Skip to main content
Glama

Va Museum

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).

TDQS

A4.5/5.0
Behavior5/5

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

Annotations indicate readOnly, idempotent, non-destructive. The description adds that it fetches the page, extracts title/description/key links, and emits standard markdown. This fully discloses behavior without contradiction, exceeding what annotations alone provide.

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 concise and well-structured: two sentences for purpose and process, followed by bullet-pointed use cases. No extraneous information, every sentence contributes to understanding.

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?

Even without an output schema, the description explains the output format (single text blob, standard llms.txt markdown). It covers input, process, output, and use cases, making it self-contained and complete for the tool's simplicity.

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%, so the baseline is 3. The description largely repeats the schema's descriptions for both parameters ('Full URL' and 'Maximum number of link entries') without adding significant new meaning or context.

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. The verb 'generate' and resource 'llms.txt' are specific, and the description distinguishes it from sibling tools by focusing on a specific output format and use case.

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 concrete use cases: getting a client's site indexed, drafting for own project, or auditing competitors. It implies when to use but does not explicitly exclude alternatives or mention when not to use, which slightly reduces the score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Va Museum toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is notable overlap between ask_pipeworx and ask_pipeworx_grounded (same underlying data query, different answer modes), and multiple polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges) can confuse agents about which to use for a given betting query. Memory tools (remember/recall/forget) are clear, but the mix of museum, financial, and prediction market tools under one server increases ambiguity.

Naming Consistency3/5

All tool names use snake_case consistently, but the naming pattern is inconsistent: some start with a verb (search_objects, list_subscriptions, remember) while others start with a noun or modifier (ai_visibility_check, entity_profile, polymarket_arbitrage). The 'ask_' prefix is used twice, but overall there is no single predictable convention like verb_noun across the set.

Tool Count3/5

29 tools is high but not excessive for a general-purpose data server. However, the server is named 'Va Museum' which implies a narrow domain, making the count seem bloated. The set includes many tools unrelated to a museum (e.g., prediction markets, SEC filings), so the count is appropriate only if the server's actual scope is broad and multi-domain.

Completeness2/5

For a museum-focused server, the tool surface is severely incomplete, with only two museum-specific tools (search_objects, get_object) out of 29. Even as a general-purpose server, it lacks tools for common operations like updating or deleting resources, and the coverage of domains (e.g., no tool for creating or managing user data) feels ad hoc rather than systematically complete.

Resources