Skip to main content
Glama

Opensky

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

A4.1/5.0
Behavior4/5

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

Description adds behavioral details beyond annotations: fetches page, extracts title/description/key links, emits standard format, output is a text blob. Annotations (readOnlyHint, idempotentHint) are consistent and description enriches them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured, front-loaded with main purpose, then details, then usage examples. Slightly verbose but every sentence adds value; could be trimmed but not excessive.

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?

For a simple tool with 2 params and no output schema, the description adequately covers behavioral aspects and output format. Annotations and usage examples provide completeness.

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 baseline is 3. The description mentions URL implicitly and 'max_links' as 'Maximum number of link entries', adding some context but not substantially beyond the schema.

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 generates a production-ready llms.txt file for any URL, specifying verb 'generate', resource 'llms.txt', and purpose 'for AI crawlers'. It distinguishes from sibling tools implicitly by unique functionality.

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?

Explicitly lists three use cases: getting a client's site indexed, drafting for own project, auditing competitor. No when-not or alternatives, but the guidance is sufficient for a tool with no close siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Opensky toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation2/5

Several tools have heavily overlapping entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all take natural-language factual questions, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. There is also overlap among get_states, get_aircraft, and airspace_activity, plus a large cluster of prediction-market tools with similar discovery purposes.

Naming Consistency4/5

The set is mostly snake_case and readable, with familiar patterns like get_*, list_*, resolve_*, and compare_*. It is not chaotic, but there are notable deviations: noun-phrase names like entity_profile, recent_changes, ai_visibility_check, and airspace_activity break the verb-first pattern.

Tool Count2/5

35 tools is too many for a single coherent server, especially because they comprise several independent families: aviation, data/research, prediction markets, memory, and subscriptions. Each tool is individually justified, but the bundle should be split into smaller focused MCP servers.

Completeness3/5

The data-research side is fairly complete, with discover, routing, grounded verification, entity resolution, search-within, compare, and follow-up tools, and the subscription and memory lifecycles are covered. However, the OpenSky side is incomplete: get_flights explicitly cannot return its data, and referenced route/arrival/departure tools are missing from the set.

Resources