AgentRead

by dev.workers.toolschemaforge.agent-read

Read URL

read_url

Read a public web page or document and return clean, LLM-ready markdown. Pricing: $0.001 (1 sat) per read, or $0.003 (3 sats) when scan is true. The free tier applies first, shared with the HTTP API and all MCP tools.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "read_url"
  }
}

Input Schema

Name	Required	Description
`url`	Yes	Public http(s) URL of the document to read.
`scan`	No	Also scan the converted document for prompt-injection risk.
`max_tokens`	No	Optional output token budget.

Output Schema

Name	Required	Description	Default
`scan`	No
`title`	Yes
`markdown`	Yes
`truncated`	Yes
`source_url`	Yes
`tokens_est`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 26, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations, the description carries full burden; it does disclose cost ($0.001/read, $0.003 with scan) and free-tier sharing, which is valuable operational context. However it omits auth/access constraints for private URLs, failure behavior, and truncation semantics for max_tokens.

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?

Purpose is front-loaded in the first sentence, with pricing as supporting detail; nothing is padded beyond what's useful. Slightly dense but every clause carries operational information.

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?

Output schema exists so return values needn't be explained, and params are fully covered in schema. The main gap is that a read tool with no annotations should say more about URL accessibility limits and error behavior.

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?

Schema coverage is 100% so all three params are documented, but the description still adds meaning beyond the schema by tying the scan=true cost premium to a specific parameter. This is genuine value-add over the schema description.

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?

States a specific verb and resource ('Read a public web page or document') plus the output format ('clean, LLM-ready markdown'). It doesn't explicitly distinguish itself from the sibling scan_text, but the 'public web page' scope is clear enough for selection.

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?

Implied usage (public pages/documents) and mentions 'scan is true' as an option, but never states when to prefer this over scan_text or what to do with a non-public URL. The agent must infer selection criteria rather than being told.

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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