose whether your Google Search Console MCP is correctly configured by running seven checks on OAuth, token, config, store, properties, browser, and extension, with fixes for each failure.

Instructions

Diagnose whether gsc-mcp is set up to talk to Search Console.

Runs seven checks in order — oauth_client, token, config, store, properties, browser, extension — and reports all of them even if one raises. A check that raises is recorded as ok: False with the exception's TYPE NAME only in detail; the message is never included, because it can carry a bearer token, a credentialed URL, a raw response body, or a filesystem path holding your account name. Every failing check carries a non-empty fix string with a concrete next step; this tool diagnoses, it does not repair anything itself.

The last two are the local setup for browser-driven submission, and they come last because they cost nothing and the first five establish whether anything works at all. browser names the profile to use; extension reports whether the bridge extension is REGISTERED in that profile — a green check does not mean its background worker is running, which needs a live connection and arrives with Milestone 3B. "Could not be checked" is reported as such, never as "not installed".

Costs at most one Search Console API call (sites.list, for the properties check); the browser and extension checks are local file reads and make none.

Returns {"ok": bool, "checks": [{"name", "ok", "detail", "fix"}, ...]}; ok is true only when every check passed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description carries the full burden, and it excels: it reveals that error messages are deliberately omitted for security, that all checks run even if one fails, that 'could not be checked' is distinct from 'not installed', and that at most one API call is made. It also discloses the exact return contract.

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 front-loaded with a one-sentence purpose, then organized into focused paragraphs covering checks, security, order rationale, cost, and return value. Every sentence adds substantive detail without redundancy.

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?

Given the tool's complexity and the absence of an output schema, the description is remarkably complete. It explains the seven checks, failure handling, security constraints, fix strings, local vs API costs, ordering rationale, and the exact return shape — leaving little ambiguity for an 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 tool has zero parameters, and the input schema has 100% coverage by being empty. Per the baseline rule for parameterless tools, this scores 4; the description adds no parameter-specific meaning because none is needed.

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 opens with a specific verb and resource: 'Diagnose whether gsc-mcp is set up to talk to Search Console.' It further clarifies the tool's scope by listing the seven checks, clearly distinguishing it from sibling tools like gsc_setup or gsc_check_status.

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?

It gives clear context for when to use the tool — to verify setup health — and explicitly states it only diagnoses, not repairs. However, it does not name specific alternatives or state when not to use it, stopping short of a full 5.

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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