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nik-kale

pangram-editorial

by nik-kale
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
PANGRAM_API_KEYYesYour Pangram API key for accessing the attribution APIs.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
pangram_attribution_auditB

Analyze text for AI attribution using Pangram's attribution analysis API.

This tool provides transparency analysis for AI-assisted content, supporting editorial review and quality assurance workflows. It identifies authorship patterns and provides segment-level attribution breakdowns.

Analysis includes:

  • Overall authorship assessment

  • Average and maximum AI attribution scores

  • Breakdown of authorship patterns by segment

  • Segment-by-segment attribution analysis with confidence levels

Args:

  • text (string): The content to analyze. Must be at least 50 words for accurate analysis.

  • response_format ('markdown' | 'json'): Output format (default: 'markdown')

Returns:

  • Markdown: Formatted transparency report with authorship assessment and segment analysis

  • JSON: Structured data with all attribution metrics

Use cases:

  • Editorial review before publication

  • Transparency audits for AI-assisted content

  • Quality assurance for professional writing workflows

  • Attribution documentation for compliance requirements

Requires: PANGRAM_API_KEY environment variable to be set

pangram_quick_snapshotA

Get a quick attribution snapshot for editorial review.

Returns essential metrics for fast iteration during the writing and editing process. Use this for rapid checks when detailed segment analysis isn't needed.

Args:

  • text (string): The content to analyze. Must be at least 50 words.

Returns: A brief summary with:

  • Authorship indicator

  • Attribution percentage

  • Review status

Example: "Quick snapshot for this paragraph" → "✅ PRIMARY AUTHOR - 12.3% AI attribution"

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.8/5.0

Scored across 2 tools

Disambiguation5/5

Both tools analyze AI attribution but serve different purposes: one provides a comprehensive audit with segment-level breakdowns, the other gives a quick summary. Their descriptions clearly distinguish them, so no ambiguity.

Naming Consistency4/5

Both tools use the 'pangram_' prefix and snake_case, but one uses 'attribution_audit' (noun_noun) and the other 'quick_snapshot' (adjective_noun). This is a minor inconsistency; otherwise, naming is predictable.

Tool Count3/5

The server has only 2 tools, which feels thin for a domain that could benefit from additional tools like batch analysis or report generation. However, the two levels of detail (quick and comprehensive) are well-scoped for its purpose.

Completeness4/5

For the narrow domain of AI attribution analysis, the tools cover quick and detailed analysis. There are no obvious missing operations, as it's a pure analysis service. Minor gap: no batch or comparison feature.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources