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dictionary

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

A4.3/5.0
Behavior4/5

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

The description discloses the key behavioral steps: fetches the page, extracts elements, and emits a single text blob. This adds useful context beyond the readOnlyHint/idempotentHint annotations, confirming it's a safe, read-only operation that produces a standardized output.

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 tight and front-loaded: first sentence states action and purpose, second explains process, third lists use cases. Every sentence earns its place, and the length is appropriate for the tool's complexity.

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?

With no output schema, the description appropriately clarifies the return format ('single text blob ready to drop at site-root/llms.txt'). It covers workflow, purpose, and use cases, making it complete for a tool with only two simple parameters.

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% for both parameters (url and max_links), so the schema does the heavy lifting. The description reinforces that output is a text blob but does not add new meaning beyond what the schema already provides, aligning with the baseline for full coverage.

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 a specific action ('Generate a production-ready llms.txt file') with a clear resource (URL) and output (standard llms.txt markdown). It distinguishes itself from sibling tools by focusing on producing the actual file rather than just analyzing AI presence.

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 provides concrete use cases (client indexing, personal projects, competitor auditing), giving clear context for when to use the tool. However, it does not explicitly name alternative tools or exclusions, so it falls short of the 'when-not-to-use' guidance.

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

A3.9/5.0
Disambiguation2/5

Several tools have near-identical purposes: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded are the same router with only an evidence-extraction difference. polymarket_edges, bet_research, and polymarket_arbitrage also overlap heavily in surfacing mispricings, and ai_visibility_check is essentially a single-entity version of scan_competitor_ai_presence.

Naming Consistency3/5

There are recognizable patterns: ask_pipeworx_*, polymarket_*, verb_noun pairs like define_word, get_synonyms, resolve_entity. However, conventions are mixed across the set — ask_pipeworx_beta uses a suffix, ai_visibility_check vs scan_competitor_ai_presence are phrased in different styles, and memory tools (remember/recall/forget) follow yet another pattern. Readable but not predictable.

Tool Count2/5

33 tools is heavy for a single server, and many of them are meta-tools (discover_tools, suggest_questions, ask_pipeworx variants, pipeworx_trending, pipeworx_feedback, memory tools) that inflate the surface. The count would be defensible if each tool were orthogonal, but the overlap in research/Polymarket/memory areas means several tools do not earn their place.

Completeness4/5

For the actual Pipeworx data-access domain, coverage is quite rich: lookup, grounded answers, deep research, entity profiles, comparisons, claim validation, subscriptions, memory, and discovery tools all exist. Minor gaps remain (e.g., no tool to manage account/API keys, patents soft-fail), but the core query-research-monitor lifecycle is well covered. The server name 'dictionary' is misleading — only two tools serve a dictionary purpose — yet the inferred domain from descriptions is a data gateway, for which the surface is strong.

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