translate_text
Translate text.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| source_lang | No | auto | |
| target_lang | Yes |
Translate text.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| source_lang | No | auto | |
| target_lang | 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?
There are no annotations, so the description carries the full burden of disclosing behavior. 'Translate text.' reveals nothing about input requirements, output format, error handling, or side effects (e.g., network calls, cost). It is essentially a tautology of the tool name, providing zero additional behavioral context.
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 short ('Translate text.'), which is concise, but it under-specifies the tool. It is not simply concise; it is skeletal. There is no structural breakdown or additional sentences to provide useful context, making it more an absence of specification than effective conciseness.
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's moderate complexity (3 parameters, no output schema, no annotations), the description is completely inadequate. It does not explain return values, parameter constraints, or typical use cases. The agent would have to guess or rely on external knowledge to invoke this tool correctly.
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%, and the description adds no meaning to the parameters. The schema lists text, source_lang, and target_lang, but the description does not explain their roles, defaults, or allowed values. With three parameters, the description should compensate for the schema's lack of documentation, but it entirely fails to do so.
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 'Translate text.' clearly identifies the action (translate) and resource (text), which is sufficient to convey the basic purpose. However, it does not distinguish itself from potential alternative translation tools or clarify scope (e.g., language pairs, auto-detection), which would elevate it to a 5.
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 usage guidance is provided. The description does not indicate when to prefer this tool over alternatives (e.g., language_detect for detection, summarize_text for summaries) or mention any prerequisites such as API keys or rate limits. This leaves the agent without contextual help for tool selection.
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.