ai.plith/plith

skills: {'id': 'dedupq_check', 'name': 'dedupq_check', 'description': 'Before executing any LLM task, check if an identical or semantically similar task has already been completed. Returns cached result on hit, saving one LLM call. On a miss, execute your task and call dedupq_complete to cache the result for future hits. Costs 1 credit.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'dedupq_complete', 'name': 'dedupq_complete', 'description': 'After executing a task, store the result so future identical or similar tasks return a cache hit via dedupq_check. Costs 2 credits.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'burnrate_estimate', 'name': 'burnrate_estimate', 'description': 'Before executing a multi-step agent plan, estimate the total LLM cost. Returns per-step breakdown and optimization suggestions. If the estimate exceeds your budget, pipe the same plan into burnrate_optimize. Costs 1 credit.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'burnrate_track', 'name': 'burnrate_track', 'description': 'Log the actual cost of an LLM call after execution. Call this after every LLM request to build calibration data that improves burnrate_estimate accuracy over time. Free — no credits charged. Returns the recorded cost entry with computed margin versus the prior estimate when one exists for this model and token range.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'burnrate_optimize', 'name': 'burnrate_optimize', 'description': 'Get a cheaper equivalent plan by substituting models with lower-cost alternatives. Call after burnrate_estimate if the estimated cost exceeds your budget. Returns the optimized plan with substituted models, new per-step costs, total savings, and whether the target_budget is met. Optionally set target_budget to constrain the optimization. Costs 1 credit.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'burnrate_budget', 'name': 'burnrate_budget', 'description': "Get today's tracked LLM spend, per-model breakdown, projection, and budget alerts. Free — no credits charged.", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'qualitygate_validate', 'name': 'qualitygate_validate', 'description': 'After your agent generates output, validate it against your rules before shipping. Runs deterministic checks (regex, JSON schema, syntax) plus optional LLM-powered tone and factual analysis. Returns a structured verdict (pass, warn, or fail) with a 0-100 score and per-check issue details. Use qualitygate_trends to spot recurring failure patterns over time. Variable cost: 1 credit per deterministic check, 8 credits per LLM check.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'guardrail_check', 'name': 'guardrail_check', 'description': 'Evaluate a proposed agent action against your governance policies. Returns allow or deny with the matched policy reason. Requires at least one active policy created via guardrail_create_policy. Deterministic rule evaluation — no LLM. Costs 1 credit.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}; uptime_30d 1.0%; p95 775.7ms; conformance: pass

How to connect

https://plith.ai/api/mcp

curl -X POST https://plith.ai/api/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}'