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Leetcode

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

A4.5/5.0
Behavior5/5

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

The description details the tool's behavior: fetches the page, extracts title/description/key links, and outputs standard llms.txt markdown. This adds context beyond the annotations, which already indicate read-only, idempotent, and non-destructive traits. No contradictions.

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 two concise sentences plus a usage list. It is well-front-loaded with the key action and output format. Every sentence adds value without redundancy.

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 simple tool with 2 parameters and no output schema, the description sufficiently explains input, behavior, output format, and use cases. It covers the essential context for an AI agent to invoke the 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 coverage is 100%, so the schema already documents both parameters. The description adds minor value (e.g., example for url) but doesn't provide new semantics beyond what's in 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 generates a llms.txt file for any URL, specifying the exact action and resource. It also highlights the benefit for AI crawlers and distinguishes itself from sibling tools like 'ai_visibility_check' and 'scan_competitor_ai_presence' by focusing on file generation.

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 explicitly lists three use cases (getting a client's site indexed, drafting for own project, auditing competitor). While it doesn't compare directly to siblings, the scenarios are clear. It could be improved by stating when not to use it, but it's generally helpful.

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

B3.1/5.0
Disambiguation2/5

Multiple tools have overlapping roles: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve the same lookup purpose, and there are six different Polymarket tools with similar names and functions. The inclusion of a large unrelated data platform alongside a few LeetCode tools makes selection additionally confusing.

Naming Consistency4/5

Almost all tools follow a consistent snake_case verb_noun pattern (ask_pipeworx, compare_entities, list_subscriptions, etc.). Minor exceptions like 'problem' and 'daily_question' are still readable and don't break the overall predictability.

Tool Count2/5

37 tools is far too many for a server named 'Leetcode' — the vast majority are unrelated Pipeworx data, prediction-market, and memory tools. Even as a general data server the count is heavy, and for the apparent LeetCode purpose it is severely over-scoped.

Completeness2/5

The LeetCode-specific tools cover basic user stats and problem details but lack problem listing/search, submissions, or any interaction beyond read-only queries. The Pipeworx side is extensive but irrelevant to the server's stated purpose, so the core domain has significant gaps.

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