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Ietf Datatracker

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/idempotentHint/destructiveHint, and the description adds useful behavioral details: fetches the page, extracts title/description/key links, and emits a single text blob in standard markdown. This goes beyond the annotations without contradicting them.

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 no waste: purpose, process, and use cases. Front-loaded with the action and resource, making it immediately scannable.

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 exactly what the output is (a text blob in llms.txt format) and when to use it. Annotations cover the safety profile, leaving no critical gaps for a two-parameter tool.

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% and both parameters already have descriptive text. The description adds no new parameter-specific meaning, so the baseline 3 applies.

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 starts with a specific verb+resource: 'Generate a production-ready llms.txt file for any URL'. It clearly distinguishes from sibling tools like scan_competitor_ai_presence and ai_visibility_check by focusing on llms.txt creation.

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 real-world use cases: getting a client's site indexed, drafting your own llms.txt, and auditing a competitor. It doesn't name alternatives or exclusions, but the context is clear and sufficient for an agent to decide when to use it.

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

B3.4/5.0
Disambiguation2/5

Several tools have overlapping or poorly distinguished purposes. For instance, `ask_pipeworx` and `ask_pipeworx_beta` have nearly identical descriptions, and `ask_pipeworx_grounded` also shares the same routing but adds a different output format. The `ai_visibility_check` and `scan_competitor_ai_presence` tools also overlap significantly.

Naming Consistency3/5

There is some consistency with verb_noun patterns (e.g., `resolve_entity`, `search_within`, `subscribe`, `unsubscribe`). However, there are many deviations: `ask_pipeworx`, `pipeworx_feedback`, `pipeworx_trending`, `entity_profile`, `scan_dependency`, and `polymarket_edges` break the pattern, mixing descriptive names with non-standard prefixes.

Tool Count4/5

37 tools is slightly above the ideal range for a single MCP server, but the tools cover a very broad and varied domain (IETF data, company research, prediction markets, package scanning, memory, etc.). The count is high but still within a manageable scope for a multi-purpose utility server.

Completeness2/5

The server combines tools from two very different domains: IETF Datatracker (document/WG/person lookups) and Pipeworx (data retrieval, prediction markets, company analysis). The IETF-related tools are sparse and incomplete (only document search, document, person, wg, wgs_search, rfc are present—no ability to create or modify records). The Pipeworx side is extensive but leaves notable gaps (e.g., no tool for submitting comments or editing IETF documents).

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