Skip to main content
Glama

Coingecko

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. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare read-only and idempotent behavior. The description adds specific process details: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This provides useful context beyond annotations without contradiction.

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 a single, well-structured paragraph that front-loads the primary purpose and efficiently covers usage, behavior, and output format with no extraneous 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?

For a simple tool with 2 parameters, no output schema, and full annotation coverage, the description is complete. It explains the input, process, and output format ('single text blob ready to drop at site-root/llms.txt').

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. The description adds default and max for 'max_links' ('default 25, max 50'), but does not significantly extend meaning beyond the schema.

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 verb 'Generate' and the resource 'llms.txt file for any URL', and distinguishes the tool from siblings like ai_visibility_check and scan_competitor_ai_presence by specifying its unique purpose for AI crawler indexing.

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 explicitly lists use cases: '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.' It lacks explicit when-not to use, but the use cases are clear and direct.

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

All Coingecko 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

A3.6/5.0
Disambiguation2/5

Several tools are near-duplicates or have heavily overlapping responsibilities (ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded, polymarket_arbitrage / polymarket_edges / polymarket_fill_risk, and ai_visibility_check / scan_competitor_ai_presence). The many meta/entry-point tools (discover_tools, suggest_questions, pipeworx_trending) also blur the boundary between discovery and execution.

Naming Consistency2/5

Tool names mix imperative verb-first patterns (get_coin, search_coins, validate_claim) with noun-phrase labels (bet_research, entity_profile, pipeworx_trending, polymarket_edge_tracker) and inconsistent prefixes. Snake_case is consistent, but the naming grammar and verb styles are not.

Tool Count2/5

35 tools is well past the comfortable range, and the count feels inflated by duplicate routing modes, overlapping polymarket scanners, and generic memory/meta utilities. A server nominally named Coingecko carries only 4 crypto tools while the overwhelming majority belong to unrelated domains.

Completeness2/5

As a CoinGecko server, the surface is severely incomplete: search/get/market/trending exist but historical prices, OHLC, exchanges, categories, and coin details are missing. As a broader data/prediction-market utility it is more expansive, but the lack of a coherent domain makes coverage impossible to assess as a single product.

Resources