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Glama

SendIt

by io.github.Shree-git

Repurpose URL Content

repurpose_url

Takes a URL (article, YouTube video, or podcast) and repurposes its content into platform-optimized posts.

Supports: • Web articles — extracts text content • YouTube videos — extracts transcript/captions • Podcast RSS feeds — extracts episode descriptions and show notes

Returns platform-specific formatted output (X threads, Instagram carousel slides, Medium articles, etc.) with a quality score (0-100) for each variant.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesSource URL to repurpose (article, YouTube video, or podcast RSS feed)
toneNoOptional tone override (e.g., 'professional', 'casual', 'witty')
brand_voice_idNoOptional brand voice profile ID to apply
target_platformsYesTarget platforms to generate content for (e.g., ['x', 'linkedin', 'instagram'])

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

The description adds behavioral detail beyond annotations by explaining extraction methods (text, transcripts, captions, show notes) and the return structure (platform-specific variants with quality score). Annotations already declare non-read-only and open-world, so the description's extra context about what it does is valuable. It does not mention potential latency or URL accessibility issues, but it is otherwise transparent.

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, using bullet points for supported source types. Every sentence is informative, and there is no filler or redundancy. It fronts the main action and immediately lists supported formats.

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?

The description covers input types, supported sources, output platforms, and the quality score, which is adequate for a tool of this complexity. Combined with the annotations and schema, it gives a solid understanding. It could mention error handling or review requirements, but it is largely complete.

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?

The input schema covers all 4 parameters with descriptions (100% coverage). The description reinforces 'url' and 'target_platforms' but adds no new parameter-specific information, such as formatting details for tone or brand_voice_id. Baseline 3 is appropriate because the schema does the heavy lifting.

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 uses specific verbs and resources: 'repurposes its content into platform-optimized posts.' It clearly identifies the input types (articles, YouTube, podcasts) and output types (X threads, Instagram carousel, Medium articles), distinguishing it from siblings like generate_content that likely create from scratch.

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

Usage Guidelines3/5

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

The description implies usage when you have a URL to repurpose, but it does not explicitly state when to use this tool over alternatives like generate_content or generate_post_bundle, nor does it mention any exclusions. It lacks explicit 'when to use' or 'when not to use' guidance.

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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TDQS

B3/5.0
Disambiguation2/5

With 148 tools, there is significant overlap. For example, generate_content, publish_ai, generate_post_bundle, and request_project_content all generate content; get_analytics, get_unified_analytics, get_post_analytics, get_ad_performance, and get_unified_ad_report all fetch performance metrics; and list_inbox vs list_conversations blur comment and conversation management. Descriptions help, but boundaries between tools are often unclear.

Naming Consistency3/5

Most tools follow a verb_noun pattern (e.g., list_teams, create_goal, delete_post), but there are notable deviations: create_library_item vs save_to_library, publish_content vs publish_ai, schedule_content vs schedule_content_advanced, and connect_platform vs connect_connector. Mixed prefixes like 'autopilot_', 'check_', and 'get_' are fine, but overlapping verbs and a hyphen in 'connect_linkedin-page' reduce consistency.

Tool Count1/5

148 tools is extreme for any server. Even for a broad social media management platform, this is far beyond what an agent can effectively navigate. The count is unwieldy and suggests the surface should be split into multiple focused servers (publishing, analytics, connectors, workflows, etc.).

Completeness3/5

The core social publishing workflow is well covered (create, schedule, publish, edit, delete, retry), and there are extensive features for analytics, workflows, connectors, and AI agents. However, some resources have CRUD gaps: no update/delete for brand voices, no delete_project, no update/delete for Product Hunt goals, and no explicit get_workflow. These are workable but notable omissions.

Resources