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Housing Intel

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 indicate read-only, open-world, idempotent, non-destructive. Description adds behavioral detail: fetches the page, extracts title/description/key links. No contradiction with annotations, and it enriches understanding of how the tool works.

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?

Two sentences, front-loaded with purpose and format, followed by use cases. No wasted words.

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?

Despite no output schema, description fully explains output: a single text blob ready for use. Inputs are simple; all relevant context is covered for a tool of this complexity.

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 is 3. Description adds extra context: URL example and a note that default max_links is 25 with max 50, which provides more guidance than schema alone.

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, listing specific actions (fetches page, extracts title/description/key links) and output format. It distinguishes from sibling tools by its unique function, with no ambiguity.

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?

Explicit use cases are provided (getting client site indexed, drafting for own project, auditing competitors). While it lacks explicit negative guidance, the positive examples are clear and cover common scenarios.

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 intentionally overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, with the beta variant explicitly matching stable behavior right now. entity_profille/recent_changes/compare_entities and the multiple polymarket scanning tools also cover closely related jobs, so an agent must read carefully to avoid picking the wrong variant.

Naming Consistency3/5

All tools use lowercase snake_case, which is a consistent base style. However, the naming grammar is mixed: proper verb_noun tools like compare_entities and validate_claim sit beside noun-phrase/domain tools like housing_market_screen and polymarket_edges, plus the awkward compound case_shiller_metro_compare. The housing_ and polymarket_ prefixes help, but the pattern is not uniform enough for a 5.

Tool Count2/5

41 tools far exceeds the 25+ threshold and the typical well-scoped 3-15 range. Many tools pertyain to Polymarket, npm scanning, llms.txt generation, and memory, which have little to do with Housing Intel, so the count is not earned by the server's stated domain.

Completeness4/5

For the housing domain specifically, the coverage is strong: market snapshot, affordability, employment, mortgage history, rental/property analysis, metro demand, signal scanning, and Case-Shiller comparisons cover the main data needs. The generic ask_pipeworx and deep_research tools also backfill specialized queries. The weakness is scope blur, not obvious missing housing operations.

Resources