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Last Fm

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 indicate readOnly, idempotent, non-destructive, openWorld. The description reinforces these by stating it 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format', adding behavioral detail 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 three sentences with no fluff. The first sentence states the core action, the second explains the process, the third lists use cases. Every sentence adds value and the structure is front-loaded.

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 annotations (readOnlyHint, idempotentHint, etc.) and 100% schema coverage, the description provides sufficient context. It mentions the output format ('single text blob') and use cases. No output schema is needed.

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?

Both parameters (url, max_links) are fully described in the schema (100% coverage). The description adds context for url ('Fetches the page') but does not enhance max_links beyond the schema defaults. 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's purpose: 'Generate a production-ready llms.txt file for any URL'. It specifies the action (generate), resource (llms.txt from a URL), and the expected output format. No sibling tool overlaps with this 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?

The description explicitly lists use cases: getting a client's site indexed, drafting for own project, auditing competitors. It provides clear context for when to use, though it does not state when not to use or mention alternatives.

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

B3.1/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the Polymarket cluster (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread). Additionally, discover_tools and suggest_questions both serve as discovery/onboarding tools, and ai_visibility_check vs scan_competitor_ai_presence are closely related. While descriptions are detailed, the boundaries between these tools are unclear, causing potential misselection.

Naming Consistency4/5

All tool names use lowercase snake_case with no camelCase or mixed styles. The naming follows a mostly consistent verb_noun or data_subject pattern (e.g., ask_pipeworx, list_subscriptions, validate_claim, artist_info, recent_alerts). Minor deviations exist, such as recent_alerts and user_top_tracks not beginning with a verb, but the overall pattern is predictable and readable.

Tool Count2/5

40 tools is far too many for a server nominally about Last.fm; only 9 tools actually relate to its stated purpose. The remaining 31 tools cover unrelated domains (Pipeworx data routing, Polymarket betting, memory, subscriptions, etc.), making the set bloated and unfocused. Many tools are near-duplicates (ask_pipeworx, _beta, _grounded; four different polymarket_* analysis tools), inflating the count without adding distinct value.

Completeness2/5

Even within the Last.fm domain, the tool surface is incomplete: there is no artist search, album search, user profile info, recent scrobbles, loved tracks, or music recommendations. The unrelated Pipeworx/Polymarket tools, while individually comprehensive for their own domains, do not compensate for the lack of core Last.fm functionality given the server's stated purpose. The overall set is a fragmented mixture that leaves significant gaps for what the server name promises.

Resources