MCP serverai.plith/plith
AI agent infrastructure: dedup, cost prediction, validation, governance, failure intelligence.
Overview
Score?
UNRATED 0.681
of what a free look can see, on 32 looks
Looks
36
last 12 hr ago
Tools
15
changed 27 days ago
More info
URL
plith.ai/api/mcp
streamable-http
Says it is
plith 1.0.0
protocol 2024-11-05
In the record since
32 days ago
Among servers18,413 with a card
0median 0.606 · this server 0.681 · highest on record 0.8561
Toolsfrom sha256:57e3fe4780…ac1ff2 · +0 −0 27 days ago
| Tool | Schema |
|---|---|
| burnrate_budget Get today's tracked LLM spend, per-model breakdown, projection, and budget alerts. Free — no credits charged. |
input · output |
| burnrate_estimate 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 p |
input · output |
| burnrate_optimize 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 pla |
input · output |
| burnrate_track 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 credit |
input · output |
| dedupq_check 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 |
input · output |
| dedupq_complete After executing a task, store the result so future identical or similar tasks return a cache hit via dedupq_check. Costs 2 credits. |
input · output |
| guardrail_check 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_c |
input · output |
| guardrail_create_policy Create a persistent governance policy that guardrail_check evaluates on every subsequent call. Define rules using and/or/not operators over action types, resource patterns, and bud |
input · output |
| pitfalldb_query Check for known failure patterns before executing a task type. Returns pitfalls with severity, fix suggestions, and confidence scores. After your agent runs, submit failures via pi |
input · output |
| pitfalldb_report Report an agent failure. PII-scrubbed before storage. Linked to existing pitfalls if similar. Free — no credits charged. |
input · output |
| qualitygate_validate 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 |
input · output |
| rigor_execute Execute a structured workflow end-to-end. Call rigor_plan first (free) to preview the step sequence and cost estimate before committing credits. Classifies the task, selects the op |
input · output |
| rigor_plan Before executing a complex task, get a structured workflow plan with per-step cost estimates. Classifies your task, selects the optimal framework sequence, and returns the full pla |
input · output |
| rigor_status Check the status of a running or completed Rigor workflow. Returns progress, step results, and the full deliverable when complete. Use after rigor_execute with polling delivery to |
input · output |
| rigor_workflows List and search Rigor workflows for your organization, with filtering and pagination. Returns status, progress, capacity usage, and available actions per workflow. Use to monitor w |
input · output |
Verify it yourself
npx teppi-check https://plith.ai/api/mcpcurl -s https://api.teppi.xyz/v1/trust/mcp/mcs_01M1FZ22H7PVXJ4VXSNSZZCH3J