wayback
Internet Archive snapshots for a URL (not in the model sandbox)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Live URL to look up in the Internet Archive | |
| limit | No | Max CDX snapshots |
Internet Archive snapshots for a URL (not in the model sandbox)
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Live URL to look up in the Internet Archive | |
| limit | No | Max CDX snapshots |
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 provided, the description carries the full burden of behavioral disclosure, but it only says the tool works with Internet Archive snapshots and "not in the model sandbox." It does not disclose expected return shape, failure behavior when no snapshots exist, rate limits, or side effects, leaving the agent with limited behavioral grounding.
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 short and front-loads the core purpose well. The parenthetical "not in the model sandbox" adds some context but is cryptic and could be clearer; otherwise there is no wasted text.
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 two-parameter tool with full schema coverage, the description is minimally adequate. However, there is no output schema and the description does not explain what a snapshot response looks like, how limits behave, or what happens for URLs with no archived captures, so an agent must infer some behavior.
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 schema already documents url and limit. The description adds no extra parameter semantics, but the baseline of 3 applies since no compensation is needed.
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 identifies the tool's domain: Internet Archive snapshots for a given URL. It distinguishes this from current-page tools like read_url or cache_url by noting the Internet Archive specifically, though it lacks an explicit verb like "retrieve" or "list."
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 usage context is implied: if an agent needs archived snapshots from the Internet Archive, this is the tool. However, there is no explicit statement of when to choose it over alternatives or when not to use it, such as when live current content is needed.
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.
Multiple tools have overlapping or identical purposes, such as ocr_url and ocr_image (both OCR from an image URL), compare_texts and text_diff (both compare or diff texts), extract_url and read_url (both extract webpage content), and content_hash and hash_text (both compute hashes). The boundaries between these tools are unclear, causing a high risk of misselection.
Naming conventions are mixed. Many tools use verb_noun (extract_url, validate_email), but others use noun_verb (language_detect, html_clean), single words (advisor, crawl, retrieve), or noun_noun (job_status, page_metadata). This inconsistency makes it harder to predict tool names.
With 100 tools, the server is extremely over-scoped for a generic agent toolkit. While some tools are distinct and useful, the sheer number does not align with a focused purpose; many tools are redundant or highly specialized, and the count exceeds what is typically manageable for an agent to reason about.
The toolkit covers a broad range of utilities including extraction, validation, processing, research, memory, and orchestration. However, there are no CRUD tools for creating/updating/deleting resources, no database or file system operations, and no integration beyond web/API basics. This leaves significant gaps for agents that need general lifecycle management, though it does handle many common tasks.