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Contextualize an outside URL

contextualize_url
Read-onlyIdempotent

Use first when you found an AI or software URL elsewhere (a release note, repo, blog post, tweet) and want to know if it is new: returns New Runtime records that cite that exact URL, related records and patterns, the source's hub, and next steps. Free, no auth. Example: {"url": "https://github.com/modelcontextprotocol/servers", "task": "is this new?"}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe https:// URL you are researching.
taskNoOptional: what you are trying to find out; its words refine related records.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / task / examples
      Added value: +[
      +  "is this new?",
      +  "who else covered this"
      +]
    • addedInput schema / properties / url / examples
      Added value: +[
      +  "https://github.com/modelcontextprotocol/servers"
      +]
  2. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds value by disclosing 'Free, no auth' and describing the output (New Runtime records, related records, patterns, hub, next steps). This goes beyond the structured annotations and helps the agent understand what to expect without contradicting them.

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?

The description is concise and well-structured: it front-loads the usage guidance, then describes the return value, adds cost/auth info, and provides a concrete example. Every sentence earns its place, and the example makes the tool immediately understandable.

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 read-only tool with 2 parameters and no output schema, the description is complete enough: it lists what will be returned (records, patterns, hub, next steps), notes it is free and auth-free, and gives a clear example. Minor gaps like error behavior or rate limits are acceptable given the simplicity and annotation support.

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 both 'url' and 'task' are already documented with descriptions and examples. The tool description offers an example JSON usage, but it doesn't add significant semantic meaning beyond what the schema already provides. Baseline 3 is appropriate.

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 function: given an external AI/software URL, it returns New Runtime records, related records/patterns, the source's hub, and next steps. The specific verb 'returns' and the explicit resource make the purpose concrete, and the 'Use first when...' opening positions it distinctly from siblings like search_archive or get_record.

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 explicitly says 'Use first when you found an AI or software URL elsewhere' and gives example contexts (release note, repo, blog post, tweet). This is clear when-to-use guidance. It does not name specific alternatives or exclusions, but the context is unambiguous enough for an agent to select it over 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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