plr-mcp
This MCP server lets any MCP client (e.g., Claude Desktop) control lab automation instruments via PyLabRobot — no direct coding required. It supports both simulation (chatterbox) and real hardware (Hamilton STAR, Opentrons OT-2, Tecan Freedom EVO).
Liquid Handler Setup & Deck Management
setup_deck— Initialize the liquid handler with a chosen backend, auto-load tip rack and 96-well platedeck_state— Inspect the current deck layout, loaded resources, and run mode
Liquid Handling Operations
pick_up_tips/drop_tips— Pick up or return tips using a well range (e.g.,A1,A1:H1)aspirate/dispense— Aspirate or dispense a specified volume (µL) from/to plate wellstransfer— Full transfer in one step: pick up tips → aspirate → dispense → drop tips
Plate Reader
read_plate— Read in absorbance (configurable wavelength), fluorescence (excitation/emission/focal height), or luminescence (focal height) mode
Thermocycler Control
thermocycler— Set block/lid temperature, open/close lid, deactivate, or check status
Heater-Shaker Control
heater_shaker— Set temperature, shake (with RPM and optional duration), stop, deactivate, or check status
All tools work end-to-end in simulation mode by default, requiring no physical hardware.
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., "@plr-mcpSetup the deck and transfer 100 µL from A1 to A2"
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.
plr-mcp
A Model Context Protocol server for PyLabRobot. It exposes a liquid handler, a plate reader, a thermocycler, and a heater-shaker as MCP tools, so any MCP client (Claude Desktop, Claude Code, or your own agent) can run lab-automation steps by calling tools instead of writing PyLabRobot code.
It ships in simulation mode by default. Every tool runs end to end against PyLabRobot's chatterbox backends with no instruments attached, so you can try the whole thing on a laptop. Point it at real hardware by setting one environment variable (see below).
Verified against PyLabRobot 0.2.1.
Why an MCP server (and not just tool-use)
Driving PyLabRobot from a Claude skill or direct tool-calls is tool-use inside one agent. An MCP server is a standalone process that speaks the Model Context Protocol over stdio, so any MCP client can discover and call these tools without knowing anything about PyLabRobot. This repo is the server.
Related MCP server: my-mcp-server
Install
git clone https://github.com/di-omics/plr-mcp.git
cd plr-mcp
pip install -e .This pulls in mcp and pylabrobot.
Prove it works (no hardware)
python examples/smoke_test.pyIt drives every tool through the chatterbox backends and prints ALL OK when
the run succeeds.
Run the server
plr-mcp # stdio transport, chatterbox simulation
PLR_MCP_BACKEND=star plr-mcp # target a real liquid handler insteadTools
Every tool is registered under a plr_ prefix so it stays unambiguous when this
server is loaded next to others.
Tool | What it does |
| Zero-motion hardware pre-flight: open the link to a real instrument, read its identity, close. Does not move the arm. See the hardware bring-up guide. |
| Build the liquid handler for the chosen backend and, for the liquid handler family, place a tip rack and a 96-well plate. Call this first. |
| List the resources on the deck and the run mode. |
| Pick up tips from the tip rack for a well range (for example |
| Return tips to the rack. |
| Aspirate a volume from each plate well in a range. |
| Dispense a volume into each plate well in a range. |
| One head pass: pick up, aspirate, dispense, drop. |
| Read absorbance, fluorescence, or luminescence. |
| Set block or lid temperature, open or close the lid, deactivate, status. |
| Set temperature, shake, stop, deactivate, status. |
| Generate a tool-agnostic, UMI-aware single-cell RNA-seq workflow with runtime-configured hooks for optional demultiplexing, UMI preprocessing, alignment, counting, and downstream analysis. Hook implementations are not bundled. |
| Run a validated PCR enrichment round 1 master-mix protocol by importing and executing the operator's existing starlab script (not a reimplementation). |
Well ranges use PyLabRobot syntax: a single well A1, a column A1:H1, or a
partial column A1:D1.
Tool semantics
Beyond names, the tools carry machine-readable metadata so an agent can use them safely:
Annotations. Each tool advertises MCP hints (
readOnlyHint,destructiveHint,idempotentHint,openWorldHint). Probes likeplr_connect_check,plr_deck_state, andplr_read_plateare read-only;plr_setup_deck(withhome), the liquid-handling tools,plr_thermocycler,plr_heater_shaker, andplr_run_pcr_enrichment_round1are marked destructive, so a client can warn before anything moves on real hardware.simulatedflag. Every result includessimulated.truemeans the numbers came from a chatterbox backend with no instrument attached; never read asimulated: truevalue as a real measurement.Structured output. Tools declare an output schema and return
structuredContent, so clients parse results against a named shape instead of an opaque object (requiresmcp>=1.9).Errors. Invalid arguments and unmet preconditions (unknown backend, bad well range, moving before
setup_deck/home) are raised as tool errors. Expected operational states a correct call can still hit (hardware unreachable from this host, missing vendor extra, a human-gated real run awaitingconfirm=true) come back as a normal result withok: falseand anoteslist to act on.
Connect a client
Claude Code
This repo ships a project-scoped .mcp.json, so just open the repo
in Claude Code and approve the plr server when prompted (check /mcp or
claude mcp list). It starts on the chatterbox backend, and Claude Code loads
CLAUDE.md for the tool catalog and safety rules. Tools appear
prefixed plr_ (for example plr_aspirate).
Prefer to register it yourself instead:
claude mcp add --transport stdio plr -- plr-mcpEither way, plr-mcp must be on PATH (pip install -e .); otherwise use the
absolute path from which plr-mcp, or -- python -m plr_mcp.server.
Claude Desktop
Add this to claude_desktop_config.json:
{
"mcpServers": {
"plr": {
"command": "plr-mcp"
}
}
}If plr-mcp is not on the client's PATH, use the absolute path to the console
script (which plr-mcp) or run it as python -m plr_mcp.server.
Backends
Pick the liquid-handling backend with PLR_MCP_BACKEND, or override it per
session in a setup_deck call (backend="star", etc.).
Backend | PyLabRobot backend | Deck | Runs with no hardware |
|
| STARLet | yes (default) |
|
| STARLet | no |
|
| OTDeck | no |
|
| EVO150 | no |
Only chatterbox runs with no instrument. The other three construct the real
PyLabRobot backend (correct API for 0.2.1) and attempt to connect; if no
instrument is reachable, or a vendor extra such as pylabrobot[opentrons] is
not installed, setup_deck reports that in notes instead of crashing. The
liquid handler tip and plate auto-load only for chatterbox and star; ot2 and
evo use vendor-specific labware, so load your own.
Driving a real STAR moves a physical arm. setup_deck(home=true) homes the
channels and iSWAP. The star backend defaults to a zero-motion connect and
blocks every liquid-handling tool until you home on a clear deck. Follow the
hardware bring-up guide for the first run.
For ot2, pass the robot IP:
PLR_MCP_BACKEND=ot2 PLR_MCP_OT2_HOST=169.254.1.1 plr-mcpThe non-liquid-handling instruments (plate reader, thermocycler, heater-shaker)
run on chatterbox simulation and expose real hardware backends as clearly
marked extension points in plr_mcp/lab.py (the _ensure_* methods). Wire in
your own (for example an Inheco ODTC thermocycler or a BioTek reader) and
validate on your deck before trusting a run.
Running a validated protocol
run_pcr_enrichment_round1 does not reimplement a protocol. It imports an existing,
hardware-validated starlab script and calls its own functions. Liquid parameters,
geometry, and tip logic remain operator-owned in that external run card; the MCP
wrapper does not bundle them and fails closed when the transfer contract is
incomplete. Point it at the scripts:
export PLR_MCP_STARLAB_DIR=/path/to/plr-tested/liquid-handler/starlab_liveOn a real run, follow the same ladder the scripts require: a clean
chatterbox dry-run, then mode='deck' on the instrument (assignment only),
then the transfer with a person watching. The star backend refuses to run
without confirm=true, because a real run homes the arm and moves liquid.
Layout
.mcp.json Claude Code project-scoped registration (starts on chatterbox)
CLAUDE.md guide Claude Code auto-loads (tools + safety rules)
plr_mcp/
lab.py stateful PyLabRobot wrapper (all the real calls live here)
server.py FastMCP server, one thin tool per Lab method
schemas.py TypedDict result shapes (the tools' output schemas)
protocols.py validated starlab protocol wrappers (run_pcr_enrichment_round1)
analysis.py single-cell RNA-seq pipeline generator
tests/
test_lab.py pytest suite, runs on chatterbox (no hardware)
examples/
smoke_test.py end-to-end run with no hardware
evals/
plr_mcp_eval.xml agent-usability questions answerable on chatterboxDevelopment
pip install -e '.[dev]'
ruff check plr_mcp tests # lint
ruff format --check plr_mcp tests
mypy plr_mcp --check-untyped-defs
pytest -qCI runs all four on Python 3.10 through 3.13 for every push and pull request.
License
MIT
Available Tools
10 toolsaspirateB
Aspirate volume microliters from each plate well in wells.
| Name | Required | Description | Default |
|---|---|---|---|
| wells | Yes | ||
| volume | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavioral traits. It only states the basic operation without mentioning side effects (e.g., tip retention, safety, or timing). For a liquid handling tool, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise single sentence with no redundant information. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description lacks essential context for a lab automation tool, such as whether tip pickup is required or the tool's role in a sequence. It is minimally complete for a simple action.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning by clarifying units (microliters) and the scope ('from each plate well in wells'). However, with 0% schema coverage, it does not fully compensate; e.g., the format of 'wells' (list, range) is unspecified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (aspirate) and the target resource (plate wells), specifying the volume per well. It distinguishes from siblings like 'dispense' and 'transfer' by focusing on aspiration only.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'dispense' or 'transfer'. No prerequisites or context provided, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
deck_stateA
List the resources currently assigned to the deck and the run mode.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. 'List' implies a read operation, but no explicit statement of safety, side effects, or permission requirements. The description is minimal and lacks details on behavior when deck is not configured.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the action and resource. No unnecessary words, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description adequately covers the tool's purpose. However, it could be slightly improved by noting that it returns a list or state without side effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so schema coverage is 100%. The description does not need to add parameter information. Baseline for zero parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'List' and identifies the resource as 'the resources currently assigned to the deck and the run mode'. It clearly distinguishes from sibling action tools like aspirate or dispense which perform operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for checking state but provides no explicit guidance on when to use this tool versus alternatives like setup_deck or read_plate. No exclusion criteria or prerequisites are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dispenseB
Dispense volume microliters into each plate well in wells.
| Name | Required | Description | Default |
|---|---|---|---|
| wells | Yes | ||
| volume | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must provide behavioral details. It only states the basic action; it omits critical info such as whether tips are needed, behavior for invalid wells or volumes, error handling, and side effects (e.g., if the deck state changes).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with backtick parameter references. No unnecessary words or repetition; it is optimally succinct.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of lab automation (e.g., tip requirements, plate compatibility), the description lacks essential context. No explanation of return values, required labware state, or sequencing with sibling tools like 'pick_up_tips'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain parameter semantics. It names the parameters but does not specify format for 'wells' (e.g., list, range) or constraints for 'volume' (e.g., min/max, units beyond microliters). This is insufficient for correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (dispense) and the target resource (each plate well), with specific parameter references (volume, wells). It effectively distinguishes from sibling tools like aspirate (withdraw) and transfer (move liquid between locations).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs. alternatives. While the name and description imply use for adding liquid to wells, there is no mention of prerequisites, excluded scenarios, or comparisons to siblings like aspirate or transfer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
drop_tipsC
Return tips to the tip rack at the given range.
| Name | Required | Description | Default |
|---|---|---|---|
| wells | No | A1:H1 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It does not mention whether tips are returned to the rack for reuse or discarded, what happens if no tip is attached, or any side effects like tip ejection. The description is insufficient for an agent to understand the tool's impact.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but lacks structure (e.g., bullet points). It is front-loaded with the verb, but the brevity sacrifices clarity. It could be improved by adding a second sentence without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (1 parameter, no output schema) and no annotations, the description is incomplete. It does not mention prerequisites (e.g., must have tips attached), the tip rack specification, or cleanup. The agent is left with insufficient information to use the tool correctly in a workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description only uses the word 'range' to allude to the 'wells' parameter. It does not explain the parameter's format, default value, or meaning. For a single parameter, the description should specify how to specify the wells, but it fails to add value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action 'Return tips' and the object 'tip rack', and mentions 'given range' which loosely maps to the 'wells' parameter. However, it does not explicitly state that this is for dropping tips from a pipette into specific wells, and the phrase 'Return tips' is ambiguous about whether they are recycled or discarded. It distinguishes from sibling 'pick_up_tips' but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With siblings like 'pick_up_tips', 'aspirate', 'dispense', it is implied to be used after using tips, but the description does not address prerequisites or conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
heater_shakerA
Control the heater-shaker. action is one of: set_temperature (needs
temperature in Celsius), shake (needs speed in rpm, optional duration in
seconds), stop, deactivate, status.
| Name | Required | Description | Default |
|---|---|---|---|
| speed | No | ||
| action | Yes | ||
| duration | No | ||
| temperature | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It lists actions but omits behavioral details like whether deactivate turns off heating or what status returns. Adequate but minimal insight into side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise: one sentence with purpose then bullet-like action list. Every word is informative, no redundancy. Front-loaded with core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple device control tool with 4 parameters (1 required) and no output schema, the description covers essential use cases. It could be more complete by explaining return values or differences between stop and deactivate, but overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema lacks descriptions (0% coverage). The description compensates by explaining the action parameter values and clarifying units (Celsius, rpm, seconds) for relevant parameters, adding value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool controls the heater-shaker and enumerates specific actions (set_temperature, shake, stop, deactivate, status), distinguishing it from sibling tools which are all different lab operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use each action and what parameters are needed (e.g., set_temperature requires temperature in Celsius). However, it does not explicitly state conditions for not using the tool or mention alternatives, though siblings are distinct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pick_up_tipsA
Pick up tips from the tip rack. wells is a PyLabRobot range such as
'A1', 'A1:H1' (a full column), or 'A1:D1'.
| Name | Required | Description | Default |
|---|---|---|---|
| wells | No | A1:H1 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits like tip consumption, deck state changes, or potential errors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no extraneous information: first states purpose, second explains the sole parameter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple tool with one parameter, but lacks context about return values, side effects, or when this action is valid.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains that 'wells' is a PyLabRobot range with examples ('A1', 'A1:H1'), adding significant meaning beyond the schema's type and default.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Pick up' and the resource 'tips', and distinguishes from sibling tools like aspirate, dispense, and drop_tips.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like drop_tips, or prerequisites such as needing tips available on the deck.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_plateA
Read the plate in the reader. mode is 'absorbance' (uses wavelength),
'fluorescence' (uses excitation/emission/focal_height), or 'luminescence'
(uses focal_height).
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | absorbance | |
| emission | No | ||
| excitation | No | ||
| wavelength | No | ||
| focal_height | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose what the tool returns (e.g., a reading value or spectrum), whether a plate must be present, or any side effects. More behavioral detail is needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the action, and provides essential mode-parameter mapping without extraneous words. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters and no output schema, the description is incomplete. It explains mode dependencies but omits the return value (e.g., measured data), prerequisites, and typical usage patterns. A more complete description would include result format and operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the tool description compensates by explaining how the mode parameter determines which other parameters (wavelength, excitation/emission, focal_height) are relevant. This adds significant meaning beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Read the plate in the reader,' a specific verb and resource. It distinguishes this tool from siblings like aspirate, dispense, and transfer, which involve liquid handling rather than plate reading.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the three modes (absorbance, fluorescence, luminescence) and which parameters they use, providing clear context for when each mode is appropriate. However, it does not explicitly state when to use this tool over alternatives or note any prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
setup_deckA
Initialize the liquid handler and place labware. Call this before any liquid handling tool.
backend: 'chatterbox' (simulation, no hardware), 'star' (Hamilton STAR), 'ot2' (Opentrons OT-2, needs host), or 'evo' (Tecan Freedom EVO). Defaults to the server's configured backend. For chatterbox and star a 1000 uL tip rack and a Corning 96-well plate are auto-loaded onto a STARLet deck. host: OT-2 robot IP address (only used when backend='ot2').
| Name | Required | Description | Default |
|---|---|---|---|
| host | No | ||
| backend | No | ||
| tip_rail | No | ||
| plate_rail | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It mentions auto-loading labware for chatterbox and star, but is silent on behavior for evo and ot2 backends. It does not state whether the operation is safe (non-destructive), what happens on default backend, or any side effects. Significant gaps remain.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loads the primary purpose. It lists backends efficiently but could be better structured (e.g., separate parameter explanations). No extraneous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the essential purpose and usage context but omits return value, error conditions, and details for all backends. Given the tool's init role, mentioning the auto-loaded labware is helpful, but ignoring tip_rail/plate_rail and not explaining the deck_state relationship leaves gaps in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must explain all parameters. It explains 'backend' and 'host' but provides no description for 'tip_rail' and 'plate_rail', which are integers with defaults. These missing explanations leave the agent unable to use the tool correctly, despite the description partially compensating for some parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool initializes the liquid handler and places labware, using a clear verb+resource structure. It distinguishes itself from sibling tools by stating it must be called before any liquid handling operations, and enumerates the supported backends.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear guidance: 'Call this before any liquid handling tool.' It also details when specific parameters are needed (host only for ot2). While it doesn't explicitly state when not to use it, the context makes it obvious that this is a prerequisite action, not an alternative to other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
thermocyclerA
Control the thermocycler. action is one of: set_block (needs
block_temp), set_lid (needs lid_temp), open_lid, close_lid, deactivate,
status. Temperatures are in Celsius.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | ||
| lid_temp | No | ||
| block_temp | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that temperatures are in Celsius and which parameters are needed for specific actions. Missing details include valid temperature ranges, behavior on invalid input, and whether actions are idempotent or have side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the main purpose. Every word contributes: the tool purpose, action list, parameter dependencies, and unit. No redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple control tool with 3 parameters, no output schema, and no annotations, the description covers the essential actions and parameters. However, it does not explain default behavior when optional parameters are omitted, nor does it cover error cases or sequencing. It is minimally complete but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains the meaning of block_temp and lid_temp (temperatures for block and lid) and their coupling to specific actions. The action parameter's enumeration is documented inline. However, it lacks valid ranges or behavior when parameters are null.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool controls a thermocycler and enumerates the possible actions. The verb 'control' plus resource 'thermocycler' makes purpose evident. Although no sibling does the same, the description distinguishes the tool from liquid handling siblings by listing specific thermocycler actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by listing actions and their needed parameters (e.g., 'set_block (needs block_temp)'). However, it does not explicitly state when to use which action or when not to use them. No alternatives are mentioned, but no sibling overlaps with this functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
transferA
Transfer volume microliters from source wells to dest wells in one
head pass (pick up tips, aspirate, dispense, drop tips). Source and dest
ranges must have the same well count, at most one column.
| Name | Required | Description | Default |
|---|---|---|---|
| dest | Yes | ||
| tips | No | ||
| source | Yes | ||
| volume | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description fully discloses the operation sequence (pick up tips, aspirate, dispense, drop tips) and constraints (same well count, at most one column). Provides clear behavioral model.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, efficient and front-loaded: first sentence states action and process, second adds constraints. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Provides key behavioral and constraint details for a 4-parameter tool with no output schema. Missing explanation of 'tips' parameter and potential errors, but covers essential usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description carries burden. Explains source, dest (ranges), volume (microliters), but does not explain the 'tips' parameter (e.g., tip reuse or type). Partial coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool transfers liquid from source to dest wells, with explanation of the head pass process. Distinguishes well from sibling tools like aspirate, dispense, pick_up_tips, and drop_tips.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage as a combined operation, but no explicit guidance on when to use transfer versus performing separate steps. Lacks alternative suggestions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
10 tool updates
v0.1.0- First observed
aspirate - First observed
deck_state - First observed
dispense - First observed
drop_tips - First observed
heater_shaker - First observed
pick_up_tips - First observed
read_plate - First observed
setup_deck - First observed
thermocycler - First observed
transfer
TDQS
Scored across 10 tools
Each tool targets a distinct operation (aspirate, dispense, transfer, tip handling, deck setup, heater-shaker, thermocycler, plate reader) with no overlaps. Actions are clearly separated.
All tool names use lower_snake_case with clear verb_noun patterns (e.g., pick_up_tips, drop_tips, read_plate). Naming is uniform and predictable.
10 tools cover core liquid handling and lab equipment operations without being excessive. Each tool earns its place for a robotics lab server.
Covers setup, liquid transfers, tip management, temperature control, and reading. Minor gaps like explicit mixing or pause/move commands exist, but transfer can handle mixing.
Maintenance
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