Skip to main content
Glama

Steam

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 readOnly, openWorld, and idempotent hints. The description adds valuable process details: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format' and describes the output as a 'single text blob'. This goes beyond the basic safety profile, though it doesn't discuss rate limits or error handling.

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 of core content plus a use case list, all front-loaded and factual. Every sentence serves a purpose—what, how, output, and when to use—without redundancy or 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?

For a moderate-complexity tool with two well-documented parameters and no output schema, the description covers purpose, process, output type, and use cases. It clearly explains the output format ('standard llms.txt markdown format', 'single text blob'), making it sufficiently complete for an agent to select and invoke it 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' fully documented. The description doesn't add parameter-specific information beyond what the schema already provides, so the baseline 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 a specific verb ('Generate'), resource ('llms.txt file'), and scope ('for any URL'), making the tool's purpose unmistakable. While it doesn't explicitly name sibling tools, the function is so distinct that differentiation is implicit.

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 clear context by listing three concrete 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'). However, it doesn't explicitly mention when not to use this tool or suggest alternatives, so it falls just short of a 5.

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

All Steam 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

C2.9/5.0
Disambiguation2/5

Many tools overlap significantly: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants, while deep_research, discover_tools, and suggest_questions all serve meta/onboarding purposes. The Steam-specific tools are distinct, but they are drowned out by a large unrelated set (Polymarket, Pipeworx, npm scanning) that makes selection confusing.

Naming Consistency2/5

Tool names follow no single convention: some are verb_noun (resolve_vanity_url, generate_llms_txt), some are noun-only (app_details, player_stats), and others use domain prefixes inconsistently (polymarket_arbitrage, ask_pipeworx_grounded, deep_research). The mix of descriptive and vague names (process, run, execute) adds to the inconsistency.

Tool Count2/5

At 45 tools, the server is heavily overloaded, especially for a server named 'Steam' where only about a third of the tools actually relate to Steam. The rest belong to Pipeworx, Polymarket, and other unrelated domains, making the scope unclear and the tool count far too large for a focused purpose.

Completeness3/5

For the Steam domain, the server covers a reasonable range: app details/news, player counts, friends, owned games, achievements, stats, bans, levels, and summaries. However, notable gaps exist such as store search, reviews, wishlist, or any user inventory/trading features. The non-Steam tools add breadth but do not address these missing Steam operations.

Resources