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MCPFax Public-Data Utility API

robots.txt / llms.txt

v1_robots
Read-onlyIdempotent

robots.txt / llms.txt: Fetch and parse a site's robots.txt and llms.txt. Source: direct fetch. $0.005 per call · GET /v1/robots

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesAny URL on the target site. Example: 'https://www.cloudflare.com'.

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the source ('direct fetch') and pricing ($0.005 per call), which are useful behavioral context beyond the annotations. It does not mention parsing behavior details or edge cases, but the annotation coverage raises the baseline.

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?

Two sentences plus a compact source/cost/method suffix. Every part earns its place: the title explains what's fetched, the description adds parsing behavior, and the suffix provides source, pricing, and HTTP method without bloat.

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?

For a single-parameter, read-only, idempotent tool with full schema coverage and helpful annotations, the description is nearly complete. It lacks explicit mention of return format or failure behavior, but no output schema is expected and the annotations cover the operation's safety profile. A 4 reflects the minor gap in response semantics.

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%, with the url parameter clearly described as 'Any URL on the target site' and a helpful example. The description adds the overall purpose but no additional parameter-level semantics, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Fetch and parse') and resource ('a site's robots.txt and llms.txt'), clearly distinguishing it from the data-lookup siblings. It doesn't explicitly contrast with a similar tool, but the resource is specific enough that an agent can understand what it does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by noting it fetches and parses robots.txt/llms.txt and takes any site URL, but it gives no explicit guidance on when to prefer this over alternatives or when not to use it. For a niche tool among many data-lookup tools, this minimal context is adequate but not enriched.

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

A3.7/5.0
Disambiguation5/5

Every tool targets a distinct resource or operation—geocoding, weather, DNS, VIN, stock quotes, etc.—with no meaningful overlap in purpose. Even the location- and finance-related tools are clearly separated by their descriptions.

Naming Consistency5/5

All tools follow the same v1_<resource>[_modifier] snake_case pattern, such as v1_air_quality, v1_reverse_geocode, and v1_validate_email. Although the names are not verb-based, the convention is perfectly consistent across all 31 tools.

Tool Count2/5

31 tools exceeds the 25+ threshold and creates a heavy selection burden for agents, even though the server's stated purpose is broad. Many endpoints are small single-purpose lookups that could be grouped into fewer combined tools without losing clarity.

Completeness4/5

As a general public-data utility, the set covers a wide range of common lookup categories: location, weather, finance, legal, health, business, internet, and reference data. It has minor gaps like historical financial time series or phone-number validation, but no obvious dead ends since all tools are self-contained read-only lookups.

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