mcp server

plith

AI agent infrastructure: dedup, cost prediction, validation, governance, failure intelligence.

Description as published by the maintainer. Source

• version 1.0.0
• archived

archived — The linked repository returns 404. It was deleted, renamed or made private.

What this server can do

15 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

burnrate_budget(daily_limit)

Get today's tracked LLM spend, per-model breakdown, projection, and budget alerts. Free — no credits charged.

burnrate_estimate(plan)

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. Required: plan.

burnrate_optimize(plan, target_budget)

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. Required: plan.

burnrate_track(model, task_id, provider, input_tokens, output_tokens, cache_read_tokens)

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. Required: provider, model, input_tokens, output_tokens.

dedupq_check(content, task_id, hash_only, similarity_threshold)

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. Required: content.

dedupq_complete(result, content, task_id, hash_only)

After executing a task, store the result so future identical or similar tasks return a cache hit via dedupq_check. Costs 2 credits. Required: content, result.

guardrail_check(agent_id, proposed_action)

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. Required: agent_id, proposed_action.

guardrail_create_policy(name, rules, priority, description, action_types)

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 budget thresholds. Call this before using guardrail_check — checks require at least one active policy. Policies persist until explicitly deleted. Duplicate policy names return an error. Returns the created policy with its ID and active status. Required: name, rules.

pitfalldb_query(filters, task_type, task_description)

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 pitfalldb_report so others benefit. Costs 2 credits. Required: task_type.

pitfalldb_report(failure, task_type, task_description)

Report an agent failure. PII-scrubbed before storage. Linked to existing pitfalls if similar. Free — no credits charged. Required: task_type, task_description, failure.

qualitygate_validate(output, schema, language, override, directives, check_types, override_reason)

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. Required: output.

rigor_execute(context, delivery, task_type, preferences, task_description)

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 optimal tool sequence, and executes each step with the right LLM model. Returns a complete deliverable — solution designs, competitive analyses, governance documents, and more. Supports SSE streaming for real-time progress, webhook callback, or polling. Required: task_description.

rigor_plan(task_type, preferences, task_description)

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 plan without executing anything. Free — no credits charged. Required: task_description.

rigor_status(workflow_id)

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 retrieve results. Required: workflow_id.

rigor_workflows(limit, cursor, status, task_type, counts_toward_limit)

List all Rigor workflows for your organization with filtering and pagination. Returns status, progress, capacity usage, and available actions per workflow. Use to monitor workflow state, understand concurrent limit usage, and identify stuck or completed workflows.

Last successful function declaration observed on 2026-08-13. Source: https://plith.ai/api/mcp. We list what the server declared; we do not call any of these functions.

Endpoint status observed on 2026-08-13. Source: https://plith.ai/api/mcp.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Where to get it

• Repository — https://github.com/chicogonzales/plith
• Remote endpoint (streamable-http) — https://plith.ai/api/mcp