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Open Elevation

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. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by explaining the process: fetches the page, extracts title/description/key links, emits standard markdown. This provides behavioral context beyond 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 a single paragraph of three sentences, front-loading the purpose, then process, then use cases. Every sentence earns its place with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 params, no output schema, no nested objects), the description covers purpose, process, usage scenarios, and output format. It lacks details on error handling for invalid URLs, but this is minor.

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 both parameters are already well-documented in the schema. The description does not add significant new semantics for parameters 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 clearly states the verb 'generate', the resource 'llms.txt file for any URL', and the purpose for AI crawlers. It distinguishes itself from sibling tools like ai_visibility_check by focusing on generating the file rather than just checking visibility.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists three use cases (client site indexing, own project, competitor audit) but does not explicitly state when not to use this tool or suggest alternatives. It lacks exclusion criteria or comparison with siblings.

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

B3.3/5.0
Disambiguation1/5

The tool set covers many unrelated domains (elevation, finance, prediction markets, AI visibility, etc.) with multiple overlapping tools per domain (e.g., three 'ask_pipeworx' variants, several 'polymarket' tools). An agent would struggle to distinguish which tool to use for a given task.

Naming Consistency2/5

Names follow no consistent pattern: some use snake_case with vague verbs (e.g., 'process', 'run'), others use descriptive but unrelated prefixes ('ai_', 'ask_pipeworx_', 'polymarket_'). There is no uniform verb_noun structure.

Tool Count3/5

With 32 tools, the count is reasonable for a large server, but the vast majority are irrelevant to the server's stated purpose (elevation). This mismatch makes the count inappropriate.

Completeness1/5

The server name 'Open Elevation' implies a focus on elevation data, yet only 2 of 32 tools (get_elevation, get_elevations) are related. There are severe gaps: no area elevation, no geocoding, no terrain analysis. The tool surface is largely off-topic.

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