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Google_search_console

by io.github.pipeworx-io

Generate llms.txt

generate_llms_txt
Read-onlyIdempotent

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL of the site to summarize, e.g. "https://example.com" or a specific landing page.
max_linksNoMaximum number of link entries to include (default 25, max 50).

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds transparency by explaining the extraction process (fetches page, extracts title/description/key links) and output format, which goes beyond annotations without contradicting them.

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 tightly written with two concise paragraphs: the first explains functionality and output format, the second lists use cases. No extraneous words, and the core action is front-loaded in the first sentence.

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 low complexity (2 parameters, no output schema), the description fully covers necessary context: what it does, what it produces, and common use cases. No gaps remain for an agent to select and invoke the tool correctly.

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

Parameters3/5

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

Schema description coverage is 100%, with both 'url' and 'max_links' having clear descriptions. The tool description adds no additional parameter context beyond what the schema provides, so a baseline score of 3 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 specific verb 'Generate' and resource 'llms.txt file' for any URL, distinguishing it from sibling tools like 'scan_competitor_ai_presence' which focus on auditing rather than file generation. The enumerated use cases further clarify its purpose.

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 explicit use case scenarios (e.g., getting a client's site indexed, drafting for own project, auditing a competitor), giving clear context for when to use. However, it does not mention when not to use or suggest alternative tools.

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

A3.5/5.0
Disambiguation2/5

The set has several overlapping clusters: ask_pipeworx and ask_pipeworx_beta are explicitly identical, multiple polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) occupy the same prediction-market space, and ai_visibility_check/scan_competitor_ai_presence are near-duplicates. A few tools (memory triad, gsc_* calls) are crisp, but the boundaries between the meta-research tools (ask_pipeworx, deep_research, discover_tools, validate_claim) are not obvious enough to prevent misselection.

Naming Consistency2/5

Naming is a mixed bag: some tools follow snake_case verb_noun (gsc_list_sites, resolve_entity, search_within), others are lowercased concatenations (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded), and several are bare nouns or adjectives (recent_alerts, forget, recall, process). There is no consistent verb style or separator convention across the set.

Tool Count1/5

35 tools is already heavy, but the bigger problem is that only 4 of them (gsc_list_sites, gsc_list_sitemaps, gsc_inspect_url, gsc_search_analytics) relate to the server's stated Google Search Console purpose. The remaining 31 are a sprawling Pipeworx data/prediction-market/memory toolkit, making the count wildly disproportionate to the apparent scope.

Completeness1/5

For a Google Search Console server, the surface is severely incomplete: it can list sites/sitemaps, inspect URLs, and query analytics, but lacks sitemap submission, property add/remove, URL removal/access control, and other core GSC operations. Conversely, the 31 off-domain tools make the domain itself incoherent — an agent cannot tell what this server is actually for.

Resources