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Arcgis Pflugerville

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?

The description goes beyond the annotations by detailing the operational flow: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' It also clarifies the output is 'a single text blob ready to drop at site-root/llms.txt,' which is useful behavioral context. No contradiction with the annotations.

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 remarkably concise: two sentences plus a use-case list. It is front-loaded with the main action, and every sentence serves a purpose—explaining what, how, and when to use the tool without fluff.

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?

Given the simple schema (2 params, no output schema), the description is complete: it explains the tool's purpose, process, output format, and use cases. It covers the return value ('single text blob') adequately, so an agent can select and invoke 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 description coverage is 100%, so the baseline is 3. The description mentions the URL concept ('for any URL') and output context, but it does not add new semantic details for the 'max_links' parameter beyond what the schema provides.

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 states a specific verb+resource: 'Generate a production-ready llms.txt file for any URL.' It also explains the process (fetch, extract, emit) and the output format, clearly distinguishing this tool from siblings like 'scan_competitor_ai_presence' by focusing on the standard llms.txt output.

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 explicit use cases: '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.' This provides clear context for when to use the tool, though it does not mention when-not-to-use or alternative tools.

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

A3.6/5.0
Disambiguation2/5

The tool set contains multiple overlapping tools for data querying (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, suggest_questions) and prediction market analysis (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread), making it difficult for an agent to distinguish which tool to use. The GIS-specific tools are few and could be confused with general data tools.

Naming Consistency2/5

Tool names follow no consistent pattern: some use verb_noun or noun_verb (ask_pipeworx, compare_entities, search_datasets), while others are longer phrases (generate_llms_txt, scan_competitor_ai_presence, polymarket_kalshi_spread). Mixed conventions and lack of uniformity reduce predictability.

Tool Count2/5

With 34 tools, the count is high for a server ostensibly focused on ArcGIS Pflugerville. Many tools are unrelated to GIS (e.g., prediction market tools, general Pipeworx utilities), making the tool surface feel bloated and poorly scoped.

Completeness2/5

For an ArcGIS server, the coverage is minimal: only three tools (search_datasets, layer_info, query_layer) directly support GIS operations. Missing typical GIS capabilities like geocoding, spatial analysis, or editing. The inclusion of many non-GIS tools does not compensate for the lack of depth in the core domain.

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