Skip to main content
Glama

Digitalnz

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 declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds that it fetches the page, extracts title/description/key links, and emits standard llms.txt format, which is useful context beyond annotations. However, it does not mention potential rate limits or error handling for unreachable URLs.

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 two sentences, front-loading the core action and output format, followed by concise use-case examples. Every sentence adds value with no redundancy or fluff.

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 has 2 parameters, 100% schema coverage, and no output schema, the description sufficiently covers the output format ('standard llms.txt markdown format', 'single text blob') and its behavioral intent. No gaps are apparent.

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 coverage is 100% with clear parameter descriptions for url and max_links. The description does not add additional semantic info about the parameters 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 explicitly states the tool generates a production-ready llms.txt file for a URL, with specific use cases like indexing a client's site or auditing a competitor. This clearly distinguishes it from sibling tools, none of which directly perform this function.

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 gives clear positive use cases (getting a client's site indexed, drafting for own project, auditing competitor) but does not explicitly mention when not to use the tool or suggest alternatives like scan_competitor_ai_presence or ai_visibility_check. This is adequate but lacks exclusions.

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

All Digitalnz toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation2/5

Several tools are difficult to distinguish: ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx_grounded, deep_research, and ask_pipeworx have fuzzy boundaries. The polymarket_* family plus bet_research also overlap heavily, requiring agents to carefully parse long descriptions to avoid misselection.

Naming Consistency4/5

Naming is predominantly snake_case with a verb-first pattern (ask_, search, subscribe, unsubscribe, list_) and clear prefix families like polymarket_ and pipeworx_. Minor deviations like ai_visibility_check and entity_profile use noun-first phrasing, but the overall pattern is still predictable and readable.

Tool Count2/5

33 tools is heavy for any single server, and the count is especially inappropriate given the server is named Digitalnz but only two tools (search, record) serve that domain. The rest form an unrelated grab-bag of data research, prediction-market, AI-visibility, memory, and utility tools.

Completeness3/5

The research workflow is fairly well covered: ask/grounded/deep modes, entity resolution, comparisons, claim validation, subscriptions, and alerts all exist. However, the DigitalNZ surface is nearly absent—just search and record—which is a significant gap for the declared server name, while other domains like AI visibility and npm dependencies are isolated one-offs.

Resources