ikeytz – Schlüsseldienst Ludwigsburg

Get Llms Txt

get_llms_txt

Read-only

WHAT: Fetch https://www.ikeytz.com/llms.txt (short AI landmap). Same engine as get_discovery(which=llms). summary = file window. USE as the first discovery read. Full dump: get_discovery(which=llms-full). MCP catalog: get_llms_mcp_server.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of lines to return after offset. Omit or null = return every remaining line in the fetch window (not the whole disk file if the fetch itself truncated). Clamped to 1..10000. For sitemap.txt (~807 URL lines) omit limit to get the full list. For llms-keywords.txt prefer a window; the file is huge.
`offset`	No	0-based line index into the fetched UTF-8 file (split on \n). 0 = first line. Omit = 0. Combined with limit = a sliding window. Use nextOffset from the previous result to page. Does not count bytes; one line can be a long URL.

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	Yes	true = use summary, canonicalUrl, and extra keys. false = do not invent data; read error + hint and retry with valid args or open hint.
`hint`	No	Present when ok=false. Absolute URL the agent should open or pass to get_ai_page / get_discovery.
`path`	No	Public path on www, e.g. /llms.txt or /.well-known/ai.txt
`tool`	Yes	Echo of the tool name that produced this object (e.g. get_service_area).
`error`	No	Present when ok=false. Codes include: unknown_tool, unknown_place, unknown_auswahl, unknown_ratgeber, unknown_doc, unknown_discovery, faq_not_found, wissen_not_found, serp_not_found, q_required, bad_datetime, bad_when_key, plus HTTP errors from fetch.
`limit`	No	Applied line limit, or null if the caller omitted limit.
`which`	No	Resolved discovery id after alias fold (llms, sitemap-txt, ai-catalog, …).
`locale`	No	Locale actually used for URLs (args.locale or de).
`offset`	No	Applied 0-based line offset.
`related`	No	Always includes https://www.ikeytz.com/llms.txt. Geo tools also include https://maps.ikeytz.com/llms.txt.
`summary`	No	Primary text for the model. For discovery tools this is the file window (possibly thousands of characters). For list tools a compact one-liner. Quote with attribution.
`totalUrls`	No	http(s) lines in the fetched text.
`truncated`	No	true if fetch hit a byte cap OR offset+limit left more lines.
`nextOffset`	No	offset + returnedLines when more lines remain; else null. Pass as the next offset.
`totalLines`	No	Line count of the fetched text (after byte cap).
`attribution`	Yes	Required citation: 'Quelle: Schlüsseldienst Ludwigsburg ikeytz (ikeytz.com) · ai-train=no · https://www.ikeytz.com/.well-known/ai.txt'
`canonicalUrl`	No	Best URL to show the user: https://www.ikeytz.com… page, or tel:+49…, mailto:…, https://wa.me/…. Prefer this over constructing URLs.
`returnedUrls`	No	Lines in this window that start with http(s)://
`bytesReturned`	No	UTF-8 byte length of the returned body window.
`returnedLines`	No	How many lines are in summary/body this call.

TDQS

A4.5/5.0

Behavior4/5

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

The readOnlyHint annotation already signals a safe read operation, and the description adds useful behavioral context: the fetched file is a 'short AI landmap,' the result is a 'file window,' and the full dump is available elsewhere. This goes beyond the annotation by clarifying scope and related behaviors.

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 compact and front-loaded with the WHAT, followed by usage guidance and alternatives. Every sentence adds value and none are wasted, making it easy for an agent to parse quickly.

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?

The tool is simple, has zero required parameters, full schema coverage, an output schema, and a read-only annotation. The description supplies the missing contextual information: when to use it, what it returns at a high level, and which related tools to choose for other needs.

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?

The input schema covers both parameters fully with detailed descriptions, clamping, defaults, and paging semantics. The description adds no additional parameter-level meaning beyond calling the content a 'short AI landmap,' so the baseline score of 3 applies.

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 states a specific verb and resource: 'Fetch https://www.ikeytz.com/llms.txt (short AI landmap).' It clearly differentiates itself from siblings by naming the same engine as get_discovery(which=llms) and contrasting with the full dump get_discovery(which=llms-full) and the MCP catalog get_llms_mcp_server.

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?

It explicitly says 'USE as the first discovery read,' which tells the agent when to invoke this tool. It also names the alternative tools for full dumps and MCP catalogs, giving the agent a clear routing decision without needing to inspect other schemas.

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

A3.9/5.0

Disambiguation3/5

Many tools have clearly distinct roles, but there is real overlap: get_discovery duplicates get_llms_txt/get_llms_mcp_server/get_sitemap_txt, get_it and get_mcp_hub describe the same machines, and get_page_summary is a stub that competes with get_ai_page/get_serp_snippet. The detailed descriptions help, but an agent could still misselect among the discovery and pointer tools.

Naming Consistency4/5

The overwhelming majority follow a consistent snake_case verb-noun pattern: get_*, list_*, compose_*, resolve_*. Minor deviations like site_overview, get_it, and find_by_keyword are readable but break the dominant convention.

Tool Count2/5

45 tools for a single small-business website is excessive and well above the 25+ threshold. Many tools are pointer/list aliases or discovery-file accessors that could be consolidated without losing capability.

Completeness4/5

Core workflows are well covered: identity, contact, emergency info, pricing, invoicing, service areas, FAQs, legal pages, and generic page content via get_ai_page. Minor gaps exist around explicit opening-hours details, existing customer reviews, and maps-domain functionality, but these are mostly declared out of scope rather than dead ends.

Resources

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