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angrysky56
by angrysky56

wiki_fetch_url

Fetch a web URL to extract clean article content as markdown, save it, ingest into the knowledge graph, and archive for research.

Instructions

Fetch a URL with defuddle (clean markdown extraction), save to raw/, ingest into the knowledge graph, and archive to Clippings/.

Use this when researching the web — it strips navigation and clutter, leaving only the article content. Much cleaner than raw web_fetch.

Args: url: The URL to fetch and process. ingest: If True (default), immediately ingest into Neo4j after saving. Set False to save to raw/ only for manual review first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
ingestNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the side effects: fetching a URL, saving to raw/, ingesting into Neo4j, and archiving to Clippings/. It also explains the conditional behavior of the ingest parameter, but it does not mention permissions or reversibility.

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 tight and structured: one pipeline sentence, one usage sentence, then a short Args list. There is some repetition of the ingest behavior in both the pipeline and parameter sections, but it is not padded or confusing.

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?

An output schema exists, so return values are not a missing piece. The description covers the main flows: default full ingestion and ingest=False partial save. Omitting prerequisites like public reachability or the relationship to sibling tools is a minor gap, not a blocking one.

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?

The schema has 0% description coverage, so the description must convey parameter meaning, and it does. 'url: The URL to fetch and process' is sufficient, while 'ingest' explains the default True behavior and explicitly says that setting False saves to raw/ for manual review first. This goes well beyond the schema's bare types and default.

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 clearly states a specific pipeline — 'Fetch a URL with defuddle (clean markdown extraction), save to raw/, ingest into the knowledge graph, and archive to Clippings/' — so the action and resources are concrete. It also contrasts with 'raw web_fetch', helping to differentiate the tool, though that particular alternative is not in the sibling list.

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?

'Use this when researching the web — it strips navigation and clutter' is explicit about the intended use case, and 'Much cleaner than raw web_fetch' names a comparison point. However, it does not say when not to use this tool nor how to route between this and sibling tools like wiki_ingest_raw.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.