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Nyc Parks

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

A4.2/5.0
Behavior4/5

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds context by detailing that the tool fetches the page, extracts title/description/key links, and outputs standard markdown. No contradictions found.

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 efficient: two sentences covering purpose and process, plus a sentence of use cases. No redundant phrases; front-loaded with key information.

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 tool's simplicity (2 params, no output schema, rich annotations), the description covers purpose, process, and outputs adequately. It lacks mention of error handling or limitations, but those are not critical for this use case.

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 description adds little beyond the schema. It implies the url parameter via 'for any URL' and max_links via 'maximum of link entries', but does not provide deeper semantics or formatting details.

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 action (generate), the resource (llms.txt file for a URL), and the outcome (AI crawlers can index the site). It distinguishes itself from siblings by specifying a unique function not covered by other 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?

Explicitly lists three use cases (client indexing, personal drafting, competitor auditing). However, it does not mention when to avoid using this tool or compare it to alternatives, which would strengthen guidance.

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

All Nyc Parks toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

The ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded trio are functionally near-identical to an agent (the beta is explicitly described as currently identical to stable), and the five polymarket_* tools (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread) have heavily overlapping concerns around finding and validating prediction-market edges. The descriptions are detailed, but the boundaries require careful reading to pick correctly.

Naming Consistency4/5

All tools use snake_case and mostly follow a verb-first or noun-phrase convention, with recognizable family prefixes (ask_pipeworx_*, polymarket_*, pipeworx_*) that aid navigation. Minor deviations exist — bare nouns like categories and events, and the inconsistent verb placement in bet_research vs. validate_claim — but the overall pattern is predictable.

Tool Count1/5

33 tools is already heavy, but the fatal problem is that the server is named 'Nyc Parks' while ~31 of 33 tools are a generic Pipeworx data-retrieval/prediction-market toolkit. The count is egregiously mismatched to the stated purpose; only 2 tools relate to NYC Parks at all.

Completeness2/5

For the server's literal name, the surface is severely incomplete: categories and events exist, but there is no way to look up parks, facilities, permits, or event details, and no CRUD-lifecycle coverage. Viewed as a Pipeworx data toolkit the surface is quite thorough, but that is not what the server claims to be, so the stated NYC Parks domain is barely covered and creates dead ends.

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