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stackexchange

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is known. The description adds process details ('Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format') and clarifies the output ('single text blob ready to drop at site-root/llms.txt'), giving useful behavioral context beyond the 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, front-loaded with the purpose, then the process, then use cases. Every sentence earns its place, with no redundant phrasing or filler.

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 simple parameters, rich annotations, and the description explicitly states the output format ('single text blob ready to drop at site-root/llms.txt'). Even without an output schema, the description fully covers what the agent needs to know for correct invocation and interpretation.

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?

The input schema already covers both parameters with clear descriptions (url and max_links), achieving 100% schema description coverage. The tool description adds minimal parameter-specific detail—it mentions 'any URL' and 'key links' but doesn't elaborate on max_links or its default/limit, which the schema already documents. Thus, a 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 opens with a specific verb-object pair ('Generate a production-ready llms.txt file for any URL'), clearly naming the resource and scope. It also names the target consumers (AI crawlers) and the output format, making it distinct from all sibling tools, none of which generate llms.txt files.

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 concrete use cases, e.g., '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.' However, it doesn't explicitly mention when not to use it or name alternative tools, so it stops short of a 5.

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

The StackExchange tools are distinct, but the dominating data-lookup cluster is highly ambiguous: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), ask_pipeworx_grounded, deep_research, and validate_claim all route into the same underlying tool catalog. The polymorpharket tools also overlap heavily, making selection between bet_research, polymarket_edges, polymarket_arbitrage, and fill-risk checks genuinely hard.

Naming Consistency2/5

The names are all snake_case but otherwise follow no consistent pattern: bare verbs (remember, forget, subscribe), prefixed names (pipeworx_feedback, stack_get_user), composite domain names (ask_pipeworx, generate_llms_txt), and generic verbs (resolve_entity, validate_claim, search_within). The StackExchange subset itself is split between stack_get_user/stack_tags and search_questions/get_answers.

Tool Count2/5

36 tools is already in the 'too many' range for one server, and the mismatch with the server name is severe: only 5 of 36 tools relate to StackExchange. The rest form a broad Pipeworx/prediction-market data platform that would itself be oversized for a focused purpose.

Completeness2/5

For a StackExchange-focused server, the surface has core read operations but lacks question-detail-by-ID, comments, related questions, or any write/community actions, and the 31 unrelated tools do not fill that gap. For the broader apparent Pipeworx platform coverage is broad, but the set has no single coherent domain against which completeness can be meaningfully judged.

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