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Ai Briefing

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

A4.2/5.0
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 content, and outputs standard llms.txt format. It does not detail error handling or rate limits but adds meaningful behavioral context 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?

Three sentences: purpose, process, and use cases. Front-loaded with purpose, no redundant information, every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with 2 parameters and no output schema, the description covers input, output format, and use cases. It lacks error handling details but is sufficient for an AI agent to understand the tool.

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% with clear descriptions for both parameters. The description references the parameters indirectly (e.g., 'extracts title/description/key links') but does not add new meaning beyond the schema.

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, specifying the action and resource. It distinguishes from sibling tools like scan_competitor_ai_presence by focusing specifically on llms.txt 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 description provides explicit use cases (e.g., indexing client sites, drafting for own project, auditing competitor visibility) but does not mention when not to use or compare 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

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple tools for querying Pipeworx data (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) and numerous tools for AI news/tools (get_ai_news, get_ai_toolbelt, get_briefing, get_model_landscape, etc.). This will cause an agent to frequently misselect the appropriate tool.

Naming Consistency4/5

Most tools follow a verb_noun pattern in snake_case (e.g., compare_entities, discover_tools, get_briefing). However, a few deviate like 'bet_research' (noun_verb) and 'what_happened' (phrase), but overall the pattern is largely consistent.

Tool Count2/5

38 tools is excessive for a server called 'Ai Briefing', which suggests a focused purpose. The tool count spans multiple domains (AI visibility, Pipeworx queries, Polymarket betting, memory, subscriptions) making it feel overstuffed and unfocused.

Completeness3/5

The tool set covers many aspects of its broad domain (querying, comparing, subscribing, memory), but there are notable gaps: no tool for modifying subscriptions, no user profile management, and the AI news tools overlap rather than cover distinct needs.

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