iReader MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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
| Capability | Details |
|---|---|
| tools | {} |
| logging | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_webpage_markdownB | Fetch the content of a url using jina reader |
| get_youtube_transcriptC | Fetch the transcript of a YouTube video |
| get_tweet_threadC | Fetch the thread of a tweet |
| get_pdfC | Extract text content from a PDF file |
| get_public_google_doc_markdownB | Fetch the markdown content of a public Google Doc by URL |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Application Logs |
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose targeting a specific content source and format: PDF extraction, Google Doc fetching, tweet thread retrieval, webpage content via Jina Reader, and YouTube transcript fetching. There is no overlap in functionality, and an agent can easily distinguish between them based on the source type and output format.
All tool names follow a consistent verb_noun pattern with 'get_' as the prefix, followed by a descriptive noun phrase (e.g., get_pdf, get_public_google_doc_markdown). This uniformity makes the tool set predictable and easy to understand, with no deviations in naming style.
With 5 tools, the server is well-scoped for its purpose of content extraction from various sources. Each tool serves a distinct and necessary function, covering key content types (PDF, Google Docs, tweets, webpages, YouTube) without being overly broad or sparse, fitting typically within the 3-15 tool range for such a domain.
The tool set covers a broad range of common content sources (PDF, Google Docs, tweets, webpages, YouTube) with clear extraction capabilities, leaving no obvious dead ends. A minor gap might be the lack of tools for other formats like Word documents or private Google Docs, but the existing coverage is sufficient for most agent workflows in this domain.