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deckofcards

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, openWorldHint, idempotentHint, destructiveHint. The description adds behavioral context: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This goes beyond annotations by explaining the process and output format.

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?

Two sentences, front-loaded with the main purpose, then process and output, followed by use cases. No redundant information; every sentence adds 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?

The description covers purpose, process, output format, and use cases. With annotations covering safety and only 2 well-documented parameters, the description is fully sufficient. No output schema required.

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 both `url` and `max_links` having descriptions. The description doesn't add additional parameter-level meaning beyond saying 'any URL', which is already in 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 the tool generates a production-ready llms.txt file for any URL, using the specific verb 'Generate' and resource 'llms.txt'. It distinguishes from siblings by focusing on file generation for AI indexing, unlike sibling tools like scan_competitor_ai_presence.

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?

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.' This gives clear context but does not name alternative tools or when-not-to-use, so score 4.

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

B3.4/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are functionally identical (beta just has experimental routing), and the Polymarket suite (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) all revolve around prediction-market signal detection with unclear boundaries. Also ai_visibility_check and scan_competitor_ai_presence overlap heavily.

Naming Consistency2/5

Naming is a mix of verb-first (draw_cards, resolve_entity, discover_tools) and noun-first (entity_profile, new_deck, recent_alerts) styles, with inconsistent prefixes (pipeworx_feedback vs ask_pipeworx) and no unifying convention. Some tools are bare verbs (recall, remember), others are noun phrases (polymarket_edges).

Tool Count2/5

34 tools is excessive for a server named 'deckofcards' — only 3 tools relate to cards. Even as a general-purpose data/research server, 34 is on the high end and includes many near-duplicates (ask_pipeworx variants) and highly specialized tools that could be consolidated.

Completeness3/5

The research side is fairly complete (lookup, grounding, comparison, profiles, claim verification, memory, subscriptions), but the card-deck functionality is minimal (only create, draw, shuffle) and lacks any deck inspection or hand management. The server's scope is unclear, making it hard to assess true coverage.

Resources