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Deezer

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

A4.5/5.0
Behavior5/5

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

The description adds significant behavioral detail beyond annotations: it explains the tool fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. This clarifies the read-only, idempotent nature implied by annotations and provides complete process transparency.

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 concise, three sentences long, with the main action front-loaded. Every sentence adds value: first defines output, second explains process, third gives use cases. No wasted words.

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 no output schema, the description adequately describes the output as 'a single text blob ready to drop at site-root/llms.txt'. The tool is simple with 2 well-documented parameters, and the description covers input, process, output, and use cases completely.

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 coverage is 100%, so baseline is 3. The description repeats schema info (e.g., 'Full URL', 'Maximum number of link entries') without adding new semantics. No additional parameter context is provided beyond what the schema already conveys.

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 the process of fetching, extracting, and emitting markdown. It distinguishes itself from siblings by focusing on llms.txt generation rather than general AI visibility checks or other analyses.

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 provides three explicit use cases: getting client sites indexed, drafting for own projects, and auditing competitor visibility. While it doesn't explicitly exclude scenarios or compare to siblings, the examples give clear guidance on when to use the tool.

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

B3.2/5.0
Disambiguation2/5

Many tools overlap in purpose (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research all route queries to structured data), and the set mixes Deezer music tools with an unrelated Pipeworx/Polymarket suite. An agent would struggle to pick the right tool for a given request.

Naming Consistency2/5

Naming is mixed: single-word nouns (album, artist, track, chart), verb_noun snake_case (list_subscriptions, resolve_entity), and verbose multi-concept names (scan_competitor_ai_presence, polymarket_kalshi_spread). No consistent pattern across the set.

Tool Count1/5

37 tools for a server named 'Deezer' is far beyond a music API scope; the overwhelming majority are unrelated data-research, prediction-market, and utility tools. This is an extreme mismatch between the server name and the tool surface.

Completeness2/5

For the Deezer music domain, the surface is incomplete (no playlists, user library, lyrics, or radio), while the many unrelated tools each cover only fragments of their domains. The overall set lacks coherent coverage of any single purpose.

Resources