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Brawl Stars

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).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive. The description adds process details: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format' and 'Output is a single text blob ready to drop at site-root/llms.txt.' This goes beyond annotations to describe output format and intended placement.

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?

Three sentences: purpose, process/output, and use cases. No filler, front-loaded with the primary action, each sentence adds unique value.

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?

With 2 well-documented params, no output schema, and full annotations, the description sufficiently covers the input, process, output format, and common use cases. It even names the destination filename (site-root/llms.txt).

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 100% of parameters with descriptions (url, max_links with default/max). The description only mentions 'key links' in the output, which loosely relates to max_links, but adds no additional parameter-level detail beyond the schema. Baseline 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 clearly states 'Generate a production-ready llms.txt file for any URL' – specific verb (Generate), resource (llms.txt), and scope (any URL). It distinguishes from siblings like scan_competitor_ai_presence by focusing on file generation rather than visibility scanning.

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 lists 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.' This provides clear context for when to use. It doesn't mention alternatives or when not to use, but the context is sufficient.

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

C2.9/5.0
Disambiguation2/5

The set mixes a general data-querying platform (Pipeworx) with a small Brawl Stars API wrapper. Within the Pipeworx cluster, ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim have overlapping lookup behavior, and the five polymarket_* tools cover similar prediction-market ground. The Brawl Stars tools are distinct but dwarfed, making the overall purpose confusing.

Naming Consistency3/5

Most Pipeworx tools use snake_case verb_noun patterns (ask_pipeworx, validate_claim, list_subscriptions), but Brawl Stars tools are bare nouns (brawler, club, player) and memory tools are bare verbs (remember, recall, forget). Some names are compound (generate_llms_txt, scan_competitor_ai_presence). No consistent pattern spans the whole set, though each subset is internally coherent.

Tool Count2/5

41 tools is far beyond what a Brawl Stars server needs; only about 10 are Brawl Stars-related. The bulk is a general-purpose data and prediction-market toolkit that seems bolted on. The count is not well-scoped to the server's declared name and purpose.

Completeness2/5

For Brawl Stars, the surface is thin: player and club profiles exist but there is no player search, club search, brawler-specific per-player stats, or detailed leaderboards. The Pipeworx side has broad coverage but is unrelated to the server name, so the domain is muddled and obvious Brawl Stars endpoints are missing.

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