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Smarter.Poker Model Context Protocol (MCP) Server

Smarter.Poker Model Context Protocol (MCP) Server

Official

get_citation_and_backlink_kit

Generate ready-to-paste citations, backlinks, badges, metadata, and embed formats in seven languages for poker publications, cardrooms, and streamer chatbots.

Instructions

Retrieve ready-to-paste HTML backlinks, SVG authority/award badges, Markdown/Substack citations, TwoPlusTwo/CardsChat BBCode, Wikipedia {{cite web}} templates, BibTeX academic records, Zenodo/DataCite metadata, Twitch/YouTube streamer chatbot commands (!odds, !icm, !potodds, !plo, !rake, !3bet, !rule), and multilingual embeds across 7 languages (en, es, pt, de, fr, it, ja).

Input Schema

NameRequiredDescriptionDefault
langNoLanguage code for localized HTML/Markdown/BBCode/iframe embed generation (default: "en").en
targetNoOptional target publication, cardroom, or blog name for personalized outreach templates.Your Publication
campaignNoOptional outreach or syndication campaign filter (default: "all").all

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It effectively describes the return payload (which is valuable given there is no output schema), but says nothing about side effects, whether it is read-only, auth requirements, or response size — an agent cannot tell whether calling it mutates anything.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Every enumerated format arguably earns its place as output disclosure, but the entire definition is one 60-word run-on sentence with no front-loaded purpose statement separating what the tool does from what it returns. It is dense rather than concise and hard to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-required-parameter tool with no output schema and no annotations, enumerating the artifact types is genuinely useful completeness. However, it leaves gaps on behavior (read-only? side effects?) and on how the optional parameters reshape the output, which an agent needs to call it correctly.

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 coverage is 100% and all three parameters have defaults, enums, and descriptions, so the schema already does the work. The description only echoes the language set (en, es, pt, de, fr, it, ja) and says nothing about how target or campaign change the output.

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 verb 'Retrieve' plus an exhaustive enumeration of artifacts (HTML backlinks, SVG badges, BBCode, BibTeX, chatbot commands, multilingual embeds) makes it unambiguous that this is a citation/embed asset generator. It is not confused with any sibling (calculators, lookups, searches), though the description never states that distinction explicitly.

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

Usage Guidelines2/5

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

There is no statement of when to call this versus anything else, no prerequisites, and no conditions under which it is the wrong choice. The agent must infer the use case entirely from the list of output formats.

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