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Funcheap

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

A4.4/5.0
Behavior4/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral details like fetching the page, extracting data, and emitting markdown. It aligns with annotations and provides context beyond them.

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 concise (~60 words), front-loaded with the main action, and well-structured with process, output, and use cases. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple tool and comprehensive annotations, the description covers main purpose, process, and output format. Missing details on error handling or large page behavior, but adequate for typical use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with clear parameter descriptions. The description adds context by explaining the extraction process and output format, slightly enhancing understanding beyond the schema.

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 an llms.txt file for any URL, using specific verbs and resources. It distinguishes itself from siblings by focusing on a specific output format and process.

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 (client indexing, personal project, competitor audit). It does not state when not to use, but the use cases imply appropriate contexts. Could contrast with similar tools like scan_competitor_ai_presence for higher clarity.

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

A4.1/5.0
Disambiguation4/5

Most tools are distinct (event search, subscriptions, memory, Polymarket arbitrage, data lookups), but ai_visibility_check, scan_competitor_ai_presence, bet_research, polymarket_edges, and polymarket_arbitrage have overlapping purposes around competitive research and prediction-market edge-finding. The detailed descriptions disambiguate them, though an agent could confuse polymarket_edges with polymarket_arbitrage.

Naming Consistency4/5

Tool names are mostly descriptive and consistent: search_events, event, categories, tags are aligned; ask_pipeworx, compare_entities, entity_profile follow a similar pattern. However, polymarket_* tools have an odd mix of `polymarket_arbitrage`, `polymarket_fill_risk`, and `polymarket_edge_tracker`, and discover_tools/recent_alerts/recent_changes are consistent, mostly. Naming is quite consistent overall with minor deviations.

Tool Count3/5

At 35 tools, the count is heavier than typical MCP servers, but it reflects a broad service (Funcheap data + Pipeworx data platform + pred markets). Still, plenty of tools serve the same primary purpose (polymarket_, ask_pipeworx variants) so some pruning would improve the surface.

Completeness4/5

The surface appears complete for its domain: search/retrieve events, category/tag navigation, memory, subscriptions (create/list/cancel/pull), data lookups, entity resolution, comparisons, profiles, edge scanning, and feedback. Minor gaps include no direct 'update' on events (but events are static), and no pricing fetch tool separate from event text, though body text covers it.

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