Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@evalsmith-mcp compare my two retrieval configs and check if the gap is significant"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Evaluate RAG retrieval and AI agents with deterministic metrics, from any MCP client or from CI.
evalsmith answers four questions that come up every time a retriever, prompt or agent changes:
Is retrieval good enough? recall@k, precision@k, nDCG@k, hit rate and MRR from qrels and a ranked run.
Which configuration is better, and is the gap real? 2-3 configurations ranked with seeded paired-bootstrap confidence intervals.
Did the agent call the right tools? Trace vs. expected calls, strict or lax order, argument matchers.
Did the prompt change break anything? Golden-set checks on baseline vs. candidate outputs.
Everything is deterministic and runs offline. An LLM judge exists for a second opinion, but it is off by default and never decides the verdict.
flowchart LR
subgraph Clients
A[MCP client<br/>desktop app, IDE, agent]
B[Remote MCP client<br/>or service]
C[Terminal / CI job]
end
subgraph evalsmith
S1[MCP server<br/>stdio]
S2[MCP server<br/>Streamable HTTP, stateless]
S3[CLI<br/>evalsmith run]
CORE[(src/core<br/>metrics, bootstrap, matchers,<br/>checks, gates, reports)]
end
A --> S1 --> CORE
B --> S2 --> CORE
C --> S3 --> CORE
S3 -. exit code + JUnit .-> CInterface | Entry point | Who uses it | Why it exists |
MCP over stdio |
| Desktop MCP clients, IDE assistants, local agents | An assistant can score a run, compare configs or audit a trace mid-conversation, with typed |
MCP over Streamable HTTP |
| Remote clients, internal platforms, the MCP Inspector | Same tools over the network. Stateless: no sessions to store, scales horizontally. |
CLI / CI |
| Pipelines and terminals |
|
All three call the same functions in src/core. The MCP tools are thin adapters: parse input with Zod, call the core, return structuredContent validated against an output schema.
Related MCP server: mcp-llm-eval
flowchart TB
subgraph Tools
T1[evaluate_retrieval]
T2[compare_configs]
T3[check_tool_calls]
T4[detect_prompt_regression]
T5[generate_report]
end
subgraph Resources
R1["evalsmith://examples/{name}<br/>12 synthetic datasets + suite"]
end
subgraph Prompts
P1[compare_rag_configs]
P2[audit_agent_trace]
P3[review_prompt_change]
end
T1 -- structuredContent --> T5
T2 -- structuredContent --> T5
T3 -- structuredContent --> T5
T4 -- structuredContent --> T5
classDef tool fill:#ffedd5,stroke:#c2410c,color:#431407
classDef res fill:#e0f2fe,stroke:#0369a1,color:#082f49
classDef prom fill:#ede9fe,stroke:#6d28d9,color:#2e1065
class T1,T2,T3,T4,T5 tool
class R1 res
class P1,P2,P3 promEvery tool declares an input and an output Zod schema, returns structuredContent plus a JSON text block, and is annotated readOnly. Four tools are also idempotent and closed-world. detect_prompt_regression is declared open-world and non-idempotent: annotations are per tool, and with the judge enabled it calls an external model whose answers can vary. With the judge off (the default) it behaves like the others.
Tool | Input | Output |
|
| Mean recall/precision/nDCG/hit rate at every k, MRR, per-query values, skipped and missing queries |
|
| Ranking with intervals, winner, runner-up, paired interval of the gap, |
|
|
|
|
| Verdict, pass rates, regressions, improvements, per-case failures, judge votes when enabled |
| Any of the results above, |
|
Bad input (unknown metric, duplicate config names, qrels with no relevant documents) comes back as an InvalidParams tool error with a message the model can act on. A judge endpoint that is down or returns an HTTP error is an InternalError.
evalsmith://examples/{name} lists and serves the synthetic datasets in examples/datasets, so a client can try every tool before wiring in its own data:
Name | Content |
| 20 help-center articles of a fictional invoicing app, 12 questions, graded judgements |
| Top-10 rankings from three retrieval configurations |
| A support agent refunding a duplicate charge, and the calls it should make |
| Six golden cases and three sets of outputs |
| The example |
Prompt | Argument | What it asks the model to do |
|
| Compare the configurations, report the gap with its interval and the weakest queries |
|
| Check the trace and propose the smallest fix for each failed step |
|
| Detect regressions, build a Markdown report, decide ship / do not ship |
sequenceDiagram
autonumber
actor User
participant Host as MCP client (LLM host)
participant ES as evalsmith (stdio)
participant Core as src/core
User->>Host: "Is the reranker worth it on our help-center set?"
Host->>ES: initialize, tools/list, resources/list
ES-->>Host: 5 tools + JSON Schemas, 13 resources
Host->>ES: resources/read evalsmith://examples/rag-qrels (+ 3 runs)
ES-->>Host: qrels and runs as JSON
Host->>ES: tools/call compare_configs {metric: "ndcg@5", configs: [...]}
ES->>Core: compareConfigs(qrels, configs, "ndcg@5", {seed: 42})
Core-->>ES: ranking, intervals, paired delta
ES-->>Host: structuredContent {winner: "hybrid+rerank", significant: true, ...}
Host->>ES: tools/call generate_report {format: "markdown", comparison}
ES-->>Host: Markdown report
Host-->>User: "Yes: +0.153 nDCG@5 over hybrid, 95% CI [0.069, 0.251]."For a query $q$ with relevant set $R_q$ (documents with grade $> 0$) and ranking $D_q$, with $D_q^{k}$ its first $k$ documents:
\mathrm{Recall@}k = \frac{|R_q \cap D_q^{k}|}{|R_q|}
\qquad
\mathrm{Precision@}k = \frac{|R_q \cap D_q^{k}|}{k}
\qquad
\mathrm{HitRate@}k = \mathbb{1}\left[\,R_q \cap D_q^{k} \neq \varnothing\,\right]\mathrm{RR}_q = \frac{1}{\text{rank of the first relevant document}}
\qquad
\mathrm{MRR} = \frac{1}{|Q|}\sum_{q \in Q} \mathrm{RR}_q\mathrm{DCG@}k = \sum_{i=1}^{k} \frac{\mathrm{rel}_i}{\log_2(i+1)}
\qquad
\mathrm{nDCG@}k = \frac{\mathrm{DCG@}k}{\mathrm{IDCG@}k}$\mathrm{rel}_i$ is the graded judgement of the document at rank $i$ (linear gain, as in trec_eval), and IDCG is the DCG of the judgements sorted in descending order. Reported values are means over judged queries.
Conventions, all covered by tests:
Queries whose judgements are all 0 are skipped and listed in skippedQueries.
Judged queries missing from the run score 0 and are listed in missingFromRun.
Precision divides by $k$, not by the ranking length.
Repeated document ids in a ranking keep only their first position, as in trec_eval, so a run cannot score above 1 by repeating a relevant document.
Cut-offs start at 1: recall@0 is rejected.
Worked example (tests/core/retrieval.test.ts): judgements a=2, b=1, c=0, ranking d, a, e, b, c.
DCG@5 $= 2/\log_2 3 + 1/\log_2 5 = 1.69254$, IDCG@5 $= 2/\log_2 2 + 1/\log_2 3 = 2.63093$, so nDCG@5 $= 0.64332$; recall@3 $= 0.5$, precision@5 $= 0.4$, RR $= 0.5$.
Bootstrap. compare_configs resamples queries with replacement, using the same indices for every configuration (paired), samples times, from a seeded PRNG (mulberry32). Intervals are percentiles of the resampled means. The lead is significant when the interval of winner minus runner-up excludes zero. The same seed gives the same intervals on every machine.
{
"tool": "create_refund",
"args": {
"invoice_id": { "type": "exact", "value": "INV-2041" },
"reason": { "type": "any" },
"metadata": { "type": "subset", "value": { "ticket": "T-5532" } },
"to": { "type": "regex", "pattern": "@acme-robotics\\.example$" }
}
}strict requires the trace to be exactly the expected calls in order; calls past the expected sequence fail the check and appear as failed test cases in JUnit. lax ignores order and pairs expected calls with trace calls by maximum bipartite matching (augmenting paths), so a loose matcher never steals the only call a stricter one accepts. Extra calls are reported without failing. Every matcher, exact included, fails when the argument is missing.
exact (trimmed by default), regex, contains (case-insensitive by default), json_schema (the output is parsed and validated with Ajv) and length (min / max characters). A regression is a case that passes on the baseline and fails on the candidate. Missing outputs count as failures.
The optional judge sends both outputs to any OpenAI-compatible /chat/completions endpoint, Ollama included, at temperature 0. To cancel position bias each case is judged twice, with baseline as answer A and then as answer B. A side wins only when both orderings agree; a disagreement counts as a tie. That costs two requests per case. Its votes are reported next to the verdict.
git clone https://github.com/diegoperezg7/evalsmith-mcp.git
cd evalsmith-mcp
npm ci # also builds dist/ through the prepare script
npm test # 82 tests: metrics, MCP in-memory + HTTP, CLI exit codes
node dist/cli/index.js run examples/evalsmith.yamlMost desktop MCP clients read an mcpServers block like this (examples/mcp-client-config.json):
{
"mcpServers": {
"evalsmith": {
"command": "node",
"args": ["/absolute/path/to/evalsmith-mcp/dist/mcp/server.js"],
"env": { "LOG_LEVEL": "warn" }
}
}
}Restart the client, then try the compare_rag_configs prompt or ask it to read evalsmith://examples/agent-trace and check it.
EVALSMITH_TRANSPORT=http EVALSMITH_HTTP_PORT=3333 node dist/mcp/server.js
curl -s localhost:3333/healthz # {"status":"ok","version":"0.1.0"}
npx @modelcontextprotocol/inspector # connect to http://127.0.0.1:3333/mcpVariable | Default | Purpose |
|
|
|
|
| HTTP bind address |
|
| OpenAI-compatible endpoint for the optional judge |
|
| Judge model |
| unset | Only for hosted endpoints |
|
| pino level; logs always go to stderr |
evalsmith run <config> [--fail-under metric=value ...] [--format table|json|markdown]
[--report file.{md,json,xml}] [--report-format markdown|json|junit]
evalsmith doctor [config] [--judge]Gates accept recall@k, precision@k, ndcg@k, hit_rate@k, mrr, tool_calls (score of check_tool_calls) and prompt_pass_rate. Gates in the YAML file apply too; a command-line gate with the same name overrides it. A prompt regression or a failed tool-call check fails the run even without gates.
Exit code | Meaning |
| Every gate passed, no regression |
| A gate is below its threshold, the candidate regressed, or the tool calls do not match |
| Usage error, invalid config, unknown metric, unreadable dataset, judge endpoint down |
node dist/cli/index.js run examples/evalsmith.yaml on the bundled datasets (examples/evalsmith.yaml):
evalsmith · ledgerly-support-assistant
Retrieval (12 queries, MRR 0.8403)
┌────┬────────┬───────────┬────────┬──────────┐
│ k │ recall │ precision │ nDCG │ hit rate │
├────┼────────┼───────────┼────────┼──────────┤
│ 1 │ 0.4583 │ 0.7500 │ 0.6667 │ 0.7500 │
│ 3 │ 0.7500 │ 0.4167 │ 0.7442 │ 0.9167 │
│ 5 │ 0.9167 │ 0.3000 │ 0.8059 │ 1.0000 │
│ 10 │ 1.0000 │ 0.1667 │ 0.8285 │ 1.0000 │
└────┴────────┴───────────┴────────┴──────────┘
Comparison on ndcg@5 (paired bootstrap, 2000 samples, seed 42)
┌───┬───────────────┬────────┬──────────────────┐
│ # │ config │ ndcg@5 │ 95% CI │
├───┼───────────────┼────────┼──────────────────┤
│ 1 │ hybrid+rerank │ 0.9588 │ [0.8950, 1.0000] │
│ 2 │ hybrid │ 0.8059 │ [0.6875, 0.9141] │
│ 3 │ bm25 │ 0.5596 │ [0.3782, 0.7354] │
└───┴───────────────┴────────┴──────────────────┘
hybrid+rerank wins on ndcg@5 (0.9588), +0.1528 over hybrid; 95% interval of the gap [0.0692, 0.2506] excludes zero.
Agent tool calls (strict)
┌──────────────────┬─────────┬────────┬───────┐
│ expected │ matched │ status │ notes │
├──────────────────┼─────────┼────────┼───────┤
│ search_customers │ #1 │ pass │ │
│ get_invoice │ #2 │ pass │ │
│ create_refund │ #3 │ pass │ │
│ send_email │ #4 │ pass │ │
└──────────────────┴─────────┴────────┴───────┘
All 4 expected call(s) matched (strict order).
Prompt regression
┌───────────────────────┬──────────┬───────────┬────────────────────┐
│ case │ baseline │ candidate │ candidate failures │
├───────────────────────┼──────────┼───────────┼────────────────────┤
│ classify-intent │ pass │ pass │ │
│ extract-invoice-id │ pass │ pass │ │
│ refund-policy-summary │ fail │ pass │ │
│ due-date-format │ pass │ pass │ │
│ refuse-card-number │ pass │ pass │ │
│ refund-payload │ pass │ pass │ │
└───────────────────────┴──────────┴───────────┴────────────────────┘
No regressions across 6 case(s). Pass rate 83.3% -> 100.0%.
Gates
┌────────────┬────────┬────────┬────────┐
│ metric │ min │ actual │ status │
├────────────┼────────┼────────┼────────┤
│ recall@5 │ 0.8000 │ 0.9167 │ pass │
│ ndcg@5 │ 0.7500 │ 0.8059 │ pass │
│ tool_calls │ 1.0000 │ 1.0000 │ pass │
└────────────┴────────┴────────┴────────┘
PASSRaising the bar makes the same run fail the build:
$ node dist/cli/index.js run examples/evalsmith.yaml --fail-under recall@5=0.95 --fail-under mrr=0.9 --report report.xml
...
Gates
┌────────────┬────────┬────────┬────────┐
│ metric │ min │ actual │ status │
├────────────┼────────┼────────┼────────┤
│ recall@5 │ 0.9500 │ 0.9167 │ FAIL │
│ ndcg@5 │ 0.7500 │ 0.8059 │ pass │
│ tool_calls │ 1.0000 │ 1.0000 │ pass │
│ mrr │ 0.9000 │ 0.8403 │ FAIL │
└────────────┴────────┴────────┴────────┘
FAIL · 2 gate(s) below threshold
Report written to report.xml
$ echo $?
1examples/github-action.yml installs evalsmith, runs the suite with gates, writes the Markdown report to the job summary and publishes the JUnit XML as a check run:
- name: Run the suite
run: >
npx evalsmith run evals/evalsmith.yaml
--fail-under recall@5=0.8
--fail-under ndcg@10=0.7
--format markdown
--report evalsmith-report.xml
>> "$GITHUB_STEP_SUMMARY"name: ledgerly-support-assistant
retrieval:
qrels: datasets/rag/qrels.json # { queryId: { docId: grade } }
run: datasets/rag/runs/hybrid.json # { queryId: [docId, ...] }
k: [1, 3, 5, 10]
compare:
metric: ndcg@5
configs:
- { name: bm25, run: datasets/rag/runs/bm25.json }
- { name: hybrid, run: datasets/rag/runs/hybrid.json }
- { name: hybrid+rerank, run: datasets/rag/runs/hybrid-rerank.json }
bootstrap: { samples: 2000, seed: 42, confidence: 0.95 }
agent:
trace: datasets/agent/trace.json
expected: datasets/agent/expected.json
order: strict
prompts:
golden: datasets/prompts/golden.json
baseline: datasets/prompts/baseline.json
candidate: datasets/prompts/candidate.json
judge: { enabled: false }
gates:
recall@5: 0.8
ndcg@5: 0.75
tool_calls: 1.0Every section is optional. Paths are relative to the YAML file.
ragcompass profiles a corpus and recommends how to configure the RAG pipeline: parser, chunking, top-k, hybrid search, reranking. evalsmith checks whether that configuration holds up:
flowchart LR
A[Documents] --> B[ragcompass analyze<br/>recommended configs]
B --> C[Index 2-3 candidate configs]
C --> D[Run the eval queries<br/>save rankings]
D --> E[evalsmith compare_configs<br/>winner + intervals]
E --> F[evalsmith run in CI<br/>--fail-under gates]Deterministic first. Metrics, matchers and checks are pure functions with hand-computed tests. A model is never needed to get a verdict, so results do not drift between runs and no paid API key is required.
Judge off by default. When enabled it goes through any OpenAI-compatible endpoint (local Ollama works), and its votes sit next to the verdict instead of changing it.
Intervals, not just means. Twelve queries can make a 0.03 lead look decisive. A paired bootstrap with a fixed seed makes the uncertainty visible and reproducible.
Stateless HTTP done properly. A stateless StreamableHTTPServerTransport cannot be reused across requests, so every POST gets its own server and transport. Registration is cheap, and a test makes two consecutive calls over real HTTP to prove it.
stdout belongs to the protocol. pino writes to stderr and ESLint forbids console in src/, so no stray byte breaks the stdio transport. The CLI keeps stdout for results only.
One schema, three uses. The Zod schemas in src/core/schemas.ts validate MCP input, generate the JSON Schemas clients see, and type the core.
src/
core/ metrics, bootstrap, tool-call matchers, prompt checks, judge, gates, reports, suite runner
mcp/ server (stdio + HTTP), tools/, resources/, prompts/
cli/ evalsmith run | doctor, table/json/markdown output
shared/ config (Zod), pino logger on stderr, MCP errors
tests/
core/ hand-computed metrics, bootstrap, matchers, checks, reports
mcp/ InMemoryTransport client and real Streamable HTTP client
cli/ exit codes 0 / 1 / 2, JUnit file, doctor
examples/
datasets/ synthetic RAG corpus + qrels + 3 runs, agent trace, golden prompts
evalsmith.yaml, github-action.yml, mcp-client-config.jsonnpm run check # typecheck + eslint + prettier + vitest
npm run inspect # MCP Inspector against the built stdio serverProprietary. See LICENSE. The source is available for evaluation only.
This server cannot be deployed
MCP-native AI evaluation: rubric audits, eval suites, and proof reports for AI/LLM output.
Prompt evals over MCP: run a prompt on your dataset, score each output 1-5 with an LLM judge.
MCP server providing access to the Scorecard API to evaluate and optimize LLM systems.
Remote MCP for Gemini upgrade evals, prompt regressions, output diffs, and eval receipts.
