Loading ToolBench
Exposes the local LFM2.5-1.2B-Instruct model as MCP tools, resources, and prompts. Runs inference on your local LFM2.5-1.2B-Instruct model via the MLX server.
LFM2.5 MCP server exposes 5 tools with explicit FastMCP registrations and visible input schemas. All tools have descriptions and schema definitions, but several critical gaps limit production readiness. Tool naming follows verb_noun convention (chat, summarize, analyze_code, translate). Descriptions are present but lack specificity about when to use each tool vs. others and do not include recovery guidance for errors. Parameters have type annotations and defaults but descriptions are overly brief (avg ~40 chars) and lack constraint details. Output schemas are undocumented, responses are inferred to be strings but structure for multi-turn conversations and error cases is not specified. Error handling relies on exceptions rather than actionable recovery messages. The chat_multi tool accepts a JSON string parameter instead of properly typed array input, forcing LLM-side JSON construction.
Analyze, review, or explain a code snippet using LFM2.5.
Send a message to LFM2.5 and get a response.
Send a multi-turn conversation to LFM2.5 and get a response.
Summarize a piece of text using LFM2.5.
Translate text into a target language using LFM2.5.
chat_multi accepts 'messages' as a JSON string instead of a properly typed array parameter. LLMs must construct and stringify JSON manually, introducing parsing errors and adding unnecessary token overhead.
Output schemas are completely undocumented. All tools return a string, but the structure and format of responses (including error cases, usage metadata, multi-message context) are not specified. LLMs cannot plan downstream actions or extract structured data from responses.
Parameter descriptions lack actionable constraints. E.g., 'temperature: Creativity level (0.0 = deterministic, 1.0 = creative)' does not specify valid range, precision, or what happens if LLM passes 1.5 or -0.1. No enum, min/max, or pattern constraints in schema.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 59 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 39 | - | v1 |
Error handling returns raw exceptions without recovery guidance. If MLX server is unreachable, the LLM sees a connection error but has no guidance to retry, fall back, or inform the user. Retry logic is internal (_complete helper) but not transparent to the agent.
Tool descriptions do not clarify selection criteria. Why call 'chat' vs 'chat_multi'? When should an LLM choose 'analyze_code' over 'chat' for code questions? Descriptions lack comparative context to guide tool selection.
No input validation or parameter sanitization visible. LLMs can pass arbitrarily large 'max_tokens', negative 'temperature', or malformed JSON in 'messages' (chat_multi). Tool should validate and return clear error messages (e.g., 'max_tokens must be 1-2048, got 100000').