Skip to main content
Glama

Gdacs

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?

Beyond the annotations (readOnlyHint, idempotentHint), the description adds valuable behavioral context: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' It also clarifies the output is a 'single text blob,' giving agents a clear expectation of the result. No contradiction with annotations.

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 three sentences, each earning its place: the first states the core purpose, the second outlines the process, and the third lists concrete use cases. Information is front-loaded and concise with no filler.

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 simplicity (2 fully-described parameters, strong annotations, straightforward output), the description is complete. It provides enough detail for an agent to select and invoke the tool correctly, including the output format and typical use cases. No additional information is needed.

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 schema already provides 100% description coverage for both parameters (url and max_links). The description adds context about what is extracted (title/description/key links) but does not meaningfully enhance the parameter semantics beyond the schema. Baseline 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 tool's function with a specific verb and resource: 'Generate a production-ready llms.txt file for any URL.' It distinguishes itself from siblings like scan_competitor_ai_presence by emphasizing the generation of the llms.txt file itself, not just analysis.

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 cases: '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.' It gives clear context for when to use the tool, but it does not explicitly name alternative tools or state when not to use it.

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

All Gdacs 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

A3.5/5.0
Disambiguation4/5

Most tools have clear, distinct purposes, but the three ask_pipeworx variants (especially ask_pipeworx_beta, identical to ask_pipeworx) and the six Polymarket tools overlap conceptually and could cause misselection. Detailed descriptions largely compensate, but the boundaries between some meta-tools (e.g., ask_pipeworx vs deep_research vs bet_research) require careful reading.

Naming Consistency3/5

All names are snake_case, but conventions are mixed: verb-first (ask_pipeworx, compare_entities, discover_tools), noun-first compounds (entity_profile, polymarket_arbitrage), and single-word nouns (event, events, rss). The pattern is predictable for common actions but inconsistent across the set, making it harder to guess names for related tasks.

Tool Count2/5

At 35 tools, the count is heavy and the server named 'Gdacs' includes many unrelated tools (Polymarket, npm scanning, AI visibility), indicating scope creep. Several tools could be consolidated (e.g., the ask_pipeworx family and multiple pattern-market scanners), and the breadth dilutes the disaster-alerting focus implied by the server name.

Completeness4/5

The surface covers core workflows: data querying (ask/research/validate), entity profiling, prediction-market analysis (arbitrage/edges/fill risk), and subscription management (create/list/cancel). Minor gaps exist, such as no direct fetch tool for a specific Pipeworx pack and limited GDACS event management (only read operations), but these are workable with the provided meta-tools.

Resources