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Opedd — Licensed Content for AI

by com.opedd
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get_content

Retrieve the full body of a licensed article using a buyer API token (opedd_buyer_live_* canonical; opedd_buyer_test_* for sandbox). Requires OPEDD_BUYER_TOKEN env var (create one at opedd.com/licenses after purchasing). Works for per-article Human republication licences (token scoped to that article) and licence orders: AI answers monthly and client display return full text; AI answers pay-per-request always returns a snippet (up to 300 words or 25% of the article, whatever delivery_mode is asked). Articles the publisher stopped licensing answer 403 ARTICLE_EXCLUDED. The publisher must have content delivery enabled and must have pushed content for the article. Phase 11 M2 RAG-extended shape: response includes 7 RAG-essential metadata fields — author, language, word_count, content_hash, image_urls, canonical_url, tags. On pre-2026-05-14 historical articles, optional fields (author/language/image_urls/canonical_url/tags) may be NULL. NULL means 'data unavailable for this article', NOT 'explicitly empty' — treat as data-missing when filtering; do not interpret as anti-match.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
article_idYesThe Opedd article UUID to retrieve content for
buyer_tokenNoBuyer API token (opedd_buyer_live_* or opedd_buyer_test_*). Falls back to OPEDD_BUYER_TOKEN env var.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden, and it excels: it discloses token environments, env var requirements, license-specific response behavior, 403 ARTICLE_EXCLUDED, publisher content-delivery prerequisites, RAG metadata fields, and nuanced NULL semantics. This is far beyond a generic 'get' phrase.

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 long but every sentence carries operational weight, from error handling to NULL interpretation. It could be lightly reorganized for scanning, but details are not padded or redundant.

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?

Given there is no output schema, the description compensates thoroughly: it explains response shape via RAG metadata fields, snippet truncation rules, error semantics, and historical NULL behavior. An agent can correctly interpret and filter results without needing additional documentation.

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 the baseline is 3, but the description adds real value: canonical live/test token formats, env var fallback, and license scoping for tokens. It improves the agent's ability to supply the right token without merely restating schema text.

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 a specific verb and resource: 'Retrieve the full body of a licensed article' using a buyer API token. It clearly articulates scope and even distinguishes full-text vs. snippet behavior by license type, which separates it from search or lookup siblings.

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?

Provides clear context on when full text is returned (per-article Human republication, AI monthly, client display) and when a snippet is returned (AI answers pay-per-request), plus the 403 error case. It does not explicitly name sibling tools as alternatives, so it stops short of full when/when-not routing.

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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