Align the MCP guidance with the analysis instructions and move to Monty 0.0.23

The execute_code description gains the toc.json node shape, the patterns
the evals put into the analysis instructions (one list_documents call to
map a title to an id, files carry no source, toc before search for a known
document, doc_item_refs are items self_refs, chunk ids join files and are
not citations) and the sandbox's read-only, no-network, time-limit and
output facts, which the analysis instructions now state too. The skill
gains pictures as images, image search when offered, and citing chunk
metadata locators. docs/mcp.md lists the interpreter's limits under Code.

pydantic-monty>=0.0.23 brings collections, itertools, functools,
dataclasses, function decorators and str.format into the sandbox; every
layer names the same modules, and a test imports them. Monty now caps host
callbacks per checkout at 1000 by default; the sandbox raises it out of
reach, since the time budgets govern.

CHANGELOG.md

@@ -22,6 +22,9 @@
 
 ### Changed
 
+- `pydantic-monty>=0.0.23`. The analysis sandbox gains `collections`,
+  `itertools`, `functools`, `dataclasses`, function decorators and
+  `str.format`.
 - `fastmcp>=4.0.2,<5.0.0`, on MCP Python SDK 2. The MCP server answers both the
   session-based and the sessionless (2026-07-28) protocol.
 - Default models are `ollama:qwen3.8`: `ModelConfig`, `qa.model`,

docs/capabilities/analysis.md

@@ -16,7 +16,7 @@ When `qa.max_searches` or `analysis.max_executions` runs out, the exhausted tool
 | `analysis_execute_code(code)` | Run Python against the virtual document filesystem. |
 | `analysis_cite(chunk_ids)` | Register retrieved or filesystem-derived chunk IDs. |
 
-The sandbox exposes documents under `/documents/{document_id}/` with `metadata.json`, `content.txt`, `items.jsonl`, `chunks.jsonl` (chunk ids with their metadata) and `toc.json`. In code, `await search()` results carry `chunk_meta` and `await list_documents()` rows carry `metadata`.
+The sandbox exposes documents under `/documents/{document_id}/` with `metadata.json`, `content.txt`, `items.jsonl`, `chunks.jsonl` (chunk ids with their metadata) and `toc.json`. In code, `await search()` results carry `chunk_meta` and `await list_documents()` rows carry `metadata`. The interpreter's limits and the per-call budgets are listed under [MCP, Code](../mcp.md#code).
 
 ## Compose an agent
 

docs/mcp.md

@@ -151,18 +151,29 @@ node's `id` in the outline is the `section_id`. A document without headings
 has an empty outline. `list_documents` returns titles, URIs and metadata,
 which is how a client learns what a filter can match.
 
+### Code
+
 `execute_code` runs a Python program in the sandbox of the
 [analysis capability](capabilities/analysis.md), over the documents `filter`
 and `sources` select, and returns what it printed. The program reads
 `/documents/{document_id}/` (`metadata.json`, `content.txt`, `items.jsonl`,
 `chunks.jsonl`, `toc.json`) and can `await search()` and
 `await list_documents()`; the tool description spells out the fields and the
-interpreter's limits. Each call is one program: nothing carries over between
+patterns that matter. Each call is one program: nothing carries over between
 calls, and the sandbox is created and closed per call. A failing program is a
 tool error carrying the interpreter's message and any output printed before
-it. `analysis.code_timeout` bounds a call and `analysis.max_output_chars` its
+it. No model runs on the server. Claude Code moves a call still running after
-output; no model runs on the server. Claude Code moves a call still running
+about two minutes to a background task.
-after about two minutes to a background task.
+
+The interpreter is [Monty](https://github.com/pydantic/monty), a Python subset.
+Useful modules include `json`, `re`, `math`, `pathlib`, `datetime`,
+`collections`, `itertools`, `functools` and `dataclasses`. Absent, and often
+reached for: `decimal` and `statistics`. No generator functions, class
+inheritance or `match` statements, and a file object cannot be iterated. Files are read-only, and
+there is no network and no filesystem
+beyond `/documents`. `analysis.code_timeout` bounds a call, counted separately
+for compute and for document reads, and `analysis.max_output_chars` bounds its
+output.
 
 ### Filters
 

haiku_rag_slim/haiku/rag/capabilities/instructions/analysis.md

@@ -16,8 +16,8 @@ Inside the code, these functions are available (use `await`):
 - `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`), chunk_meta (the matched chunk's stored metadata, custom keys included)
 - `await list_documents()` → list of dicts with keys: id, title, uri, created_at, metadata
 
-Available modules: `json`, `re`, `math`, `pathlib`
+Useful modules include `json`, `re`, `math`, `pathlib`, `datetime`, `collections`, `itertools`, `functools` and `dataclasses`. `decimal` and `statistics` do not exist.
-Not supported: class inheritance and metaclasses, generators/yield, match statements, decorators, `collections`, iterating a file object (`for line in f`)
+Not supported: class inheritance and metaclasses, generators/yield, match statements, iterating a file object (`for line in f`)
 
 ### analysis_search
 Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
@@ -49,7 +49,7 @@ All documents are mounted as a virtual filesystem at `/documents/`:
 `{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora.
 
 ### Reading files
-Read with `Path.read_text()` or `open()` (including `with` blocks); file objects support `.read()`, `.readline()`, and `.readlines()`. A file object cannot be iterated, so read line-wise with `.readlines()` or `.read().split("\n")` instead of `for line in f`. Files are read-only; writing raises `PermissionError`.
+Read with `Path.read_text()` or `open()` (including `with` blocks); file objects support `.read()`, `.readline()`, and `.readlines()`. A file object cannot be iterated, so read line-wise with `.readlines()` or `.read().split("\n")` instead of `for line in f`. Files are read-only; writing raises `PermissionError`. There is no network. A call has a time limit, named in the error when it is hit, and output past a size is cut with an `... (output truncated)` marker.
 
 ```python
 from pathlib import Path
@@ -116,6 +116,6 @@ You MUST call `analysis_cite` before producing your final answer, every time, wi
 - Use `print()` to output results — the output is your only feedback
 - When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `analysis_search → analysis_cite`.
 - Use `await` for all async functions inside `analysis_execute_code` (`search`, `list_documents`)
-- Read files with `Path.read_text()` or `open()`/`with`. For lines use `.readlines()` or `.read().split("\n")`, never `for line in f`. The `collections` module is unavailable.
+- Read files with `Path.read_text()` or `open()`/`with`. For lines use `.readlines()` or `.read().split("\n")`, never `for line in f`.
 - Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `analysis_cite` tool separately to register citations. `cite{...}` markdown-style inline references do nothing; only an actual `analysis_cite` tool call registers a citation.
 - **Before you write your final answer, invoke the `analysis_cite` tool with the supporting chunk_ids, or with an empty list if there are none.** This is the last tool call before answering, every time.

haiku_rag_slim/haiku/rag/mcp.py

@@ -464,15 +464,31 @@ def _covering(scope: "DatabaseScope", config: AppConfig) -> FastMCP:
         (id, title, uri, created_at, metadata), `content.txt` (the whole text),
         `items.jsonl` (one item per line: self_ref, label, text, page_numbers,
         heading_level, chunk_ids), `chunks.jsonl` (one chunk per line: chunk_id,
-        metadata) and `toc.json` (the section tree, each node with an item_range
+        metadata) and `toc.json` (`doc_id`, `title`, `tree`; each node has
-        slice into items.jsonl). Read files with `Path.read_text()` or `open()`;
+        self_ref, level, title, page_numbers, item_range as a slice into
-        a file object cannot be iterated, use `.readlines()`.
+        items.jsonl, chunk_ids and children; an empty tree means no headings).
-        `await search(query, limit=10)` returns dicts with chunk_id, content,
+        Read files with `Path.read_text()` or `open()`; a file object cannot be
-        document_id, document_title, document_uri, source, score, page_numbers,
+        iterated, use `.readlines()`. `await search(query, limit=10)` returns
-        headings, doc_item_refs, labels and chunk_meta. `await list_documents()`
+        dicts with chunk_id, content, document_id, document_title, document_uri,
-        returns dicts with id, title, uri, created_at, source and metadata.
+        source, score, page_numbers, headings, doc_item_refs, labels,
-        Modules: json, re, math, pathlib. Not available: generators, class
+        picture_refs (the doc_item_refs that are pictures) and chunk_meta.
-        inheritance, match statements, decorators, collections.
+        `await list_documents()` returns dicts with id, title, uri, created_at,
+        source and metadata. Both see the documents `filter` and `sources`
+        select. Useful modules include json, re, math, pathlib, datetime,
+        collections, itertools, functools and dataclasses; decimal and
+        statistics do not exist. No generator functions, match statements or
+        class inheritance.
+        Files are read-only, there is no network, a call has a time limit named
+        in the error when it is hit, and output past a size is truncated.
+
+        Map a title or URI to a document id with one `list_documents()` call
+        rather than reading every `metadata.json`. The files carry no `source`,
+        so over several collections group by the `source` of `list_documents()`
+        rows. For a known document's structure read its `toc.json` before
+        searching: `search()` ranks across every document. A hit's
+        `doc_item_refs` are `self_ref` values in `items.jsonl`, which places it
+        in its section. `chunk_ids` on items and `chunk_id` in `chunks.jsonl`
+        join the two files; they are not citations.
 
         Args:
             code: The program. Use `await` on search and list_documents.

haiku_rag_slim/haiku/rag/sandbox/sandbox.py

@@ -33,6 +33,8 @@ if TYPE_CHECKING:
 
 logger = logging.getLogger(__name__)
 
+_MAX_HOST_CALLS = 10_000_000
+
 
 def _host_failure(where: str, e: Exception) -> RuntimeError:
     """The error a program gets for a failure on the host side of a call.
@@ -613,9 +615,16 @@ class Sandbox:
         covers the whole run. ``code_timeout`` is enforced per call elsewhere: the read
         deadline in ``_run_on_loop`` bounds a call that reads, and the pool's
         ``request_timeout`` bounds one that computes.
+
+        ``max_suspensions`` counts host callbacks per session, document reads
+        included, defaults to 1000 and cannot be disabled. The time budgets are
+        the governors here, so it is set where no program reaches it.
         """
         analysis = self._config.analysis
-        return {"max_duration_secs": analysis.code_timeout * analysis.max_executions}
+        return {
+            "max_duration_secs": analysis.code_timeout * analysis.max_executions,
+            "max_suspensions": _MAX_HOST_CALLS,
+        }
 
     async def _ensure_initialized(self) -> tuple[AsyncMontySession, OSAccess]:
         """Check out a worker session and build the VFS on first use."""

haiku_rag_slim/pyproject.toml

@@ -45,7 +45,7 @@ dependencies = [
     "pathspec>=1.0.4",
     "pydantic>=2.12.5",
     "pydantic-ai-slim[openai,logfire,ag-ui]>=2.18.0,<3.0.0",
-    "pydantic-monty>=0.0.19",
+    "pydantic-monty>=0.0.23",
     "pypdfium2>=5.0",
     "python-dotenv>=1.2.2",
     "pyyaml>=6.0.3",

plugins/haiku-rag/skills/haiku-rag/SKILL.md

@@ -25,10 +25,12 @@ could be about the user's documents. Say so when it has nothing relevant.
 
 `search_documents` is the first call. Results come best first with the document
 title, section headings, the matched chunk's metadata when it has any, and the
-passage in its section. `filter` restricts which
+passage in its section. Pictures in the results arrive as images: answer
-documents are searched, `limit` how many results come back. If it misses,
+figure questions from them. `filter` restricts which documents are searched,
-rephrase once or narrow with a filter before concluding the material is not
+`limit` how many results come back. If it misses, rephrase once or narrow with
-there.
+a filter before concluding the material is not there. When the question is
+about an image rather than words and the server offers
+`search_documents_by_image`, it takes the image as the query.
 
 ## Read
 
@@ -48,7 +50,11 @@ comparison across many documents, a lookup by document or chunk metadata, or a
 pattern over whole documents: whatever search cannot rank. Each call is one
 program and variables do not carry over, so gather, compute and `print` a
 compact result in the same program. `filter` and `sources` select the documents
-it sees. Answer and cite from what it printed.
+it sees. For a known document's structure read its `toc.json` first; `search()`
+ranks across every document. Map a title or URI to an id with one
+`list_documents()` call rather than reading every `metadata.json`; the files
+carry no `source`, so over several collections group by its rows. Answer and
+cite from what it printed.
 
 ## Explore
 
@@ -66,5 +72,6 @@ it with LIKE: `metadata LIKE '%"author": "Smith"%'`. Also `uri LIKE '%.pdf'`,
 
 Rank is the signal; scores are not comparable across queries and are never
 confidence. Cite the document title or URI, the section heading and page
-numbers when present. When results carry `source`, the server covers several
+numbers when present, and the matched chunk's metadata when it carries locators
-collections: name it, and pass `sources` to search a subset.
+such as paragraph or footnote numbers. When results carry `source`, the server
+covers several collections: name it, and pass `sources` to search a subset.

tests/sandbox/test_sandbox.py

@@ -19,6 +19,18 @@ def vcr_cassette_dir():
 class TestSandboxBasics:
     """Test basic sandbox functionality."""
 
+    @pytest.mark.asyncio
+    async def test_the_documented_modules_import(self, sandbox):
+        """The modules the instructions and the MCP description promise."""
+        result = await sandbox.execute(
+            "import json, re, math, pathlib, datetime\n"
+            "import collections, itertools, functools, dataclasses\n"
+            "print(collections.Counter('aab').most_common(1),"
+            " list(itertools.islice(itertools.count(), 2)))"
+        )
+        assert result.success, result.stderr
+        assert "[('a', 2)] [0, 1]" in result.stdout
+
     @pytest.mark.asyncio
     async def test_execute_simple_code(self, sandbox):
         """Test executing simple code in the sandbox."""
@@ -1048,7 +1060,51 @@ class TestSandboxReadDeadline:
 
         sb = Sandbox(db_path=temp_db_path, config=config, context=AnalysisContext())
 
-        assert sb._session_limits() == {"max_duration_secs": 15.0}
+        limits = sb._session_limits()
+
+        assert limits["max_duration_secs"] == 15.0
+        cap = limits["max_suspensions"]
+        assert cap is not None
+        assert cap >= 1_000_000
+
+    @pytest.mark.asyncio
+    async def test_a_program_may_read_more_than_a_thousand_times(self, temp_db_path):
+        """Monty caps host callbacks per checkout at 1000 unless told otherwise;
+        a corpus-wide pass over documents reads far more than that."""
+        from docling_core.types.doc.document import DoclingDocument
+        from docling_core.types.doc.labels import DocItemLabel
+
+        config = AppConfig()
+        docling = DoclingDocument(name="d")
+        docling.add_text(label=DocItemLabel.TEXT, text="Foxes and dogs.")
+        async with HaikuRAG(temp_db_path, create=True) as client:
+            doc = await client.import_document(
+                docling,
+                [
+                    Chunk(
+                        content="Foxes and dogs.",
+                        embedding=[0.1] * config.embeddings.model.vector_dim,
+                        order=0,
+                    )
+                ],
+                uri="test://many-reads",
+            )
+
+        sb = Sandbox(db_path=temp_db_path, config=config, context=AnalysisContext())
+        try:
+            result = await sb.execute(
+                "from pathlib import Path\n"
+                f"p = Path('/documents/{doc.id}/content.txt')\n"
+                "n = 0\n"
+                "for i in range(1100):\n"
+                "    n += len(p.read_text())\n"
+                "print(n)"
+            )
+        finally:
+            await sb.close()
+
+        assert result.success, result.stderr
+        assert result.stdout.strip() == str(1100 * len("Foxes and dogs."))
 
     @pytest.mark.asyncio
     async def test_refused_read_fails_the_execution(self, temp_db_path, monkeypatch):

uv.lock

@@ -1776,7 +1776,7 @@ requires-dist = [
     { name = "pydantic-ai-slim", extras = ["mistral"], marker = "extra == 'mistral'" },
     { name = "pydantic-ai-slim", extras = ["openai", "logfire", "ag-ui"], specifier = ">=2.18.0,<3.0.0" },
     { name = "pydantic-ai-slim", extras = ["voyageai"], marker = "extra == 'voyageai'" },
-    { name = "pydantic-monty", specifier = ">=0.0.19" },
+    { name = "pydantic-monty", specifier = ">=0.0.23" },
     { name = "pypdfium2", specifier = ">=5.0" },
     { name = "python-dotenv", specifier = ">=1.2.2" },
     { name = "pyyaml", specifier = ">=6.0.3" },
@@ -3991,94 +3991,82 @@ wheels = [
 
 [[package]]
 name = "pydantic-monty"
-version = "0.0.19"
+version = "0.0.23"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
+    { name = "pydantic-monty-client" },
     { name = "pydantic-monty-runtime" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/59/01/cd7927f51500a13c1661e2db6488f6ff668194c4378d0fb218928892aaba/pydantic_monty-0.0.23.tar.gz", hash = "sha256:ee674b81ed12f81cfbe0db210fe5e803c754d0682634331931f4d70d5742058d", size = 6713, upload-time = "2026-09-05T19:26:03.24Z" }
+wheels = [
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+]
+
+[[package]]
+name = "pydantic-monty-client"
+version = "0.0.23"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
     { name = "typing-extensions" },
 ]
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