dns_lookup
DNS A/MX/TXT lookup
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain name (e.g. example.com) | |
| record_type | No | DNS record type: A, MX, TXT, … | A |
DNS A/MX/TXT lookup
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain name (e.g. example.com) | |
| record_type | No | DNS record type: A, MX, TXT, … | A |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / argsRemoved value: -{
- "description": "Tool arguments",
- "properties": {
- "text": {
- "description": "Primary input text",
- "type": "string"
- }
- },
- "type": "object"
-}Input schema / properties / domainAdded value: +{
+ "description": "Domain name (e.g. example.com)",
+ "type": "string"
+}Input schema / properties / record_typeAdded value: +{
+ "default": "A",
+ "description": "DNS record type: A, MX, TXT, …",
+ "type": "string"
+}Input schema / requiredPrevious value: -[]New value: +[
+ "domain"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention that this triggers a network request, what the output format looks like, or any potential errors (e.g., domain not found, timeout). The description adds no extra context beyond the operation itself.
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 and front-loaded, stating the tool's essence in five words. While it is terse, it is appropriate for a simple lookup tool and avoids unnecessary filler.
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?
The tool has a simple schema but no output schema and no annotations. The description does not explain what the tool returns (e.g., IP addresses, TTL, record list), nor any network-related caveats. For an agent to use the tool effectively, more context is needed.
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% for both parameters (domain and record_type), so the baseline is 3. The description 'A/MX/TXT' partially echoes the record_type parameter's description but adds no meaningful semantics beyond what the schema already provides.
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 'DNS A/MX/TXT lookup' clearly identifies the tool as performing DNS record lookups for specific record types (A, MX, TXT). It distinguishes from sibling tools like ssl_check or validate_domain, though it could be more explicit about the verb ('perform a lookup').
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?
No guidance is provided on when to use this tool versus alternatives (e.g., validate_domain, ssl_check). There is no mention of use cases, prerequisites, or scenarios where other DNS-related tools would be more appropriate.
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.