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Greenhouse

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

A4.6/5.0
Behavior5/5

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

Openly describes the fetch-extract-emit workflow. Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false) align perfectly; description adds context about the process without contradiction. For a non-destructive read tool, this is complete.

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?

Three sentences with zero redundancy: first defines function, second explains mechanism/output, third lists use cases. Perfectly front-loaded with the core action.

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?

Covers purpose, mechanism, output format, and use cases. Lacks detail on error handling or edge cases (e.g., unparseable pages), but the tool is simple and annotations cover safety. Given no output schema, description sufficiently characterizes the single text blob output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers both parameters completely, and the description adds value by explaining 'Full URL' and specifying default (25) and max (50) for max_links. This enhances schema's descriptive comments.

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 it generates a production-ready llms.txt file for any URL, specifying the verb 'generate' and the resource 'llms.txt'. Completely differentiates from sibling tools like ai_visibility_check or scan_competitor_ai_presence, which focus on visibility 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?

Explicitly lists three use cases (client site indexing, personal project drafting, competitor auditing). However, doesn't specify when not to use it or mention alternatives among siblings, though the unique purpose makes alternatives less critical.

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

A3.8/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions, but some overlap exists between research tools like ask_pipeworx, deep_research, and bet_research, which could confuse an agent. The Greenhouse-specific tools are clearly separated by the 'greenhouse_' prefix, aiding disambiguation.

Naming Consistency3/5

Tool names follow snake_case but vary in style: some have a prefix like 'greenhouse_' or 'pipeworx_', others do not (e.g., ask_pipeworx vs. deep_research). The verb-object pattern is inconsistent (e.g., 'generate_llms_txt' vs. 'entity_profile'), making naming less predictable.

Tool Count2/5

35 tools is excessive for a single server, especially one named 'Greenhouse' which implies an ATS focus. The set aggregates multiple domains (ATS, data research, memory, prediction markets) without clear scoping, overwhelming the agent and reducing coherence.

Completeness3/5

The Pipeworx/data research subset is fairly complete with lookups, comparisons, verification, and subscriptions. However, the Greenhouse ATS subset lacks create/update/delete operations, leaving notable gaps. The mixed domains make overall completeness uneven.

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