Strip an email body down to what was actually written: removes quoted replies, signatures, mobile footers, tracking params, unsubscribe lines, and invisible characters.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Email body (plain text or markdown) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It clearly lists the transformations performed (removes quoted replies, signatures, mobile footers, tracking params, unsubscribe lines, invisible characters) and the outcome ('down to what was actually written'). However, it does not disclose any limitations or edge cases, such as behavior with non-text formats, but the explicit list provides solid transparency.
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, well-structured sentence that front-loads the primary action ('Strip an email body down...') and then enumerates the specific removals, achieving high informational density with zero wasted words. It is concise and immediately readable.
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 tool with one straightforward parameter and no output schema, the description sufficiently explains the tool's function and scope. It does not explicitly state the return value, but 'strip down' implies the cleaned text is returned. Given the simplicity, the description is complete enough for an agent to understand and invoke the tool appropriately.
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?
The input schema provides 100% coverage for the single parameter 'text' with a clear description. The tool description does not add any additional meaning beyond the schema, but since the schema already fully documents the parameter, the baseline of 3 is appropriate. There is no extra nuance, but none is needed for this simple parameter.
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 uses a specific verb 'Strip' with a clear resource 'an email body' and enumerates exactly what is removed (quoted replies, signatures, etc.), making the tool's purpose unambiguous and distinct from the sibling tools, none of which offer similar email-specific cleaning functionality.
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 clearly implies use when an email body needs cleanup, but it does not explicitly state when to prefer this over alternatives or any exclusions. There is no guidance on when not to use it, such as for preserving formatting or handling HTML emails. Still, the purpose is specific enough that usage context is inferred.
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.
Most tools are clearly distinct, but there is overlap between local ops tools (gbrain_get/list/search, host_memory_get/set, forge_loop_status, list_mesh_snapshot, etc.) and public status/product tools (forge_status, popular_tools) which could confuse an agent. Descriptions help by tagging local ops, but the boundaries remain blurred.
All tool names use snake_case and mostly follow a verb_noun or noun_verb pattern (e.g., get_product, list_live_products, csv_to_markdown). A few names like now_iso and lorem_ipsum deviate from the verb-first style, but overall the naming is predictable and consistent.
With 40 tools, the server is overloaded for a coherent set. Many are simple utility functions that could be consolidated, and the mix of generic utilities, product APIs, and local ops adds unnecessary bulk, pushing the count well above the comfortable range.
The server lacks a clear domain, making it impossible to assess lifecycle coverage. While it offers many utilities, there is no coherent surface—missing common operations for any single category (e.g., no CRUD, no file handling) and many tools feel randomly assembled rather than forming a complete workflow.