Llm Usage MCP vs Opik MCP — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Llm Usage MCP vs Opik MCP
In-depth architectural comparison of the Llm Usage MCP and Opik MCP MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Llm Usage MCP
Monitoring · Local stdio
Quality: 61/100 (Good) | Auth: No auth required
Opik MCP
Monitoring · Local stdio
Quality: 61/100 (Good) | Auth: API Key required
Verdict Summary: Choose Llm Usage MCP if you need specialized Monitoring tools running via a local process. Choose Opik MCP if your workspace requires Monitoring integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Llm Usage MCP when:
You need dedicated capabilities in the Monitoring domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Local-first LLM API cost tracker. Captures usage across Anthropic, OpenAI, Qwen, and DeepSeek into a local SQLite ledger and exposes spend queries, provider comparison, and recommendations as MCP tools — with first-class Chinese-provider support (CNY→USD). Install: uvx llm-usage-mcp.
Use natural language to explore LLM observability, traces, and monitoring data captured by Opik.
Category & Scope
Tools & Capabilities Breakdown
Llm Usage MCP Tools (7)
record_usage
Record a single LLM API call with token counts.
Cost is computed automatically from the pricing table at insert time.
`request_id` enables idempotent recording — replaying a log file
won't double-count.
query_spend
Return spending broken down by a chosen axis over a time window.
`start` and `end` are ISO-8601 strings (trailing-`Z`, `+00:00`, or
naive — naive is interpreted as UTC). Default window is the last
30 days. `group_by` is one of provider | model | project | tag | day.
`filter` AND-combines optional provider/model/project equality
predicates.
`include_failed` defaults to `False` so failure rows (e.g. streams
that died mid-flight with partial counts) are excluded from totals
and groups. Pass `True` to fold them back in — useful for debugging
capture-layer behavior, not for honest spend numbers.
Tag semantics: events with NULL/empty tags are excluded from
`group_by="tag"` results entirely; multi-tag events contribute once
per tag (so per-group `calls` sums can exceed the window total).
Project semantics are symmetric: NULL projects are dropped from
`group_by="project"`. Groups are ordered cost-desc with
alphabetical ties.
compare_providers
Project the cost of a hypothetical workload across providers/models.
Returns models ranked by absolute cost ascending, with
`relative_cost_pct` measured against the cheapest entry
(cheapest = 100%). `models`, if given, restricts the comparison to
those model names. Cost is computed from input/output tokens only;
`RankedEntry.notes` is always `None` in v1 (the field is retained
for future per-row caveats like "tiered pricing approximated").
`include_snapshots=False` (the default) family-dedups the ranked
list: rows sharing both a model-family root (`gpt-5-mini` ↔
`gpt-5-mini-2025-08-07`) AND an identical projected cost collapse
to one representative, with `RankedEntry.variant_count` recording
how many catalog rows the entry stands for. Set
`include_snapshots=True` to see every catalog row (each with
`variant_count=1`) — useful when comparing snapshot-by-snapshot
pricing for production pinning.
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Llm Usage MCP is categorized under Monitoring and uses a local stdio subprocess. In contrast, Opik MCP belongs to Monitoring using local stdio subprocess. Select Llm Usage MCP when you need capabilities focused on monitoring and Opik MCP when you require tools for monitoring.
Recommend the cheapest priced model that fits the workload + budget.
v1 ranks by cost only. A future release will incorporate quality
benchmarks (see `quality_snapshot` — the table is reserved for that
purpose) and accept a `quality_priority` axis; for v1 those would
rely on data we don't yet have, so the surface stays cost-only and
honest.
`expected_input_tokens` / `expected_output_tokens` default to a
nominal 1k/1k workload when absent; the `reasoning` notes when
defaults are in use. `budget_usd`, when set, filters out models
that exceed it — if nothing fits, falls back to the cheapest model
overall (the result fields are required, so there's no "no match"
return shape) and the `reasoning` says so plainly.
`providers` / `models` are optional whitelists (AND-combine when
both passed). Both are applied before the budget cut, so an over-
budget fallback returns the cheapest within the filter set rather
than the cheapest priced model overall. A whitelist that matches
nothing raises rather than fabricating a result — likely a
spelling error in the caller's name list.
`task_description` is **optional** and echoed into the reasoning
but does not drive selection — the tool isn't an LLM and can't
interpret free text. Omit it (or pass `None`) and the reasoning
opens with `"Recommending …"` instead of `"For task 'X': …"`.
get_pricing
Return current pricing for one model, one provider, or all models.
Both filters are optional and AND-combined. An unknown
(provider, model) returns an empty list rather than an error — the
caller can distinguish "model not in our table" from "no model
matches your filter" by passing `provider` alone.
usage_summary
Return a one-shot summary of usage over a named calendar period.
`period` is one of today | week | month | year (default: "week").
Boundaries are calendar UTC: `today` = since 00:00 UTC today,
`week` = since Monday 00:00 UTC, `month` = since the 1st of the
month, `year` = since January 1st. Returns totals, the top-3
providers and top-3 models by cost (with `pct` of total), and the
single most expensive call in the window — or `largest_call=None`
when the window is empty.
`include_failed` defaults to `False`: totals, top-N rollups, and
`largest_call` all exclude `success=False` rows (partial-stream
captures and other failure rows). Pass `True` for symmetric
debugging access to the failure population.
list_providers
List every provider we know about, with their models and OpenAI-compat flag.
Sources the provider/model lists from `pricing_snapshot`, so a
provider whose pricing hasn't been seeded simply doesn't appear.
After `bootstrap()` runs on a fresh install this includes every v1
provider (anthropic, openai, qwen, deepseek). Order is alphabetical
by provider, then by model within each provider.
Opik MCP Tools (1)
read_skill
Read one of the Opik agent skills bundled with this server