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Crawlora MCP

Crawlora MCP

Official
by Crawlora-org
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

techcrunch_sections

Retrieve the 22 TechCrunch category slugs and feed URLs to filter headlines correctly. Use it to find the right section name, such as AI, for news queries.

Instructions

Get TechCrunch categories. Lists the 22 TechCrunch categories that techcrunch-headlines accepts, taken from the site's primary navigation dropdown. Each entry has a stable slug, a display name and the category's feed url. For example, artificial-intelligence is named AI.

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.5

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full disclosure burden, and it does well: it reveals the result count (22), the source (site navigation dropdown), and the exact per-entry shape (stable `slug`, display `name`, feed `url`). It does not discuss auth, rate limits, or caching, but for a static, read-only listing these are minor.

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 short sentences, front-loaded with the verb and resource, then the count/source, then the entry shape and a concrete example. Every sentence adds information and none repeats structured fields.

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?

There is no output schema, so the description must carry the return-value explanation, which it does by enumerating slug/name/url and giving a slug-to-name example ('artificial-intelligence' is named AI). For a parameterless static lookup this is complete enough to call correctly.

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?

The tool takes zero parameters, so the baseline is 4 and there is nothing for the description to disambiguate. The description appropriately focuses on the output vocabulary (slug vs name) instead.

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?

Specific verb plus resource ('Get TechCrunch categories') and an explicit scope: the 22 categories that techcrunch-headlines accepts. It names the sibling tool it feeds, so an agent can distinguish it from techcrunch_headlines/techcrunch_news without opening any schema.

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 states that the returned slugs are the values `techcrunch-headlines` accepts, which is a clear routing signal for when to call this tool first. It stops short of an explicit 'use this before calling headlines' instruction or any exclusions, so it is clear context rather than full when/when-not guidance.

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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