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bch1212

agentfetch-mcp

fetch_url

Fetch any URL and return clean Markdown with token count, metadata, and 6-hour caching. Automatically routes to appropriate fetcher for JS-rendered, PDF, or paywalled content.

Instructions

Fetch any URL and return clean, LLM-ready Markdown with token count, metadata, and 6h caching.

WHEN TO USE:

  • You have a specific URL whose content you need.

  • You want to cap response size to stay inside your context window.

  • You want repeat fetches to be cheap (cache hits ≈ $0.0001).

  • The URL might be JS-rendered, a PDF, or behind a paywall — this tool auto-routes to the right fetcher (Trafilatura → Jina → FireCrawl → PDF).

WHEN NOT TO USE:

  • You don't know which URL to fetch — use search_and_fetch instead.

  • You have many URLs to fetch — use fetch_multiple instead.

Args: url: The URL to fetch. max_tokens: Hard cap on response size. Default unlimited. Pass this if you're tight on context budget — cheaper than over-fetching. format: "markdown" (default — recommended), "text", or "json". use_cache: True returns a cached copy if one exists (≤6h old). Pass False only when freshness matters (live news, prices).

Returns: { "url": str, "success": bool, "markdown": str, "metadata": {title, author, published_date, domain, word_count, token_count, reading_time_seconds, content_type, language}, "cache": {hit, cached_at, expires_at}, "fetch_info": {fetcher_used, fetch_time_ms, cost_credits}, "error": str | None }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
max_tokensNo
formatNomarkdown
use_cacheNo
Behavior5/5

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

No annotations exist, so the description fully bears the transparency burden. It discloses caching (6h), auto-routing to multiple fetchers, cost estimates, and return structure including error handling. This far exceeds minimal requirements.

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 well-structured with sections, bullet points, and a return format sample. While fairly long, every sentence adds value—usage guidance, parameter details, and return schema. Minor conciseness loss from repetition of 'WHEN TO USE' structure.

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 and no annotations, the description compensates fully: explains return structure in detail, caching behavior, fetcher selection logic, cost implications, and context window management (max_tokens). An agent has everything needed to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must add context for all parameters. It explains url (implicit), max_tokens (hard cap, default unlimited, context budget advice), format (default markdown, recommended), and use_cache (default true, when to pass false). Each parameter gets meaningful guidance beyond the schema.

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 opens with 'Fetch any URL and return clean, LLM-ready Markdown' clearly stating the verb+resource+output quality. It explicitly distinguishes from siblings 'search_and_fetch' and 'fetch_multiple' in the WHEN NOT TO USE section.

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

Usage Guidelines5/5

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

The WHEN TO USE and WHEN NOT TO USE sections provide explicit scenarios (e.g., specific URL, JS-rendered, PDF, paywall) and name alternative tools. This gives clear guidance on when to invoke this tool versus its siblings.

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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