Infer TypeScript interfaces from a JSON string (or object). Local, no LLM. Caps depth/size for agent safety.
| Name | Required | Description | Default |
|---|---|---|---|
| json | Yes | JSON text to convert (preferred) | |
| root_name | No | Root interface name (default Root) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It adds non-obvious traits: 'Local, no LLM' (no external calls) and 'Caps depth/size' (safety limits), which are helpful for an agent. However, it remains vague about the exact nature of the caps and doesn't mention error behavior or return format.
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: two sentences, front-loaded with the core purpose, and every clause adds value. There is no fluff or repetition.
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 utility with no output schema, the description is reasonably complete: it states the transformation, local execution, and safety caps. It lacks explicit return format or error handling, but these are somewhat implied by 'Infer TypeScript interfaces' and the tool's simplicity. The full schema coverage compensates for missing parameter details.
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 already has 100% coverage of both parameters, so the baseline is 3. The description adds little beyond the schema, except for the phrase '(or object)' which slightly contradicts the schema's string type for 'json'. It does not enrich parameter syntax or provide additional formatting guidance.
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's function with a specific verb ('Infer') and resource ('TypeScript interfaces from a JSON string'), making its purpose unambiguous. It also distinguishes itself from sibling tools, none of which perform JSON-to-TypeScript conversion.
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 provides some context for when to use the tool ('Local, no LLM') implying offline/fast conversion, but it does not explicitly state when not to use it or mention alternatives. The 'Caps depth/size' hint implies limits but lacks clear exclusion criteria.
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.