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Mfapi India

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

A4.2/5.0
Behavior4/5

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

Description adds behavioral details beyond annotations: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This explains the non-destructive, read-only process. No contradictions with annotations (readOnlyHint=true, destructiveHint=false).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (under 100 words) and front-loaded with the main action. It efficiently covers purpose, process, and use cases without redundant sentences. Slightly verbose but acceptable.

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?

Despite no output schema, the description fully explains the output format: 'standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt.' It covers process, inputs, and expected result comprehensively for a simple 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 description coverage is 100%, so the description adds minimal value beyond what schema already provides. The description mentions 'fetches the page... extracts title/description/key links,' which aligns with the url parameter but does not add new semantics. No additional depth is provided.

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's purpose: 'Generate a production-ready llms.txt file for any URL'. It specifies the exact resource (llms.txt) and action (generate), differentiating it from sibling tools like scan_competitor_ai_presence by focusing on the output file format.

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 lists explicit 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 provides clear context for when to use the tool, though it does not explicitly mention when not to use it or compare to alternatives.

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

A4.4/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose. The various `ask_pipeworx*` variants are differentiated by mode (single vs grounded vs research). `get_nav_history` vs `latest_nav` serve different query granularities. Administrative tools like `remember`/`recall`/`forget` are clearly separate. No two tools overlap in functionality.

Naming Consistency4/5

Tool names follow a consistent snake_case convention and generally use `verb_noun` order (e.g., `ask_pipeworx`, `search_schemes`, `validate_claim`). A few exceptions like `entity_profile` (noun_verb) exist, but the pattern is mostly predictable.

Tool Count3/5

At 34 tools, the server is quite large, including many specialized tools (e.g., multiple Polymarket tools, administrative memory/subscription tools) that could arguably be split into separate servers. The number feels slightly excessive for a coherent, focused server.

Completeness5/5

The tool set covers an extraordinarily wide range of domains: company financials, SEC filings, FDA drugs, economic data, mutual funds, real estate, prediction markets, npm dependencies, AI visibility, and more. It also includes memory, subscription, and feedback mechanisms. There are no obvious gaps for the domains addressed.

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