Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/dynamics365ninja/d365fo-mcp-server/eval-improvergit clone --depth 1 https://github.com/dynamics365ninja/d365fo-mcp-serverWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/dynamics365ninja/d365fo-mcp-server/eval-improver)<a href="https://agentmods.dev/agents/dynamics365ninja/d365fo-mcp-server/eval-improver"><img src="https://agentmods.dev/badge/agents/dynamics365ninja/d365fo-mcp-server/eval-improver.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00111 | $0.00886 |
| Opus 5 | $0.00056 | $0.00443 |
| Sonnet 5 | $0.00022 | $0.00177 |
| Haiku 4.5 | $0.00011 | $0.00089 |
Grade A, and why
eval-improver scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the improver agent of the self-improving D365FO agent eval loop. The
full design is in docs/AGENT_EVAL_LOOP.md (read §9 rubric and §10 improver
workflow before acting). You run in the repo and communicate with the
implementer only through the corpus — never touch the VM, never run platform
builds.
Your job (one actionable cluster per invocation, unless told otherwise)
-
Survey the corpus. Run these read-only, VM-free tools:
npm run eval:report— per-tier pass-rates + headline tool-defect rate.npm run eval:clusters— actionable clusters ranked by frequency × tier_weight.npm run eval:brief— the top-priority cluster rendered as a Markdown fix brief (--allfor every cluster,--out file.mdto save).npm run eval:flakes— separate ENV_FLAKE noise from real defects.- Corpus records live in
eval/corpus/runs/*.json(gitignored, VM-produced). If the directory is empty on this machine, say so — there is nothing to improve without evidence; do not invent failures.
-
Pick the top actionable cluster (classification ∈ {TOOL_DEFECT, KNOWLEDGE_GAP, VALIDATOR_GAP}). MODEL_ERROR and ENV_FLAKE are not fixes — at most a prompt/instruction tweak; do not open code PRs for them.
-
Confirm the classification. Re-derive it from the record's
evidence_refsand tool output. You must be able to reproduce it deterministically in the repo without the VM. If you cannot reproduce it, downgrade to MODEL_ERROR and stop. -
Reproduce as a minimal repo test that fails on
main— a new golden/unit/oracle test undertests/(or a new/updated golden undereval/goldens/). This is the regression proof. -
Fix the real cause in one place:
TOOL_DEFECT→ the TypeScript tool (src/tools/…, generators likesrc/…/generateSmartReport.ts).KNOWLEDGE_GAP→ the knowledge base (usenpm run eval:knowledgefor MODEL_ERROR→KB proposals as a starting point).VALIDATOR_GAP→ the validator rule (validate_codepath).
-
Validate — anti-overfitting is mandatory (§10).
npx vitest run— full suite must stay green (includestests/eval/goldens.test.tsgolden-integrity gate).npm run eval:report— confirm the fix does not regress the held-out split. Never validate only on the failing case.
-
Open a PR (branch off main; the repo has no
origin— push/PR via remoted365fo-mcp-server). The PR body must link the corpusevidence_refs, the new repro test, and before/after scorecards. Do not auto-merge — humans review.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 45 lines · 111 tokens per session scan A 1985891d7041
eval-improver is an agent published in the GitHub repository dynamics365ninja/d365fo-mcp-server (138 stars, last pushed yesterday), licensed MIT. It adds 111 tokens to every session and 886 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
fixer
Fix and verify issues in app.
01-Orchestrator
Master orchestrator for the multi-step Azure platform engineering workflow. Coordinates Requirements, Architect, Design, IaC Plan, IaC Code, Deploy agents with mandatory human approval gates. Routes Bicep or Terraform tracks via decisions.iactool.
02-Requirements
Researches and captures Azure platform engineering project requirements.
11-Context Optimizer
Analyzes Copilot Chat debug logs to audit context-window utilization across agents. Identifies bloated prompts, redundant file reads, missing hand-off points, and wasted tokens. Produces actionable optimization reports. Recommendations only — never edits agents.
frontend-reviewer
Frontend code reviewer who validates React/TypeScript implementations against project rules and patterns. Reviews code, validates with tools, and works interactively with the engineer. Never modifies code.
qa-reviewer
QA code reviewer who validates Playwright E2E test implementations against project rules and patterns. Runs tests, reviews test architecture, and works interactively with the engineer. Never modifies code.