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Arcgis Fairfield

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. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive, so the safety profile is covered. The description adds valuable behavioral detail: fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format, as well as noting the output is a single text blob ready for site-root placement.

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 plus a 'Useful for' list deliver all essential information with no wasted words. It is front-loaded with the core action, then details on process and output, followed by use cases.

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?

The tool makes an HTTP request, parses content, and produces a standard file format. The description covers inputs, process, output format, and use cases. No output schema exists, but the response shape is described clearly ('single text blob'). Given the moderate complexity and strong annotations, the description is sufficiently complete.

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?

Both parameters are fully described in the schema (100% coverage), so the baseline is 3. The description only reinforces the 'url' purpose and mentions link entries, but does not add meaning beyond the schema's existing descriptions.

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 opens with a specific verb ('Generate') and a precise resource ('production-ready llms.txt file for any URL'), immediately distinguishing this tool from siblings. It also names the target AI crawlers, making the purpose unmistakable.

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?

It explicitly lists three concrete use cases (client sites, own project, competitor audit), which gives the agent clear context for when to invoke the tool. It doesn't mention when not to use it, but the use-case list is specific enough to infer appropriateness.

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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, deep_research, and also multiple Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage). Descriptions try to differentiate but the boundaries are unclear, causing confusion.

Naming Consistency2/5

Naming conventions are mixed: some use snake_case (ask_pipeworx, query_layer), some use descriptive phrases (entity_profile, recent_changes), and there is no consistent verb_noun pattern. The variety makes it hard to predict tool names.

Tool Count1/5

33 tools is far too many for a server named 'Arcgis Fairfield', as most tools are unrelated to GIS or Fairfield (e.g., npm dependency checks, prediction markets, AI visibility). The tool count severely mismatches the server's purported scope.

Completeness1/5

The server's stated purpose is ArcGIS Fairfield, but only 3 tools (layer_info, query_layer, search_datasets) relate to that domain. Critical GIS operations like updating features or managing services are missing, while the vast majority of tools are for other domains.

Resources