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Quickbooks

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

A3.8/5.0
Behavior4/5

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

The description discloses that the tool fetches the page, extracts title/description/key links, and emits markdown, which is behavior beyond annotations. Annotations already indicate it is read-only, idempotent, and non-destructive, so the description adds contextual detail about the external network call and output structure. No mention of rate limits or handling of large pages, but still solid.

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 a single paragraph with a front-loaded main point, followed by a concise process explanation and three bullet-like use cases. Every sentence serves a purpose; no fluff. Ideal length and structure.

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?

Given the tool's simplicity (2 params, no output schema, strong annotations), the description covers the core functionality and output format. It states the output is 'a single text blob ready to drop at site-root/llms.txt'. It lacks mention of potential issues (e.g., only public pages, JavaScript rendering, timeout handling) but is sufficient for an AI agent to understand usage.

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 have clear schema descriptions (100% coverage). The tool description does not add significant extra meaning beyond what the schema already provides—it restates the purpose of url and max_links without additional formatting or behavioral nuance. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 URL, specifying the process (fetch, extract, emit) and the output format. It provides use cases but does not explicitly differentiate from sibling tools like scan_competitor_ai_presence, which might also involve AI indexing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists three typical use cases ('getting a client's site indexed...', 'drafting for your own project', 'auditing competitor'), giving clear context for when to use it. However, it does not explain when not to use it (e.g., for sites behind logins) 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

A3.5/5.0
Disambiguation2/5

Several tools are near-duplicates (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded), and there are overlapping clusters among the polymarket_* tools, research tools (deep_research, entity_profile, compare_entities, recent_changes), and AI-visibility tools (ai_visibility_check vs scan_competitor_ai_presence). The detailed descriptions help, but an agent selecting among these could easily pick the wrong one.

Naming Consistency2/5

Snake_case is used consistently, but the naming pattern is otherwise mixed: some tools are verb_noun (validate_claim, suggest_questions), some are bare nouns (entity_profile, polymarket_edges), some are verbs without objects (remember, forget, ask_pipeworx), and only the five QuickBooks tools share a qb_ prefix. This creates multiple naming ecosystems with no unified convention.

Tool Count2/5

At 36 tools, this is well above the 25+ threshold for 'too many'. More importantly, the server is named Quickbooks but only 5 tools are accounting-related; the other 31 are unrelated Pipeworx data, prediction-market, memory, and meta tools, making the count both excessive and off-purpose.

Completeness2/5

For the QuickBooks domain named by the server, the surface is read-only: get customer, get invoice, list accounts, list invoices, and generic query. There are no create, update, delete, payment, bill, deposit, or report operations, which is a significant gap. For the broader Pipeworx data domain it is fairly complete, but that domain is not what the server name promises.

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