toolnexus 0.5.0

Provider-agnostic toolkit: dynamic MCP servers + agent skills for any LLM.

toolnexus

Build an agent in a few lines. Point at an mcp.json and a skills/ folder, call run(), and you have a working agent — MCP servers, agent skills, your own functions, and HTTP endpoints unified as one tool set, driving any LLM.

Right-sized. Not a framework (no builders, no config to wade through), not a toy that falls over the moment you need streaming or a retry. Everything a real agent needs — the loop, hooks, streaming, retries, memory — and nothing it doesn't.

The Python port of toolnexus — the same library, byte-identical, also in JavaScript, Go, Java, and C#. Built on the official MCP Python SDK (the mcp package). Python ≥ 3.11.

Install

pip install toolnexus

Quick start — a working agent in 5 lines

No mcp.json, no skills folder. The 10 built-in tools (bash, read, grep, webfetch, …) are on by default, so the model can actually do things right away:

import asyncio
from toolnexus import create_toolkit, create_client

async def main():
    tk = await create_toolkit()                          # built-in tools, on by default
    agent = create_client(
        base_url="https://openrouter.ai/api/v1", style="openai",
        model="deepseek/deepseek-chat",                  # any OpenRouter/OpenAI/Anthropic model
    )
    res = await agent.run("List the files here, then count them.", tk)
    print(res.text)
    await tk.close()

asyncio.run(main())

export OPENROUTER_API_KEY=...      # or OPENAI_API_KEY / ANTHROPIC_API_KEY

create_client reads the key from OPENROUTER_API_KEY / OPENAI_API_KEY / ANTHROPIC_API_KEY (no api_key= needed).

With MCP servers + skills

The MCP SDK is async, so the toolkit is async:

import asyncio
from toolnexus import create_toolkit, create_client


async def main():
    # 1. tools from an mcp.json + a skills/ folder
    tk = await create_toolkit(mcp_config="./mcp.json", skills_dir="./skills")

    # 2. point at any OpenAI- or Anthropic-style endpoint
    agent = create_client(
        base_url="https://openrouter.ai/api/v1",
        style="openai",                # or "anthropic"
        model="openai/gpt-4o-mini",
    )

    # 3. run — skills injected, tools called for you, looped to an answer
    res = await agent.run("Refund order 1234 for the customer.", tk)
    print(res.text)
    await tk.close()


asyncio.run(main())

The Toolkit is also an async context manager (async with await create_toolkit(...) as tk:) if you'd rather not call close() yourself.

Conversations / memory

run() is stateless — each call starts fresh. For a multi-turn thread that remembers, use ask(prompt, tk, id=...). Give it an id and the client's ConversationStore does the work: load that thread's transcript → run → save the updated transcript. The next ask with the same id continues where it left off. Call ask without an id and it's a stateless one-shot — identical to run.

agent = create_client(base_url="https://openrouter.ai/api/v1", style="openai",
                      model="openai/gpt-4o-mini")

await agent.ask("I trade NIFTY.", tk, id="trader-42")
res = await agent.ask("What do I trade?", tk, id="trader-42")
print(res.text)   # -> "NIFTY" — the second turn remembers the first

Every client has a store — by default an in-memory InMemoryConversationStore that lives as long as the client. To persist across processes (a file, a DB, Redis), pass your own to create_client:

from toolnexus import create_client, ConversationStore

class FileStore:                                  # implements ConversationStore
    async def get(self, id):                      # -> list[messages] | None
        ...
    async def save(self, id, messages):           # persist the updated transcript
        ...

agent = create_client(base_url=..., style="openai", model=..., store=FileStore())

ConversationStore is just two async methods — get(id) and save(id, messages). The A2A serve side uses the same store: an inbound peer's turns are keyed by their A2A contextId, so a served agent remembers a caller across tasks (see A2A agents).

Streaming with memory. The id also works while streaming. Pass on_text (a sync- or async-callable) to ask to stream text deltas as they arrive — ask still returns the final RunResult — or iterate stream() directly. With an id, the thread is loaded before the stream and saved on the terminal done event.

# block-style: stream deltas, still get the RunResult back — remembered under `id`
res = await agent.ask("Draft a reply.", tk, id="trader-42",
                      on_text=lambda delta: print(delta, end="", flush=True))

# async iterator: consume text + tool events; `id` makes it stateful (load before, save on done)
async for ev in agent.stream("And summarise it.", tk, id="trader-42"):
    if ev["type"] == "text":
        print(ev["delta"], end="", flush=True)
    elif ev["type"] == "done":
        print("\n", ev["result"].usage)

Observability / metrics

Zero-dependency, two outputs from one internal instrumentation — both opt-in, no cost when unused.

on_metric — a semantic event feed. Pass it to create_client and it receives a readable, snake_case dict at each significant point: {"event": "llm", "model", "status", "ms", "prompt_tokens", "completion_tokens"} per model call, {"event": "tool", "tool", "source", "is_error", "ms"} per tool call, and a terminal {"event": "run", "model", "turns", "tool_calls", "total_tokens", "ms", "error"?} per run/ask. Forward it anywhere (statsd, logs, OpenTelemetry).

agent = create_client(
    base_url=..., style="openai", model=...,
    on_metric=lambda ev: print("[metric]", ev["event"], ev),
)

agent.metrics() — built-in Prometheus text. The same events feed a tiny in-memory registry that renders the Prometheus text exposition format (no third-party dep). Mount it at GET /metrics:

from http.server import BaseHTTPRequestHandler, HTTPServer

class Handler(BaseHTTPRequestHandler):
    def do_GET(self):
        if self.path == "/metrics":
            body = agent.metrics().encode()
            self.send_response(200)
            self.send_header("Content-Type", "text/plain; version=0.0.4")
            self.end_headers()
            self.wfile.write(body)
        else:
            self.send_response(404)
            self.end_headers()

HTTPServer(("", 9090), Handler).serve_forever()

Series: toolnexus_llm_requests_total{model,status}, toolnexus_llm_tokens_total{type}, toolnexus_tool_calls_total{tool,source,is_error}, toolnexus_run_errors_total{model}, plus the toolnexus_llm_request_duration_seconds and toolnexus_tool_duration_seconds histograms. The rendered text is byte-identical across all five ports; OTLP push is a planned future companion.

Add your own tools

from toolnexus import define_tool, http_tool

# a plain function → a tool (schema inferred from the signature)
def add(a: float, b: float) -> str:
    """Add two numbers and return the sum."""
    return str(a + b)

tk.register(define_tool(add, name="add"))

# a REST endpoint → a tool
tk.register(http_tool(
    name="create_ticket", description="Create a ticket", method="POST",
    url="https://api.example.com/tickets",
    headers={"Authorization": "Bearer ${API_TOKEN}"},   # ${ENV} expands from os.environ, never logged
    input_schema={"type": "object", "properties": {"title": {"type": "string"}}, "required": ["title"]},
))

URL {placeholders} are filled from args; the rest become the JSON body. Non-2xx → ToolResult(output="HTTP <status>: <body>", is_error=True).

Built-in tools

A fifth source ships 10 built-in tools — bash, read, write, edit, grep, glob, webfetch, question, apply_patch, todowrite (names + input schemas match opencode) — so an agent can act with zero wiring. They appear in the tool schema (to_openai()/to_anthropic()/to_gemini()), like MCP tools — not the system prompt.

On by default. One global toggle turns the whole source off, or a per-tool tools map disables individual builtins on the all-on baseline:

tk = await create_toolkit(mcp_config="./mcp.json", builtins=False)
# also accepts {"disabled": True} or {"enabled": False}

# per-tool: drop bash, keep the other nine (unknown names ignored; whole-source-off still wins)
tk2 = await create_toolkit(mcp_config="./mcp.json", builtins={"tools": {"bash": False}})

bash/write/edit/apply_patch run commands and mutate the filesystem — the toggle is the off-switch for locked-down hosts.

A2A agents (agent-to-agent)

Call remote A2A agents (each of their skills becomes a tool) and serve your own toolkit as an agent other A2A peers can call. A genuine, minimal subset of real A2A (JSON-RPC 2.0; Agent Card at /.well-known/agent-card.json; SendMessage → poll GetTask). No streaming / push / auth in v1.

Outbound — call a remote agent. Each advertised skill becomes a tool named <agent>_<skill> (source="a2a"):

from toolnexus import create_toolkit, agent

tk = await create_toolkit(
    agents=[agent("https://researcher.example.com/.well-known/agent-card.json")],
)

# or add one at runtime (an Agent or a bare card URL):
await tk.add_agent("https://writer.example.com/.well-known/agent-card.json")

agent(card, *, headers=None, timeout=None, poll_every=None) — headers support ${ENV} expansion (never logged); timeout / poll_every are milliseconds (300000 / 1000 defaults). A config file can also carry an agents block. A failing agent is isolated — contributes no tools, never fatal.

Inbound — serve your toolkit as an agent. The Agent Card is built from your SKILL.md skills (never raw tools):

from toolnexus import create_client

agent_client = create_client(base_url="https://openrouter.ai/api/v1", style="openai", model="openai/gpt-4o-mini")

handle = await tk.serve("127.0.0.1:0", client=agent_client, a2a={
    "name": "research-agent",
    "description": "Answers research questions.",
    # "skills": ["hello-world"],   # subset of skills to advertise; omit ⇒ all
    "store": "memory",             # "memory" (default) | "file:<dir>" | a custom TaskStore
})
print(handle.url)                  # GET /.well-known/agent-card.json ; POST / (SendMessage / GetTask)
await handle.stop()

serve(addr, *, client, a2a=None, on_task=None) fulfils each inbound task through the client: a message carrying an A2A contextId goes through client.ask(..., id=contextId), so a peer's turns are remembered across tasks via the client's ConversationStore; without a contextId it's a stateless client.run. Task persistence is a separate pluggable TaskStore (in-memory default, "file:<dir>", or your own).

Serve as an MCP server (be a gateway)

The inbound mirror of A2A: expose your whole toolkit as an MCP server so any MCP client — an IDE, another agent, a remote host — can call its tools. Point toolnexus at N MCP servers + skills + your own functions, then re-expose the union as one MCP server. Unlike A2A, the MCP client is the LLM host, so each tools/call dispatches straight to the tool's execute — no client, no tasks, no store.

# streamable-HTTP — an embeddable MCP server mounted at POST /mcp, beside any A2A routes:
srv = await tk.serve(
    "127.0.0.1:0",
    mcp={"name": "my-gateway"},   # optional "tools": ["echo"] subset; omit ⇒ every toolkit tool
    on_call=lambda ev: print(ev["name"], ev["ms"], ev["is_error"]),
)
print(srv.url + "/mcp")   # connect any MCP client here
await srv.stop()

tools/list advertises every toolkit tool (name verbatim, inputSchema = the tool's parameters); mcp["tools"] narrows the surface. The mcp profile can also live in the config file as a top-level mcpServer block (singular — distinct from the client-side mcpServers). (Transport is streamable-HTTP; a stdio transport for local clients like Claude Desktop is a planned follow-up.)

Bring your own loop

Don't want the host loop? Use the schema adapters and execute calls yourself:

tools  = tk.to_openai()        # or tk.to_anthropic() / tk.to_gemini()
system = tk.skills_prompt()    # skills catalog for your system prompt (opens with a preamble telling the model to use the skill tool)
# when the model returns a tool call { name, arguments }:
res = await tk.execute(name, arguments)   # -> ToolResult(output, is_error, metadata)

The four sources

Source	How
MCP servers	an mcp.json (mcpServers/servers/mcp); local stdio + remote streamable-HTTP, headers for auth
Agent skills	a folder of <name>/SKILL.md; a skill tool loads each on demand + a system-prompt catalog
Native tools	define_tool(fn) / the @tool decorator — a function becomes a tool
HTTP / REST	http_tool(...) — an endpoint becomes a tool, ${ENV} headers

All four appear as one uniform Tool in tk.tools(), with source in "mcp" | "skill" | "custom".

API

Python	Description
await create_toolkit(...)	async factory → Toolkit
create_client(..., store=?, on_metric=?)	the unified host loop; store is the ConversationStore (default in-memory); on_metric is the metric-event sink
await agent.run(prompt, tk)	one stateless agent loop → RunResult(text, messages, tool_calls, usage, …)
await agent.ask(prompt, tk, *, id=None, on_text=None)	with id: remembers the thread via store (get → run → save); without: one-shot (= run); on_text streams text deltas
agent.stream(prompt, tk, *, id=None)	streaming variant — async-iterate text/tool/usage/done events; id ⇒ stateful
agent.metrics()	Prometheus text exposition of cumulative metrics — mount at GET /metrics
ConversationStore / InMemoryConversationStore	async get(id) / async save(id, messages) — implement for file/db; in-memory default
tk.tools() / tk.get(name)	the uniform tools
await tk.execute(name, args, ctx=None)	run a tool → ToolResult
tk.skills_prompt()	system-prompt skill catalog
tk.mcp_status()	per-server connection status
tk.to_openai() / to_anthropic() / to_gemini()	provider tool schemas
tk.register(*tools)	add native/http/custom tools
await tk.serve(addr, client=…, a2a=…)	serve the toolkit as an A2A agent
await tk.close()	disconnect MCP servers

More

Full docs, the other four language ports, the shared behavior spec, and runnable examples: https://github.com/muthuishere/toolnexus

MIT licensed.