Fetch and convert a webpage to clean markdown. Fast, no JavaScript rendering. Price: $0.002
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to fetch | |
| cache | No | Serve from cache when available (default true; cached responses cost 70% less) | |
| timeout | No | Request timeout in ms (default 10000) | |
| cacheTtl | No | Cache TTL in seconds (default 3600) |
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?
With no annotations, the description carries the full transparency burden. It does disclose a key behavioral trait: 'no JavaScript rendering', which sets expectations for dynamic pages. However, it does not mention caching behavior, error handling, or rate limits, which are relevant for a fetch tool. The cache parameter exists in the schema but is not mentioned in the description.
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, front-loading the core purpose in the first sentence. The second sentence adds a key limitation ('no JavaScript rendering') and cost information. Every clause earns its place without redundancy.
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?
For a simple fetch tool, the description is adequate but not complete. It explains what the tool does and a key limitation, and the schema covers parameters. However, given the large number of sibling tools (batch_fetch, fetch_resilient, fetch_webpage_pro), the description lacks enough contextual differentiation to help an agent choose correctly. No output schema exists, but the description's mention of 'clean markdown' partially covers return format.
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 100%, so the input schema already documents all four parameters clearly. The description adds no parameter-specific semantics beyond what the schema provides. The cost note ('Price: $0.002') is not parameter-related. Baseline 3 is appropriate since the schema handles parameter meaning.
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 clearly states the action ('Fetch') and the resource ('a webpage') and the output format ('clean markdown'). It is specific about the conversion, but it does not explicitly distinguish itself from sibling tools like fetch_webpage_pro or batch_fetch, which is a minor gap.
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 provides no explicit guidance on when to use this tool versus alternatives. Phrases like 'Fast, no JavaScript rendering' imply suitability for simple static pages, but there is no stated when-not-to-use or comparison with sibling tools such as fetch_resilient or smart_extract.
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.
Several tool clusters have near-overlapping purposes: fetch_webpage/fetch_webpage_pro/fetch_resilient and batch_fetch/get_contents are hard to distinguish, and answer_question/research/deep_research differ mainly in price and depth. The search_* and intel_* families are clearer, but the core fetching and research overlap creates ambiguity.
Most tools follow a verb_noun snake_case pattern (fetch_webpage, search_web, extract_data), but there are notable exceptions like domain_intel, package_intel, youtube_transcript, memory_set, and intel_company, where the prefix/suffix convention is inconsistent. Still, the naming is broadly readable.
35 tools is a large surface, far beyond the typical 3-15 range. The server covers many research verticals, but the number feels bloated, especially with multiple fetch and research variants that could be consolidated.
The tool set covers a wide range of web research needs: searching, fetching, crawling, extracting, screenshots, domain/tech/package intelligence, and market/competitive analysis. It lacks obvious lifecycle operations for monitors (list/delete/update) and memory (get/delete), but core workflows are well covered.