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Celestrak

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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that it 'Fetches the page, extracts title/description/key links, and emits standard markdown', complementing the annotations with concrete steps. No contradiction.

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?

Two well-structured sentences: first states main action and output format, second lists use cases. Every sentence adds value; no redundancy.

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?

Given the tool's simplicity (2 params, no output schema, clear annotations), the description fully covers purpose, behavior, and usage scenarios. No gaps identified.

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?

Input schema has 100% coverage with descriptions for both parameters. The description does not add new semantic meaning beyond the schema, but it implicitly explains how 'url' is used (fetched). Baseline 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 clearly states the tool generates an llms.txt file for a given URL, with specific verb 'Generate' and resource 'llms.txt'. It differentiates from siblings like 'scan_competitor_ai_presence' by focusing on outputting the standard llms.txt format for AI crawlers.

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?

Provides explicit use cases: getting a client's site indexed, drafting for own project, or auditing competitor's AI visibility. Lacks explicit 'when not to use' or alternatives, but the context is clear.

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

The tool set mixes several distinct domains (satellite orbital data, Pipeworx data routing, prediction-market analysis, memory management, subscriptions), but within the Pipeworx umbrella there is heavy overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route natural-language questions to the same underlying 5,756 tools. An agent could easily misselect between them, especially since ask_pipeworx and ask_pipeworx_beta are described as currently identical.

Naming Consistency3/5

Many tools follow a clear verb_noun pattern (list_subscriptions, create... none, but compare_entities, resolve_entity, generate_llms_txt, scan_dependency, subscribe/unsubscribe, remember/recall/forget), yet the naming is inconsistent across the set: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, get_satellite, get_group, recent_alerts, recent_changes, entity_profile, and deep_research do not share a uniform convention. CamelCase appears in polymarket_arbitrage, polymarket_edges, etc. while most others are snake_case, and the satellite tools (get_satellite, get_group, search_by_name) form a distinct sub-pattern that clashes with the Pipeworx meta-tools.

Tool Count2/5

34 tools is on the heavy side, and the effective surface is bloated: there are three variants of ask_pipeworx, four polymarket_* tools, three satellite-specific tools that are unrelated to the server's apparent core purpose, and several meta/utility tools (remember, recall, forget, pipeworx_feedback, pipeworx_trending, suggest_questions) that could be consolidated or are only tangentially related. The count itself is not extreme, but the scope is muddled: the server claims the name Celestrak (satellite tracking) while the overwhelming majority of tools are for Pipeworx data access and prediction markets, making the tool count feel inappropriate for either purpose.

Completeness3/5

For the Pipeworx data-access domain, the tool set is quite thorough: natural-language routing, grounded answers, deep research, entity profiling, entity comparison, claim verification, semantic search, and tool discovery are all present. However, there are notable gaps: the subscription lifecycle lacks an update/resume mechanism, and the satellite domain (the server's namesake) is severely incomplete — only three lookup tools with no live tracking, no group listing beyond a handful of groups, and no clear lifecycle CRUD. The memory tools (remember/recall/forget) are minimal but complete for their narrow scope.

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