A
licenseA
qualityB
maintenanceEnables coding agents to offload reading and summarizing multiple large files to a cheaper worker LLM, keeping full file contents out of the main agent's context.
1
14 npm
1
MIT
LLM Delegator
by shangzhihao
Related Servers
Alternatives to LLM Delegator
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityCmaintenanceEnables coding agents to delegate file reads, command output triage, page fetching, and image inspection to cheap flash models, returning concise answers and verified pointers while keeping raw dumps out of the main model's context.MIT
- AlicenseNot gradedqualityBmaintenanceDelegates large file reads to a cheap worker model so coding agents avoid consuming context, with optional hook enforcement and per-call token savings.MIT
- AlicenseNot gradedqualityAmaintenanceEnables an MCP client to offload bounded tasks such as code review, boilerplate generation, commit messages, and format conversion to a local or cloud LLM, keeping those files out of the main model's context window to cut token costs. It also provides a cheap second model for reviewing code and drafting content while the primary model handles planning and tool use.MIT
- AlicenseAqualityBmaintenanceLets an AI coding agent offload low-complexity, monotonous tasks to a cheap local or OpenAI-compatible model that runs its own server-side tool loop for reading, searching, and staging file writes, so large file contents and intermediate steps never enter the caller's context. Also provides summaries, extraction, classification, bulk file mapping, staged-diff review, model management, and session token-savings reporting.42257 npmMIT
- AlicenseNot gradedqualityBmaintenanceEnables Claude Code to delegate bulk, read-heavy file analysis to a local DeepSeek Harness agent, keeping file contents out of the conversation context.2MIT
TDQS
A3.6/5.0
Scored across 1 tool
Disambiguation5/5
With only one tool, there is no possibility of misselection or overlapping purpose. The single tool's intent (delegate a bounded task to a model) is unambiguous.
Naming Consistency5/5
The lone name 'delegate_task' follows a clean verb_noun snake_case convention. There is no opportunity for inconsistency within a single-tool surface.
Tool Count3/5
One tool is thin for any server, even a narrowly scoped delegation service; it earns its place but leaves the surface feeling minimal. A few companion operations (e.g. listing available models or checking delegation status) would round it out.
Completeness4/5
For a task-delegation domain, a single call that submits a task and returns the result covers the core lifecycle. Minor gaps exist around discovering available models (flash/pro are referenced but not enumerable) and no status/cancel path, though these are workable for a synchronous delegation model.
Maintenance
ActivitySlowing
ResponsivenessNo issues