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Arcgis Allegheny

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral detail: it fetches the page, extracts title/description/key links, and returns markdown output. It also clarifies that the output is a text blob, which is not in the annotations. No contradictions.

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 three sentences: purpose, process, and use cases. It is front-loaded with the primary action and contains no redundant filler.

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?

For a tool with 2 parameters and no output schema, the description explains the output (single text blob) and workflow clearly. Combined with strong annotations, it is complete enough for an agent to select and invoke the tool 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 coverage is 100%, with both `url` and `max_links` already having descriptive comments in the input schema. The description doesn't add parameter-specific semantics beyond what's in the schema, so a baseline score of 3 is appropriate.

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 clear purpose with a specific verb ('Generate') and resource ('llms.txt file'), and elaborates on the process (fetching, extracting, emitting). It distinguishes itself from sibling tools like `scan_competitor_ai_presence` by focusing on producing the file rather than just analyzing visibility.

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 use cases: client indexing, drafting for projects, auditing competitor AI visibility. It lacks explicit 'when not to use' comparisons with alternatives, but the use-case list effectively conveys when to pick this tool.

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

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, and several Polymarket analysis tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, bet_research) blur together. The three ArcGIS tools are distinct, but they are drowned out by a large set of data-query and prediction-market tools with unclear boundaries.

Naming Consistency3/5

All tool names use snake_case, but the verb/noun pattern is mixed: some are command-style (query_layer, validate_claim), some are noun phrases (layer_info, entity_profile), and others are bare verbs (remember, forget). The naming is readable but not consistently patterned.

Tool Count2/5

34 tools is too many for a server named 'Arcgis Allegheny', especially since only three tools (search_datasets, layer_info, query_layer) relate to ArcGIS at all. The bulk of the tools address unrelated domains like Pipeworx data lookups and Polymarket betting, making the count excessive for the apparent purpose.

Completeness1/5

For a server focused on ArcGIS Allegheny County data, the surface is severely incomplete: only three read-only tools (search_datasets, layer_info, query_layer) cover the domain, and they lack operations like adding, updating, or deleting features. The remaining 31 tools are unrelated to GIS, so the server fails to provide a coherent or complete toolset for its stated purpose.

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