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tle

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, openWorldHint, idempotentHint, and non-destructive behavior. The description adds behavioral context by explaining that it 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format', and that output is a 'single text blob ready to drop at site-root/llms.txt'. This goes beyond the annotations by detailing the operation and output format, though it does not cover edge cases like error handling.

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, front-loads the main purpose, and includes a useful 'Useful for' section without redundancy. Every sentence contributes meaning: the action, the output format, and the use cases. It is concise and well-structured.

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?

The tool has only 2 parameters, no output schema, and simple behavior. The description fully covers the operation, the output format, and the intended use cases. It explains the return value ('single text blob') and the processing steps, making it complete for the tool's complexity.

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 (url and max_links) are fully documented in the schema. The tool description does not add any additional parameter semantics. According to the rubric, the baseline is 3 when the schema fully covers parameters, and there is no extra value added here.

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 with a specific verb and resource: 'Generate a production-ready llms.txt file for any URL'. It also describes the process (fetches, extracts, emits) and the output format, making it distinct from sibling tools like scan_competitor_ai_presence which focus on analysis rather than generation.

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 'Useful for' section explicitly lists three scenarios: client indexing, own project drafting, and competitor auditing. This provides clear context for when to use the tool, though it does not mention alternatives or exclusion criteria. Therefore it falls short of a 5 but is more than a vague implication.

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
Disambiguation3/5

Most tools have clearly distinct jobs, but ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research are overlapping query/research entry points—and ask_pipeworx_beta is currently identical to ask_pipeworx. Similarly, ai_visibility_check and scan_competitor_ai_presence overlap by composition. The descriptions are detailed enough to choose correctly with care, but the boundaries are not always crisp.

Naming Consistency3/5

All names use lowercase snake_case, which keeps the surface readable, but the naming conventions are mixed: some are imperative verbs (get_tle, list_recent, validate_claim), some are noun phrases (entity_profile, recent_alerts, pipeworx_trending), and several use domain prefixes without a clear verb (polymarket_arbitrage, polymarket_edges). This is still discoverable naming, but it does not follow a consistent verb_noun pattern.

Tool Count2/5

34 tools is well beyond the well-scoped range, and the vast majority belong to a broad Pipeworx research/prediction-market platform rather than the server's apparent 'tle' satellite theme. Only get_tle, list_recent, and search_satellites directly match the server name. It feels like several tool surfaces aggregated into one server rather than one coherent product.

Completeness3/5

For the satellite TLE theme, NORAD lookup, name search, and recent-catalog listing are covered, but orbit propagation, pass prediction, and historical TLE data are missing. For the broader data-research surface, coverage is rich—query, grounded answers, entity resolution, comparison, validation, subscriptions, and memory are all present—so agents have workable paths for most tasks, but the server's mixed scope creates obvious thematic gaps.

Resources