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Giantbomb

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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: it fetches the page, extracts title/description/key links, and emits standard markdown format. This explains the process and output nature beyond 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 long, front-loaded with purpose, followed by process and use cases. Every sentence adds value; there is no redundancy or verbosity.

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 simple parameters (2, one required), no output schema, and annotations covering safety, the description provides complete context: what it does, how it works, output format, and use cases. Nothing essential is missing for an AI agent to select and invoke this tool 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% (both 'url' and 'max_links' are described in the input schema). The tool description does not add additional parameter meaning beyond what the schema provides. 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 uses a specific verb ('Generate'), identifies the resource ('llms.txt file for any URL'), and clearly states the end result. It distinguishes itself from sibling tools like 'ai_visibility_check' and 'scan_competitor_ai_presence' by focusing on generating the standard llms.txt file for AI crawlers.

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 three 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'), providing clear context for when to use the tool. It does not specify when not to use or name alternatives, but the use cases are sufficient for guidance.

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

C2.8/5.0
Disambiguation2/5

Several tool families have blurry boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route into the same underlying catalog with only subtle differences, and the six polymarket_* tools overlap heavily (edge scanning vs. fill-risk vs. arbitrage vs. cross-venue spread). Compounding this, the server is named 'Giantbomb' but only 7 of 38 tools relate to that domain, so an agent cannot predict what this server does from its name.

Naming Consistency3/5

All tool names are snake_case, which helps, but the verb pattern is inconsistent: verb_noun (list_subscriptions, resolve_entity, validate_claim), bare verbs (remember, recall, forget), noun-prefix subjects (entity_profile, pipeworx_feedback, polymarket_edges, recent_changes), and even a versioned name (ask_pipeworx_beta) that breaks its own family's pattern. There is no predictable naming scheme an agent could use to guess a tool's name.

Tool Count2/5

38 tools is well past the 25+ 'too many' threshold, and the count is split across two unrelated concerns: ~7 Giant Bomb tools (several explicitly marked 'API offline as of 2026-05') and ~31 tools for a Pipeworx data-research and Polymarket-trading platform. The number is not merely heavy; it serves no single coherent purpose.

Completeness2/5

For the declared Giant Bomb domain, the surface is incomplete (missing videos, franchises, reviews, people, and other known resource types) and largely dead since the underlying API is offline. The Pipeworx half is comparatively complete with routing, grounded answers, research, memory, subscriptions, and feedback, but that coverage is attached to the wrong server identity and does not fill the gaps in the claimed purpose.

Resources