A-

plith.ai

https://plith.ai/api/mcp
92/100 · MCP Trust Grade · checked 4h ago · MCP 2024-11-05
Watched since 2026-06-03 — behavioral baseline locked. We re-check this server's tool surface on a schedule; if it adds, removes, or silently rewrites a tool (rug-pull), we record it.

What it offers — 15 tools · Developer Tools

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

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.

burnrate_estimate

Before executing a multi-step agent plan, estimate the total LLM cost. Returns per-step breakdown and optimization suggestions. If the estimate exceed

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 accura

burnrate_optimize

Get a cheaper equivalent plan by substituting models with lower-cost alternatives. Call after burnrate_estimate if the estimated cost exceeds your bud

burnrate_budget

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

qualitygate_validate

After your agent generates output, validate it against your rules before shipping. Runs deterministic checks (regex, JSON schema, syntax) plus optiona

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

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 typ

pitfalldb_query

Check for known failure patterns before executing a task type. Returns pitfalls with severity, fix suggestions, and confidence scores. After your agen

pitfalldb_report

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

rigor_plan

Before executing a complex task, get a structured workflow plan with per-step cost estimates. Classifies your task, selects the optimal framework sequ

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. Class

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_exe

rigor_workflows

List all Rigor workflows for your organization with filtering and pagination. Returns status, progress, capacity usage, and available actions per work

Spec conformance20%100
Security (OWASP MCP)30%100
Reliability / performance20%84
Tool hygiene15%95
Transparency / provenance15%70

Observed behavior

No proxied traffic observed for this host yet. Connect it at /connect and its grade gains a measured Reliability score + per-tool behavioral evidence — the half a static scan can't produce.

Findings

No blocking issues found in the static + spec checks.
Grade another server

We re-grade plith.ai on a schedule and alert your Slack/webhook the moment its tools change or its grade drops — rug-pull insurance for the connection.

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A 1200×630 card with the grade + audit — drop it in a post, Slack, or your repo.

MCP Trust report card — plith.ai grade A-
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A live badge — it re-verifies itself and shows current stability. Static scorecards can't. Paste it in your README or site to show users you're independently audited.

MCP Trust Grade A- · wmcp.sh
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Agents: check this before connecting

Add the wmcp.sh trust oracle as an MCP server and call grade_mcp_server / check_mcp_drift in your agent's pre-connection gate:

https://wmcp.sh/mcp/trust
How this grade is computed. An open, independent rubric — Spec conformance (20%), Security mapped to the OWASP MCP Top 10 (30%), Reliability (20%), Tool hygiene (15%), Transparency (15%) — run by connecting to the server and inspecting its real MCP surface. The grade is free and identical whether or not the operator pays. v1 uses static + spec signals from a single connection; continuous uptime, real latency, and annotation-truthing (declared readOnly vs observed behavior) layer on via the wmcp.sh proxy.