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Google_docs

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false. Description adds process details: fetches page, extracts title/description/key links, emits standard markdown. No contradictions; fully transparent about read-only, non-destructive behavior.

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?

Front-loaded with main action and purpose in first sentence. Process in second, output format in third, use cases in bullet list. No redundant words; every sentence earns its place.

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 tool simplicity and rich annotations/schema, description covers all essential aspects: action, input, process, output (single text blob), and use cases. No output schema needed; context is complete.

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 3. Description does not explicitly detail parameters but implies max_links controls link count. Schema descriptions are clear, so description adds no significant extra meaning.

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?

Clearly states verb+resource: 'Generate a production-ready llms.txt file for any URL'. Specifies output format and purpose (AI crawlers). Distinguishes from siblings like ai_visibility_check by focusing on file 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?

Provides concrete use cases in a bullet list (client indexing, own project, competitor auditing). Lacks explicit when-not-to-use or comparison with alternatives, but the scenarios are clear enough.

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

B3.4/5.0
Disambiguation2/5

Several tools are nearly indistinguishable: ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior, while ask_pipeworx_grounded and deep_research heavily overlap with the same router. The dense Polymarket tool cluster and discovery tools (discover_tools vs suggest_questions) further blur boundaries, though many individual tools do have distinct niches.

Naming Consistency3/5

Names are consistently lowercase snake_case, which helps, but the convention is mixed: some are verb_noun (docs_create, list_subscriptions), some are noun phrases (entity_profile, deep_research, bet_research), and one uses a suffix (ask_pipeworx_beta). It is readable but not a predictable pattern across the set.

Tool Count2/5

37 tools is already above the 25+ threshold, but the bigger problem is that the server is named Google_docs and only 6 of the 37 tools relate to Google Docs. The remaining 31 tools form a broad data-research and prediction-market platform, making the set feel bloated and mislabeled for its apparent purpose.

Completeness2/5

For a Google Docs server, the surface is incomplete: you can create, read, insert, replace, and append text, but there is no delete, no list/search, no formatting control, and no permission handling. The extensive Pipeworx and Polymarket tools cover a different domain entirely, so they do not fill the gaps in the docs workflow.

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