Fetch 1-20 URLs and get clean markdown per page, truncated to a character cap. Price: $0.0015/URL
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | URLs to fetch (1-20) | |
| timeout | No | Per-URL timeout in ms (default 10000) | |
| maxChars | No | Per-page content character cap (default 20000) |
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 burden. It discloses output format (clean markdown), truncation behavior (character cap), and cost (price per URL), which are meaningful behavioral traits. However, it omits error handling, redirect behavior, and what 'clean' means in edge cases, so it is not exhaustive.
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 a single, front-loaded sentence that states the action, constraints, output, and pricing. Every word earns its place with no 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 read-only fetch tool, the description covers the core semantics: batch size, output format, and truncation. It lacks details on failures or partial results, but given the simplicity and presence of a full schema, it is sufficiently complete for an agent to invoke correctly in most cases.
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 coverage is 100%, so the baseline is 3. The description mentions '1-20 URLs' and 'character cap,' but these are already described in the schema (urls and maxChars). It adds no new parameter-level meaning beyond restating schema constraints.
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 tool fetches 1-20 URLs and returns clean markdown per page, truncated to a character cap. This specific verb+resource+output combination distinguishes it from siblings like fetch_webpage (likely single URL) and batch_fetch (may not produce markdown).
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 batch-fetching multiple URLs into markdown but does not explicitly contrast it with alternatives like fetch_webpage or batch_fetch. No 'use this when' or 'instead of' guidance is provided, leaving the decision to inference.
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.