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Gong

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?

Annotations (readOnlyHint, destructiveHint false, idempotentHint) are fully consistent with description's 'Fetches the page, extracts...' and non-destructive nature. Description adds details about output format and placement 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?

Single, well-organized paragraph: purpose first, then process, output details, use cases. Every sentence adds value with no redundancy.

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?

Adequately covers purpose, process, output format, and use cases for a tool with only two parameters. Could mention error handling or redirect behavior, but sufficient for typical usage.

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 description coverage is 100%. Description adds example values for url and clarifies default/max for max_links, enriching the schema's descriptions slightly.

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?

Description clearly states the tool generates an llms.txt file, details the process (fetch, extract, emit), and specifies the output format and location. Distinguishes from siblings through unique functionality.

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 (indexing client sites, drafting own, auditing competitors), providing clear context for when to use. No exclusion criteria but sufficient for the tool's simplicity.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes due to detailed descriptions, but there is potential confusion among the many Polymarket and pipeworx-related tools. The Gong-specific tools are clearly separated.

Naming Consistency3/5

Naming conventions are mixed: some use snake_case, others camelCase, and there is inconsistency between groups (e.g., gong_* vs. polymarket_*). However, within each subgroup, naming is consistent.

Tool Count2/5

35 tools is high for coherence. The server covers multiple domains (Gong calls, data research, betting), leading to an overloaded toolset that could be streamlined into fewer, more general tools.

Completeness4/5

The toolset is comprehensive for its intended use cases, covering Gong call management, a wide array of data lookups, and Polymarket betting analysis. Minor gaps exist, such as limited CRM features beyond calls.

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