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Arcgis Chapelhill

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 declare readOnlyHint=true and idempotentHint=true, but the description adds valuable behavioral context by stating it 'fetches the page' (an external network call) and emits a 'standard llms.txt markdown format' output. This goes beyond the generic safe-read annotations, warranting a score above baseline.

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 and well-structured: two sentences covering functionality and output, followed by a clear 'Useful for' list. Every sentence earns its place without repetition or 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?

Despite having no output schema, the description explicitly states the output format ('single text blob ready to drop at site-root/llms.txt') and the process (fetch, extract, emit). It covers the tool's behavior, use cases, and output, making it complete for its moderate complexity.

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 parameters are already well-documented. The description adds minimal semantic value beyond the schema, mentioning 'any URL' and 'key links' but not introducing new parameter-related detail, which aligns with the baseline of 3.

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's purpose with a specific verb ('generate'), resource ('llms.txt file'), and outcome ('so AI crawlers can index the site cleanly'). It also distinguishes the tool from siblings by focusing on llms.txt generation, a unique niche among the listed sibling tools.

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 explicit 'Useful for' scenarios (client indexing, personal drafts, competitor auditing), which gives clear context on when to use the tool. However, it does not mention when not to use it or explicitly name alternatives like scan_competitor_ai_presence, so it falls short of a 5.

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

A3.8/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently literally identical, and the polymarket_* family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread, polymarket_edge_tracker) all orbit the same prediction-market opportunity space. The descriptions are detailed and do help, but the sheer number of near-synonymous entry points makes misselection likely.

Naming Consistency4/5

The vast majority of tools follow a consistent snake_case, verb-first pattern: ask_pipeworx, compare_entities, query_layer, resolve_entity, search_datasets, validate_claim. Minor deviations exist — the polymarket_* tools are noun-phrase style and a few names like entity_profile, layer_info, and ai_visibility_check are noun-led — but the overall style is uniform enough to be predictable.

Tool Count2/5

34 tools is already in the overstuffed range, but the bigger problem is that only 3 of them (search_datasets, layer_info, query_layer) relate to the server's stated ArcGIS/Chapel Hill identity. The other 31 tools appear to be an unrelated Pipeworx data-research, prediction-market, memory, and subscription bundle merged into this server, making the count grossly disproportionate to the apparent purpose.

Completeness2/5

The three GIS tools form a usable read-only search → schema → query workflow, so the ArcGIS domain is not completely absent. However, as a whole the server has no coherent domain to be complete for, and the ArcGIS side lacks broader capabilities like layer enumeration, spatial filters/statistics, or any write/edit operations. For a server named Arcgis Chapelhill, having 31 out-of-scope tools constitutes a major completeness failure.

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