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Geoboundaries

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, so the safety profile is known. The description adds valuable behavioral detail: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format' and specifies the output is a 'single text blob ready to drop at site-root/llms.txt'. No contradiction with 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 three sentences with no redundancy. It leads with the core purpose, explains the mechanism, and concludes with practical use cases. Every sentence adds value.

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?

With only two parameters and no output schema, the description fully covers the tool's behavior, output format ('Output is a single text blob ready to drop at site-root/llms.txt'), and use cases. Annotations cover safety. The description is complete for this simple tool.

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?

The input schema has 100% coverage for both parameters, so the baseline is 3. The description adds context about what the tool does with the URL (fetches, extracts) but does not add new parameter-level details beyond what the schema already provides for max_links or url.

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 function: 'Generate a production-ready llms.txt file for any URL'. It uses a specific verb and resource, and distinguishes itself from siblings by focusing solely on llms.txt generation, which no other tool handles.

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 ('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') and clarifies the input is 'any URL'. However, it does not explicitly mention alternative tools or when not to use it, 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.

All Geoboundaries toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Many tools have overlapping or unclear purposes, e.g., multiple entity research tools (entity_profile, recent_changes, compare_entities, validate_claim) and several betting tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_kalshi_spread). The memory tools (remember, recall, forget) and generic lookup tools (ask_pipeworx, discover_tools) further blur boundaries, making it hard for an agent to consistently select the right tool.

Naming Consistency2/5

Tool names follow no consistent pattern: some use snake_case verbs (forget, recall, remember), others are noun phrases (entity_profile, recent_changes), and many have mixed conventions (ai_visibility_check, ask_pipeworx, bet_research). There is no clear verb_noun structure, and the naming style varies widely across the set.

Tool Count3/5

At 22 tools, the count is within a reasonable range, but the server's stated purpose ('Geoboundaries') is severely mismatched with the actual tool set, which covers data retrieval, betting, memory, and more. The number is not excessive for the breadth of functionality, but it feels bloated for a server that should be focused on geographic boundaries.

Completeness1/5

The server is named 'Geoboundaries' but only provides two boundary-related tools (get_boundaries, get_geometry). The remaining 20 tools are unrelated, covering general data access, betting arbitrage, and memory operations. This leaves massive gaps for the implied domain (no tools for boundary editing, search by location, or other geographic operations) while over-supplying tools for unrelated tasks.

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