Harden agent tool calling and add MCP server support by jhd3197 · Pull Request #95 · jhd3197/Prompture

jhd3197 · 2026-08-06T03:38:52Z

The tool loop had a human-in-the-loop approval gate that approved everything, a stop() that stopped nothing, and schemas that described half your parameters as strings regardless of what they were — three ways of being confidently wrong. This PR makes the loop tell the truth: honest parameter schemas with argument validation, approval and cancellation that actually take effect, per-tool timeouts, graceful exits instead of exceptions, and tool_choice plumbed through to the providers that support it. It also adds an MCP client, so agents can consume tools from any MCP server through the ordinary tool path.

Highlights

Async approval handlers are now honoured. Previously an async def handler returned a coroutine that read as "approved", so every tool guarded by human review ran unattended
Stopping a running agent now actually stops it, including for the default agent (the previous behaviour only ever reached agents configured to reuse a conversation)
A run that exhausts its tool rounds answers with what it gathered instead of throwing away the whole run — the case that hurt most on the weaker local models that prompted tool calling targets
Tools can be given a time limit, so a single hung tool no longer wedges a run indefinitely
The model is told the real type of every parameter — optionals, enums, fixed choices, mappings and nested models — and when it sends the wrong type it gets a correctable message back instead of an exception
Agents can consume tools from any MCP server, via the new optional mcp extra
The model now receives a tool's whole docstring rather than only its first line, so the prose describing when to use a tool and what it will not do finally reaches it
Forcing, restricting or disabling tool use now reaches OpenAI, Anthropic and Google instead of being silently discarded
Tool results are capped by default, so one oversized result can no longer crowd out the conversation

Technical changes

Tool schema generation

Extend _python_type_to_json_schema to emit unions (both typing.Union and PEP-604) as anyOf with {"type": "null"} for NoneType, Literal and Enum as enum via _enum_schema (adding a shared type only when every value agrees), list/tuple/dict with items/prefixItems/additionalProperties, datetime/date/time/UUID as string plus a format, and nested pydantic models, dataclasses and TypedDict expanded through pydantic.TypeAdapter. All of these previously collapsed to {"type": "string"}
Add _structured_fallback_schema and _structured_required_fields, which rebuild an object schema from get_type_hints when TypeAdapter refuses a structured type — pydantic rejects typing.TypedDict below Python 3.12, so on two of the four supported versions such a parameter was advertised as a bare string. Optionality is read per kind (__required_keys__, absent dataclass default/default_factory, FieldInfo.is_required()) and required is omitted entirely when undeterminable
Return the empty schema for a missing annotation instead of {"type": "string"}; an unannotated parameter means "type unknown", and since arguments are now validated, claiming string rejected every correct non-string call
Add _coerce_and_validate_arguments, which coerces each argument toward its annotation, rejects unknown keys, and validates against the tool's schema with jsonschema.Draft7Validator, returning a message naming the offending argument. Wired into both ToolRegistry.execute and aexecute
Add _strict_parameters and _make_nullable for OpenAI strict tool use, forcing originally-optional parameters nullable since strict mode moves every property into required
Build tool descriptions from the docstring summary and extended description via _split_docstring, _section_key, _docstring_description and _truncate_description, with Returns:/Yields: appended, Args: omitted (already in the parameter schema), and a MAX_TOOL_DESCRIPTION_CHARS cap of 1024 truncated on a paragraph, sentence or word boundary. Rewrite _parse_docstring_params on the new section splitter, which also fixes Args: failing to terminate at multi-word headers such as See Also:
Indent continuation lines in ToolDefinition.to_prompt_format() and the XML grammar renderer, both of which assumed a single-line description

Agent loop

Add _invoke_cb_sync, which detects an awaitable callback result and drives it to completion — directly via asyncio.run, or on a worker thread carrying a contextvars.copy_context() snapshot when a loop is already running. Applied to on_tool_start, on_tool_end and on_approval_needed; the last of these is why an async def approval handler auto-approved
Track the in-flight conversation in _active_conversation via _track_active, so stop() reaches non-persistent runs; _conversation is only populated under persistent_conversation=True. A stop() arriving before the conversation is built is replayed onto it, and the field is reset at every run entry (_execute, _execute_iter, _execute_stream, _execute_live)
Add a tool_timeout constructor parameter on Agent/AsyncAgent, forwarded into the conversation and overridable per call through options={"tool_timeout": ...}
Add _make_live_ctx_fn so RunContext is rebuilt per tool invocation, giving tools the current iteration, message history and usage rather than a snapshot from the start of the run
Thread a tool_timings list through the tool wrappers to populate AgentStep.duration_ms (previously a dead field), and build an id_to_name map from assistant tool_calls messages so AgentStep.tool_name records the tool name instead of the call id
Apply the _agent_depth guard and reset to every run path, not just run_live
Await async tools through the _async_fn hook on the current event loop, replacing asyncio.run on a throwaway thread — which blocked the loop, dropped contextvars (tukuy SecurityContext, current_tool_call_id) and could deadlock
Discard a cached persistent conversation in _build_conversation when the agent's driver has since been swapped, so a budget fallback is not silently ignored
Remove the agent-level pre-call budget block, which read a freshly constructed and therefore always-empty UsageSession; enforcement lives in Conversation._check_budget, which sees real usage

Conversation

Add request_stop() and a max_rounds_reached property to Conversation/AsyncConversation
Replace the RuntimeError on exhausted tool rounds with _final_answer_without_tools / _final_answer_simulated, which take one more driver call with the tools removed and instruct the model to answer from the results already gathered. Shared by max-rounds exhaustion and cooperative stop
Add _call_tool, enforcing the timeout with a single-worker ThreadPoolExecutor and shutdown(wait=False) to detach a thread Python cannot kill; the async path uses asyncio.wait_for
Add _execute_tool_call, which sets current_tool_call_id on the sync path as well (previously async-only) and converts TimeoutError into a tool result the model can react to
Add _malformed_arguments_message, which returns a retry instruction instead of executing a call whose driver flagged arguments_error or truncated
Add _trim_history, dropping leading orphaned tool messages after the sliding-window slice; a naive tail-slice could cut between an assistant tool_calls message and its results, which providers reject with a 400
Change the max_tool_result_length default from None to 16000, bounding tool results out of the box
Run independent tool calls concurrently in AsyncConversation._run_tool_calls via asyncio.gather, with a sequential_tools opt-out, and pair results using zip(..., strict=True) so a mis-pairing fails loudly rather than attaching a result to the wrong tool_call_id
Clear _full_tool_results in clear() so a reset conversation does not retain prior results

Driver layer

Add _normalize_stop_reason with _STOP_REASON_MAP and _GOOGLE_STOP_REASON_MAP (including Gemini's numeric FinishReason values), mapping providers onto a shared vocabulary of end_turn, tool_use, max_tokens, content_filter and error, upgrading to tool_use when a provider reports end-of-turn alongside tool calls, and passing unknown strings through unchanged. The provider's raw value is preserved in meta["raw_stop_reason"] and usage["raw_stop_reason"]
Add _translate_tool_choice and _apply_openai_tool_options to forward tool_choice and parallel_tool_calls, translating the normalized form into each provider's wire shape and warning rather than sending a payload the API will reject. Applied across the OpenAI, Azure, Claude, Google, Groq, Grok, Moonshot, ModelScope, MiniMax and Zai drivers
Add _parse_tool_arguments_with_error and _tool_call_dict, attaching arguments_error and truncated to a tool call so malformed or max_tokens-truncated arguments are reported instead of executed as {}. Applied to the buffered path and both streaming finalizers
Add DriverHTTPError carrying status and response body for raw-HTTP driver failures
Add truncated and raw_stop_reason fields to the ToolUseStop live event
Accept single- as well as double-quoted attributes in the prompted-tool XML grammar, and add ToolGrammar.open_prefix so the streaming parser holds back a trailing run that could still grow into an opening delimiter split across chunks

MCP

Add prompture/integrations/mcp_bridge.py with register_mcp_tools, register_mcp_tools_sync and the mcp_session_from_stdio async context manager. MCP inputSchema is already JSON Schema and passes through unchanged; names are sanitized to ^[a-zA-Z0-9_-]{1,64}$ with optional prefixing to namespace multiple servers
Register each MCP tool with an _async_fn hook so AsyncAgent/AsyncConversation await it natively, with the sync path raising a clear error when called from inside a running loop
Flatten results through _serialize_content_block/_serialize_call_result: text blocks joined, non-text blocks reduced to a short placeholder, and MCP call errors returned as Error: ... strings the model can react to
Export the bridge lazily through a module __getattr__ in prompture/integrations/__init__.py, so importing the package never requires the optional dependency
Add the mcp = ["mcp>=1.0"] extra and include it in all; document the flow in docs/INTEGRATIONS.md

tukuy bridge

Add _resolved_param_schemas and _repair_parameter_types, re-deriving skill parameter types through get_type_hints. tukuy builds its schema from raw __annotations__, so a skill in a module using from __future__ import annotations handed it the string "int" and every parameter degraded to {"type": "string"} — harmless when it only misinformed the model, but a hard failure once arguments are validated. Only properties tukuy already emitted are touched, so parameters it deliberately hides stay hidden, and non-shape keys it attached are preserved
Reduce prompture/integrations/tukuy_bridge.py to a re-export shim. It was a byte-identical copy of prompture/extraction/tukuy_bridge.py, which meant two distinct current_tool_call_id ContextVars — a tool executed through one module's var was invisible to code reading the other's, and any fix would have applied to only one copy

tool_from_function derived a tool's description from the first line of the docstring only, so everything after the summary — the prose that actually says what a tool covers, when to use it, and what it will not do — never reached the model. The description is now built from the docstring's summary plus its extended description, with Returns:/Yields: appended as a single line. Args: stays out since it is already encoded in the parameter schema, and Raises:/Examples:/ Notes: are dropped to keep descriptions dense. Descriptions are capped at MAX_TOOL_DESCRIPTION_CHARS (1024, OpenAI's limit) and truncated on a paragraph, sentence, or word boundary; pass max_description_chars=None to opt out. ToolRegistry.register() threads the cap through. _parse_docstring_params is rewritten on the new section splitter, which also fixes Args: not terminating at multi-word headers such as See Also. Two renderers assumed single-line descriptions and now indent continuation lines: ToolDefinition.to_prompt_format() and the XML tool-grammar block. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

tukuy derives a skill's JSON Schema from the raw `__annotations__`, so a skill defined in a module using `from __future__ import annotations` hands it the *string* `"int"` rather than `int` — and every such parameter silently degraded to `{"type": "string"}`. That style is used throughout this codebase, so it was the common case rather than an edge case. Previously this only misinformed the model. Now that tool arguments are validated against the advertised schema it rejects correct calls outright (`double(x=7)` -> "7 is not of type 'string'"). Re-derive the parameter fragments via `typing.get_type_hints`, which resolves the strings, and merge them over tukuy's output. Only properties tukuy already emitted are touched, so parameters it deliberately hides (`context`, config params) stay hidden, and non-shape keys it attached (description, default) are preserved. Also reduce `integrations/tukuy_bridge.py` to a re-export shim. It was a byte-identical copy of the canonical module, which meant two *distinct* `current_tool_call_id` ContextVars — a tool executed via one module's var was invisible to code reading the other's, and this fix would have applied to only one copy. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

…args Three gaps in the driver layer surfaced by a tool-calling audit: `tool_choice` was silently dropped by the OpenAI, Claude and Google drivers (only minor raw-HTTP drivers forwarded it), and `parallel_tool_calls` was exposed nowhere. Both now travel through a shared `_translate_tool_choice` that maps the normalized form onto each provider's wire format, warning and ignoring rather than sending something the API will reject. `tool_choice="required"` on a final round is the standard cure for an agent that never stops, so it needs to actually reach the API. `stop_reason` was passed through raw, so callers had to know each provider's vocabulary (`tool_calls` vs `tool_use` vs numeric Gemini enums) and truncation was invisible to the agent loop. Responses now carry a normalized reason (`end_turn`, `tool_use`, `max_tokens`, `content_filter`, `error`) with the provider's original value preserved in `meta["raw_stop_reason"]` / `usage["raw_stop_reason"]`. Unknown strings pass through unchanged so no information is lost. Malformed or truncated tool arguments were logged and then executed as `{}` — a `write_file` call truncated at `max_tokens` ran with empty arguments instead of the model being told to retry. Both the buffered and streaming paths now attach `arguments_error` / `truncated` to the tool call so the conversation layer can feed the problem back to the model. Also accept single-quoted attributes in the prompted-tool XML grammar and give `ToolGrammar` an `open_prefix` so the streaming parser can hold back a partial delimiter split across chunks. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

`_python_type_to_json_schema` degraded most non-trivial annotations to `{"type": "string"}`: `int | None`, `Literal`, `Enum`, nested pydantic models, dataclasses, `TypedDict`, `dict[str, X]` and container element types. The schema handed to the model therefore disagreed with what the tool actually accepted, and coercion existed but was wired only to extraction, never to tool arguments. Now emitted faithfully: unions as `anyOf` (with `{"type": "null"}` for NoneType), `Literal`/`Enum` as `enum` (with a shared `type` when all values agree), containers with `items`/`prefixItems`/`additionalProperties`, date-ish scalars with a `format`, and nested structured types expanded via `pydantic.TypeAdapter`. Arguments are coerced towards the annotation and validated against the schema, returning an LLM-friendly message naming the offending argument so the model can self-correct instead of a bare TypeError escaping the tool. A *missing* annotation now yields the empty (unconstrained) schema rather than a string. An unannotated parameter means "type unknown", and since arguments are now validated, claiming `string` rejected every correct non-string call — `lambda x: x + 1` could no longer be called with an int. Adds direct coverage for the type mapping, which previously had none: 38 cases across unions, enumerations, containers, formats and nested models, plus end-to-end checks that a tool's advertised schema matches what it accepts and survives conversion to OpenAI wire format. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

An async `on_approval_needed` handler auto-approved everything. The callback was typed as possibly-awaitable but invoked bare from the sync agent, so an `async def` handler returned a coroutine — always truthy — and every dangerous tool was silently approved, defeating the only human-in-the-loop mechanism. Callbacks now run through a bridge that drives awaitables to completion, carrying contextvars across the thread hop when a loop is already running. `Agent.stop()` was dead code: `_stop_requested` was written in six places and read nowhere, and the one test asserted only that the flag was set, so the suite certified a no-op. `stop()` now forwards to the conversation driving the run. It is tracked in `_active_conversation` rather than `_conversation`, because the latter is only populated when `persistent_conversation=True` — forwarding through it alone left `stop()` a silent no-op for the default agent. A `stop()` arriving before the conversation exists is replayed onto it rather than lost to the race. Exhausting `max_iterations` raised RuntimeError and discarded the whole run. The loop now takes one final turn with tools removed, so the model answers with what it has and `max_rounds_reached` records why — the worst case was exactly the weak local models that prompted tools target. Async tools ran coroutines via `asyncio.run` on a throwaway thread, which blocked the loop, dropped contextvars (tukuy `SecurityContext`, `current_tool_call_id`) and could deadlock; they now await natively. Adds a per-tool timeout, so a hung tool no longer wedges a run forever, exposed as `Agent(tool_timeout=...)` and overridable per call via `options={"tool_timeout": ...}`. Independent async calls execute concurrently via `asyncio.gather`; `zip(tool_calls, results)` pairs results with `strict=True` so a mis-pairing fails loudly instead of attaching a result to the wrong `tool_call_id`. Remaining sync/async drift and loop hardening: history trimming no longer orphans a `tool` message from its assistant `tool_calls` (which providers reject with a 400), malformed and truncated arguments are reported back to the model instead of executed as `{}`, tool results have a default size bound, `current_tool_call_id` is set on the sync path too, depth guards apply to every run path, `RunContext` is rebuilt per tool invocation so tools see live iteration and history, and `AgentStep` records the real tool name and a populated `duration_ms` instead of the call id and a dead field. Agent-level pre-call budget enforcement is removed: it read a freshly created, always-empty UsageSession and so never triggered. Enforcement lives in the Conversation, which sees real usage. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

Prompture had no MCP support, while every peer framework (OpenAI Agents SDK, Pydantic AI, LangChain) now consumes MCP servers. It maps cleanly onto the existing ToolRegistry interface — `list_tools()` becomes ToolDefinitions and `call_tool()` becomes the tool function — so MCP tools are called through the normal tool path with no special casing in the agent loop. `register_mcp_tools(registry, session)` registers every tool an initialized `mcp.ClientSession` advertises, with an optional `prefix` to avoid collisions across servers. `mcp_session_from_stdio()` is a convenience context manager for stdio servers, and `register_mcp_tools_sync()` covers sync `Agent` code outside an event loop. Results are flattened to strings: text blocks joined, non-text blocks (images, resources) reduced to a short placeholder, and MCP call errors returned as `Error: ...` so the model can react to them. Behind the optional `prompture[mcp]` extra, exported lazily so importing `prompture.integrations` never requires the dependency. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

Both streaming tests asserted a 200 but got 501, which is the endpoint correctly reporting that `sse-starlette` is unavailable rather than a defect. `sse-starlette` ships in the optional `serve` extra, so a valid install without it left the suite red. Skip via `importorskip`, matching the `importorskip("fastapi")` already at the top of both modules. The non-streaming tests need no extra and keep running. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

CI caught `test_typed_dict_expands` failing on Python 3.11: the schema had no `properties` at all. pydantic refuses `typing.TypedDict` below 3.12 (it requires the `typing_extensions` variant), so `TypeAdapter` raised, the except branch logged at debug level and fell through to the generic `{"type": "string"}` return. So on two of the four supported Python versions a TypedDict tool parameter was advertised to the model as a bare string — the exact silent degradation the richer type mapping was written to eliminate. It only looked fixed because local development runs 3.14, where pydantic accepts it. Rebuild the object schema from resolved type hints when TypeAdapter cannot, so field names and types survive on every supported version. Optionality is read per kind — `__required_keys__` for TypedDict, absent default/ default_factory for dataclasses, `is_required()` for models — and `required` is omitted entirely when it cannot be determined, so nothing is over-constrained. The fallback is exercised on all versions by forcing TypeAdapter to raise, rather than being covered only where pydantic happens to refuse; otherwise the path would stay untested on 3.12+ and regress unnoticed again. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

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Pull request overview

This PR hardens Prompture’s agent tool-calling pipeline end-to-end: it improves tool JSON Schema fidelity and argument validation, makes stop/approval/timeout behavior actually take effect across sync + async runs, normalizes provider stop reasons and tool-choice wiring, and adds optional MCP server tool integration via the normal ToolRegistry path.

Changes:

Expand tool schema generation + add argument coercion/validation to prevent malformed calls from executing and to give correctable feedback.
Rework conversation/agent tool loops for cooperative stop, graceful max-rounds completion, per-tool timeouts, concurrency correctness, and richer step metadata (tool name + duration).
Add MCP bridge (optional extra) and plumb tool-choice + stop-reason normalization through multiple drivers (OpenAI-compatible, Anthropic, Google, etc.).

Reviewed changes

Copilot reviewed 35 out of 35 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
tests/test_tukuy_bridge.py	Adds regression coverage for PEP-563 annotation resolution in tukuy bridge tool schemas.
tests/test_tools_schema.py	Adds extensive schema-generation and validation/coercion test coverage.
tests/test_tool_use.py	Tests richer docstring descriptions + graceful max-rounds behavior.
tests/test_simulated_tools.py	Updates simulated-tools loop tests to expect graceful completion.
tests/test_server.py	Skips SSE tests when the `serve` extra isn’t installed.
tests/test_openai_server.py	Skips SSE tests when the `serve` extra isn’t installed.
tests/test_mcp_bridge.py	Adds mocked unit tests for MCP tool discovery/registration/execution.
tests/test_live_events.py	Updates expectations for normalized stop reasons + raw preservation.
tests/test_conversation_robustness.py	Adds robustness contracts for malformed args, stop, timeouts, trimming, ordering, budgets.
tests/test_agent.py	Adds broad agent-level regression coverage for approval/stop/timeout/context/steps.
pyproject.toml	Adds `mcp` optional extra and includes it in `all`.
prompture/integrations/tukuy_bridge.py	Replaces duplicated code with a deprecated re-export alias.
prompture/integrations/mcp_bridge.py	Implements MCP tool bridge (registration + stdio session helper).
prompture/integrations/init.py	Lazily exports MCP bridge functions to avoid hard dependency.
prompture/extraction/tukuy_bridge.py	Repairs tukuy-emitted parameter schemas using resolved type hints.
prompture/drivers/openai_driver.py	Normalizes stop reasons, preserves raw stop reason, applies tool-choice options.
prompture/drivers/groq_driver.py	Normalizes stop reasons, preserves raw stop reason, applies tool-choice options.
prompture/drivers/google_driver.py	Adds tool-choice wiring, stop-reason normalization, and richer HTTP error type.
prompture/drivers/claude_driver.py	Adds tool-choice wiring, stop-reason normalization, and truncation-aware streamed tool parsing.
prompture/drivers/base.py	Adds shared stop-reason normalization, tool-choice translation, tool-call parsing with errors, HTTP error type.
prompture/drivers/async_openai_driver.py	Mirrors OpenAI buffered tool-call changes for async driver.
prompture/drivers/async_google_driver.py	Mirrors Google driver changes for async driver.
prompture/drivers/async_claude_driver.py	Mirrors Claude driver changes for async driver.
prompture/drivers/async_base.py	Improves streamed event generation for tool calls (ids + truncation flags).
prompture/drivers/_prompted_tool_stream.py	Makes prompted-tool streaming parser prefix-aware for chunk-split delimiters.
prompture/drivers/_openai_compat_stream.py	Adds thinking deltas, robust tool-stop finalization, and normalized MessageStop.
prompture/agents/types.py	Updates RunContext/AgentCallbacks docs to match new execution semantics.
prompture/agents/tool_grammars.py	Improves XML tool grammar parsing/rendering (quotes, multiline descriptions, prefix).
prompture/agents/live_events.py	Extends ToolUseStop event with truncation + raw stop reason metadata.
prompture/agents/conversation.py	Adds cooperative stop, graceful max-rounds, timeouts, history trimming, malformed-arg handling.
prompture/agents/async_conversation.py	Async parity for stop/max-rounds/timeouts/history trimming + parallel tool execution.
prompture/agents/async_agent.py	Fixes async tool/callback execution semantics; adds live contexts + step timing/name improvements.
prompture/agents/agent.py	Sync parity for stop/approval semantics; adds live contexts + step timing/name improvements.
docs/INTEGRATIONS.md	Documents MCP tool integration and installation flow.

+        pool = ThreadPoolExecutor(max_workers=1)
+        try:
+            future = pool.submit(self._tools.execute, name, arguments)
+            return future.result(timeout=timeout)


+    """End of one assistant turn. ``stop_reason`` uses the shared vocabulary
+    (``end_turn``, ``tool_use``, ``max_tokens``, ``content_filter``,
+    ``error``); drivers preserve the provider's raw value in
+    ``usage["raw_stop_reason"]``."""


+        text = _serialize_call_result(result)
+        if getattr(result, "isError", False):
+            return f"Error from MCP tool '{mcp_name}': {text}"
+        return text


jhd3197 and others added 8 commits August 5, 2026 16:47

Copilot AI lite review requested due to automatic review settings August 6, 2026 03:38

jhd3197 merged commit 6f8e57b into main Aug 6, 2026
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Harden agent tool calling and add MCP server support - #95

Harden agent tool calling and add MCP server support#95
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