read
Any public URL as clean Markdown
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
Any public URL as clean Markdown
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It does not state whether the tool modifies anything (destructive behavior), requires authentication, has rate limits, handles errors for non-accessible URLs, or what happens with dynamic content. 'Clean Markdown' is vague and could mean different things.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at 5 words, with no wasted text. It front-loads the core purpose immediately. Every word serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 1 parameter, no output schema, and no annotations, the description is incomplete. It doesn't describe error behavior, output structure, or limitations (e.g., supported content types, size limits). For a simple tool, more context is needed to ensure correct agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema provides no parameter descriptions. The description does not add meaning to the 'url' parameter beyond its name and type. It doesn't specify URL format requirements (e.g., must include http/https), max length, or accepted protocols.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Any public URL as clean Markdown' clearly states the verb (read/convert) and resource (public URL), and the output format (clean Markdown). It distinguishes itself from sibling tools like 'screenshot' and 'url_safety' by implying a text extraction function rather than image capture or security checking.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for converting public URLs to Markdown, but provides no guidance on when not to use it (e.g., for private/internal URLs, or when to use 'screenshot' for visual content). No exclusions or alternatives are mentioned beyond what sibling names suggest.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes: financial data tools (calendar, candles, earnings, etc.) are separate from web scraping tools (read, screenshot, unlock, etc.). However, there is some overlap between 'history' (daily closes + range change) and 'candles' (OHLCV), which could cause minor confusion.
Tool names are inconsistent: most are single-word nouns (e.g., 'calendar', 'candles') but some are verbs ('read', 'search', 'unlock') and one uses an underscore ('url_safety'). There is no uniform verb_noun or noun pattern, making it harder for an agent to predict tool names.
With 16 tools, the server is slightly above the ideal range of 3-15, but each tool appears to serve a distinct purpose. The count is manageable given the two domains (financial data and web scraping).
The financial data tools cover many common data points (economic releases, price history, filings, earnings) but miss order books, real-time trades, or news. The web scraping tools are comprehensive for fetching content but lack structured data extraction. The feedback tool suggests the server is still evolving, indicating notable gaps.