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Arcgis Montana

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

A4.3/5.0
Behavior4/5

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

Annotations already indicate a safe, read-only, idempotent operation. The description adds valuable behavioral context: it fetches the page, extracts key content, and emits a single text blob for site-root placement. This goes beyond annotations by explaining the internal workflow 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?

Three sentences, front-loaded with the primary action. Every sentence earns its place: what it does, how it works, and when to use it. No filler or repetition.

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?

Despite lacking an output schema, the description explains the output format ('standard llms.txt markdown', 'single text blob ready to drop at site-root/llms.txt'). It covers input, process, output, and use cases, making it complete for a tool of this complexity with rich annotations.

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 baseline is 3. The description mentions 'key links' which relates to max_links, but it doesn't add much beyond the schema's own parameter descriptions. The schema already documents url and max_links adequately.

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 uses a specific verb ('Generate') and names the exact resource (llms.txt file for any URL), clearly distinguishing it from sibling tools. It states what the tool does: fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. This is a specific, non-tautological purpose statement.

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 provides clear use cases: indexing a client's site, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor. It does not explicitly name alternatives or when-not-to-use, but the context is clear enough to guide selection.

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

A3.9/5.0
Disambiguation3/5

Several clusters of similar tools exist: ask_pipeworx, ask_pipeworx_beta (identical right now), and ask_pipeworx_grounded are easily confused, and the many polymarket_* tools overlap heavily. However, the extremely detailed descriptions usually clarify the specific intent, so an agent can often pick correctly.

Naming Consistency4/5

Tool names are uniformly snake_case and mostly follow verb_noun or a recognizable prefix pattern (ask_, polymarket_, pipeworx_). There are a few noun-phrase exceptions like layer_info and entity_profile, but the style is consistent enough that naming is not a major source of confusion.

Tool Count2/5

34 tools is heavy for any server, and especially for one named 'Arcgis Montana' where only three tools (search_datasets, query_layer, layer_info) relate to GIS. Most of the surface is a general-purpose data/analytics API, making the count feel bloated and unfocused.

Completeness2/5

Relative to the implied ArcGIS Montana purpose, the surface is severely incomplete: it only supports searching and querying datasets, with no create/update/delete or administrative capabilities. The Pipeworx functionality is broad, but the GIS side is a thin slice, leaving obvious gaps for geospatial workflows.

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