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ipinfo

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

A4.1/5.0
Behavior4/5

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

The description discloses that the tool fetches a page, extracts metadata, and outputs a text blob, adding behavioral detail beyond the readOnlyHint and idempotentHint annotations. It does not cover edge cases like invalid URLs or rate limits, but given the tool's simplicity and existing annotations, this is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two well-structured sentences plus a concise use-case list. It front-loads the core purpose and process, but the list slightly extends length without breaking clarity. Every sentence earns its place, though a slightly tighter list could improve conciseness.

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 tool with only two parameters and no output schema, the description covers the action, process, output format, and common use cases. It lacks details like error behavior or return value specifics, but these are not critical given the simple output (single text blob) and the schema already documenting input limits.

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 description coverage is 100%, with both 'url' and 'max_links' clearly defined in the input schema. The description adds no extra parameter specifics, but the baseline of 3 applies since the schema already handles parameter semantics fully.

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 a production-ready llms.txt file for any URL, with specific detail on the process (fetches, extracts title/description/key links, emits markdown) and the intended output. It differentiates from sibling tools like ai_visibility_check or scan_competitor_ai_presence by focusing on file generation rather than analysis or scanning.

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: getting a client's site indexed, drafting for your own project, or auditing a competitor. However, it does not explicitly mention when not to use it or point to alternative sibling tools for related but different tasks, so the guidance stops short of full exclusions.

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

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer data questions; polymarket_arbitrage, polymarket_edges, and polymarket_edge_tracker all scan prediction markets. Despite long descriptions, an agent could easily select the wrong one, especially the currently identical ask_pipeworx and ask_pipeworx_beta.

Naming Consistency3/5

Mostly snake_case, but with inconsistent patterns: get_my_ip/lookup_ip use verb_noun, entity_profile/recent_changes are noun phrases, remember/recall/forget are bare verbs, and the polymarket_* tools share a brand-prefixed noun style. Readable but not a predictable, uniform convention.

Tool Count2/5

At 33 tools, the server is overloaded, far exceeding the 25-tool threshold for 'too many.' It combines two unrelated identities—the original ipinfo IP lookup and the massive Pipeworx data/research platform—making the toolset heavy and harder for an agent to navigate efficiently.

Completeness4/5

The tool surface provides strong coverage of the data query and research lifecycle: discovery (discover_tools, suggest_questions), entity resolution (resolve_entity), lookup (ask_pipeworx, entity_profile), verification (validate_claim, ask_pipeworx_grounded), and post-processing (search_within, recent_changes). Minor gaps exist—for example, no dedicated 'get SEC filing by accession' tool or single-purpose financials endpoint—but these are workaroundable via the routing tools.

Resources