Ein Model Context Protocol (MCP)-Server, der SAT-, SMT- und Constraint-Lösungsfunktionen für große Sprachmodelle bereitstellt.
Der MCP Solver integriert SAT, SMT und Constraint Solving mit LLMs über das Model Context Protocol und ermöglicht KI-Modellen die interaktive Erstellung, Bearbeitung und Lösung von:
Eine detaillierte Beschreibung der Systemarchitektur und der theoretischen Grundlagen des MCP-Solvers finden Sie im begleitenden Forschungspapier: Stefan Szeider, „MCP-Solver: Integrating Language Models with Constraint Programming Systems“ , arXiv:2501.00539, 2024.
Related MCP server: mcp-optimizer
Im Folgenden bezieht sich „Element“ auf einen Teil des Codes (MinZinc/Pysat/Z3) und „Modell“ auf die Kodierung.
Werkzeugname | Beschreibung |
| Entfernen Sie alle Elemente aus dem Modell |
| Neues Element an einem bestimmten Index hinzufügen |
| Element am Index löschen |
| Element am Index ersetzen |
| Aktuelle Modellinhalte mit nummerierten Elementen abrufen |
| Lösen Sie das Modell (mit Timeout-Parameter) |
Python und Projektmanager uv
Python 3.11+
Modusspezifische Anforderungen: MiniZinc, PySAT, Python Z3 (erforderliche Pakete werden über Pip installiert)
Betriebssysteme: macOS, Windows, Linux (mit entsprechenden Anpassungen)
MCP Solver erfordert Python 3.11+, den uv Paketmanager und solverspezifische Abhängigkeiten (MiniZinc, Z3 oder PySAT).
Ausführliche Installationsanweisungen für Windows, macOS und Linux finden Sie unter INSTALL.md .
Schnellstart:
git clone https://github.com/szeider/mcp-solver.git
cd mcp-solver
uv venv
source .venv/bin/activate
uv pip install -e ".[all]" # Install all solversDer MCP-Solver bietet drei verschiedene Betriebsmodi, die jeweils mit einem anderen Backend zur Constraint-Lösung integriert sind. Jeder Modus erfordert spezifische Abhängigkeiten und bietet einzigartige Funktionen zur Lösung unterschiedlicher Problemklassen.
Der MiniZinc-Modus ermöglicht die Integration mit der MiniZinc-Einschränkungsmodellierungssprache mit den folgenden Funktionen:
Umfangreicher Einschränkungsausdruck mit globalen Einschränkungen
Integration mit dem Chuffed-Constraint-Solver
Optimierungsmöglichkeiten
Zugriff auf Lösungswerte über get_solution
Abhängigkeiten : Erfordert das minizinc Paket ( uv pip install -e ".[mzn]" )
Konfiguration : Um im MiniZinc-Modus zu laufen, verwenden Sie:
mcp-solver-mznDer PySAT-Modus ermöglicht die Interaktion mit dem Python SAT-Lösungs-Toolkit mit den folgenden Funktionen:
Propositionale Constraint-Modellierung mit CNF (Konjunktive Normalform)
Zugriff auf verschiedene SAT-Solver (Glucose3, Glucose4, Lingeling usw.)
Kardinalitätsbeschränkungen (höchstens k, mindestens k, genau k)
Unterstützung für die Lösung boolescher Einschränkungen
Abhängigkeiten : Erfordert das Paket python-sat ( uv pip install -e ".[pysat]" )
Konfiguration : Um im PySAT-Modus zu laufen, verwenden Sie:
mcp-solver-pysatDer Z3-Modus bietet Zugriff auf die Lösungsfunktionen von Z3 SMT (Satisfiability Modulo Theories) mit den folgenden Funktionen:
Umfangreiches Typsystem: Boolesche Werte, Ganzzahlen, Realzahlen, Bitvektoren, Arrays
Constraint-Lösung mit Quantifizierern
Optimierungsmöglichkeiten
Vorlagenbibliothek für gängige Modellierungsmuster
Abhängigkeiten : Erfordert das z3-solver Paket ( uv pip install -e ".[z3]" )
Konfiguration : Um im Z3-Modus zu laufen, verwenden Sie:
mcp-solver-z3Der MCP Solver enthält einen MCP-Client für Entwicklungs-, Experimentier- und Diagnosezwecke, basierend auf dem ReAct -Agenten-Framework. Dieser Client dient als Vermittler zwischen einem LLM und dem MCP-Server und erleichtert die Übersetzung natürlichsprachlicher Problemstellungen in formale Constraint-Programmierungslösungen.
# Install client dependencies
uv pip install -e ".[client]"
# Verify client installation and configuration
uv run test-setup-clientDer Client benötigt einen API-Schlüssel von einem LLM-Anbieter. Für Anthropic (das Standard-LLM ist Claude Sonnet 3.7) setzen Sie die Umgebungsvariable ANTHROPIC_API_KEY . Diese kann in Ihrer Umgebung festgelegt oder in einer .env Datei im Projektstammverzeichnis eingefügt werden.
Der Client unterstützt auch andere LLM-Anbieter, darunter OpenAI, Google (Gemini), OpenRouter und sogar lokale Modelle über LM Studio. Sie können das zu verwendende Modell mit dem Kommandozeilenflag --mc angeben. Details zu den Modellcodeformaten finden Sie in INSTALL.md .
Der Client stellt spezielle Befehle für jedes Solver-Backend bereit:
# MiniZinc mode
uv run run-test mzn --problem <path/to/problem.md>
# PySAT mode
uv run run-test pysat --problem <path/to/problem.md>
# Z3 mode
uv run run-test z3 --problem <path/to/problem.md>Dies basiert auf einem Beispiel von George Johnson (NYT, 13. Juli 1999).
Benutzer
Angenommen, ein Theaterregisseur fühlt sich verpflichtet, entweder seine junge Frau, die Schauspielerin Alvarez, oder seinen Neffen, den Schauspieler Cohen, in einer Produktion zu besetzen. Doch Miss Alvarez will nicht mit Mr. Cohen (ihrem ehemaligen Liebhaber) in einem Stück mitspielen und verlangt daher, dass ihre neue Flamme, der Schauspieler Davenport, in die Besetzung aufgenommen wird. Der Produzent, der sich selbst revanchieren muss, besteht darauf, dass Schauspieler Branislavsky eine Rolle bekommt. Doch Mr. Branislavsky will in keinem Stück mit Miss Alvarez oder Mr. Davenport mitspielen. Kann der Regisseur das Stück besetzen?
System: clear_model
Model cleared
System: add_item (mehrere)
var bool: alvarez; var bool: cohen; var bool: branislavsky; var bool: davenport; constraint alvarez \/ cohen; % Must cast either Alvarez or Cohen constraint not (alvarez /\ cohen); % Alvarez won't work with Cohen constraint alvarez -> davenport; % If Alvarez is cast, Davenport must be cast constraint branislavsky; % Branislavsky must be cast constraint not (branislavsky /\ alvarez); % Branislavsky won't work with Alvarez constraint not (branislavsky /\ davenport); % Branislavsky won't work with Davenport solve satisfy;
System: solve_model
{'status': 'SAT', 'solution': Solution(alvarez=False, cohen=True, branislavsky=True, davenport=False, _checker=''), 'solve_time': 0.0}
Die Lösung (Cohen und Branislavsky besetzen, aber nicht Alvarez oder Davenport) erfüllt alle Einschränkungen.
Benutzer
Überprüfe, ob du n Damen auf einem nxn Schachbrett platzieren kannst. Versuche n=10,20,30,40 und vergleiche die Lösungszeiten.
System: clear_model
Model cleared
System: add_item (mehrere)
int: n = 10; array[1..n] of var 1..n: queens; constraint alldifferent(queens); constraint alldifferent([queens[i] + i | i in 1..n]); constraint alldifferent([queens[i] - i | i in 1..n]); solve satisfy;
System: solve_model (mit n=10, 20, 30 und 40 durch Ändern des Wertes von n)
Vergleich der Lösungszeiten: n=10: 0,001 s n=20: 0,005 s n=30: 0,040 s n=40: 0,043 s
Für alle getesteten Platinengrößen gibt es Lösungen. Die Zeitkomplexität steigt nichtlinear an, bleibt aber bis n=40 beherrschbar.
Benutzer
Eine Verkäuferin aus Wien plant ihre nächste Österreich-Tour und besucht dabei jede Landeshauptstadt einmal. Helfen Sie mit, die kürzeste Route zu finden. Entfernungen in km: 1=Wien, 2=St. Pölten, 3=Eisenstadt, 4=Linz, 5=Graz, 6=Klagenfurt, 7=Salzburg, 8=Innsbruck, 9=Bregenz
[Distanzmatrixtabelle in Benutzernachricht bereitgestellt]
System: add_item (mehrere)
include "globals.mzn"; int: n = 9; array[1..n,1..n] of int: dist = [|0,65,60,184,195,319,299,478,631|65,0,125,119,130,254,234,413,566|60,125,0,184,157,281,261,440,593|184,119,184,0,208,252,136,315,468|195,130,157,208,0,136,280,459,629|319,254,281,252,136,0,217,391,566|299,234,261,136,280,217,0,188,343|478,413,440,315,459,391,188,0,157|631,566,593,468,629,566,343,157,0|]; array[1..n] of var 1..n: succ; var int: total_dist = sum(i in 1..n)(dist[i,succ[i]]); constraint circuit(succ); solve minimize total_dist;
System: solve_model
{'status': 'SAT', 'solution': Solution(objective=1564, succ=[2, 4, 1, 7, 3, 5, 9, 6, 8], _checker=''), 'solve_time': 0.002}
Optimale Route: Wien → St. Pölten → Linz → Salzburg → Bregenz → Innsbruck → Klagenfurt → Graz → Eisenstadt → Wien mit einer Gesamtstrecke von 1.564 km.
Über dieses Formular können Sie dem Autor Feedback geben.
Dieser MCP-Solver befindet sich im Prototypstadium und sollte mit Vorsicht verwendet werden. Benutzer werden ermutigt, zu experimentieren, aber die Verwendung in kritischen Umgebungen erfolgt auf eigene Gefahr.
Dieses Projekt ist unter der MIT-Lizenz lizenziert – Einzelheiten finden Sie in der Datei LICENSE .
Execute Python code in the persistent solving kernel.
Runs in the kernel set up by select_backend unless kernel_id overrides it. State persists across calls (variables, imports). Raise timeout (seconds, max 300) for long solver runs.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| timeout | No | ||
| kernel_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so the description carries the full burden. It usefully discloses that state (variables, imports) persists across calls and that timeout caps at 300 seconds, but says nothing about error behavior, sandboxing, or how execution output is captured/returned.
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?
Four short lines, purpose front-loaded, with each remaining sentence adding a distinct operational fact (kernel source, state persistence, timeout cap). No filler or restated name text.
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?
No output schema exists, yet the description never describes what an execution returns (stdout, errors, result value), which is a meaningful gap for a code-execution tool. Kernel selection, persistence, and timeout behavior are covered, so the definition is workable but not complete.
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, and it does for two of three params: kernel_id is explained as overriding the kernel established by select_backend, and timeout is defined in seconds with a max of 300. The 'code' param is left implicit but is self-evident from the tool purpose.
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?
States a specific verb+resource ('Execute Python code') and adds the key qualifier that it runs in the persistent solving kernel, which differentiates it from a one-shot execution. It references select_backend for kernel setup, but never contrasts itself with submit_code, so sibling distinction is only partial.
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?
Gives implied usage guidance: raise the timeout for long solver runs, and rely on state persisting across calls. However, it never says when to prefer this over submit_code or when to instead use python_interrupt/python_reset, so the caller must infer routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Interrupt running code in the solving kernel (state is preserved).
| Name | Required | Description | Default |
|---|---|---|---|
| kernel_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does disclose one meaningful trait — that state is preserved rather than discarded — but says nothing about what happens if nothing is running, whether interruption is cooperative, permissions, or the result of the call.
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?
A single front-loaded sentence with no filler; the verb and effect lead the reader immediately. It is efficient, though it is arguably too terse for the behavioral gaps it leaves.
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 one-parameter tool with no output schema and no annotations, the description covers purpose and one behavioral note. It omits how to target a specific kernel and what the caller observes after interrupting, leaving the agent to guess at invocation details.
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?
One parameter (kernel_id, nullable, default null) has 0% schema description coverage, and the description adds no meaning: it never explains what kernel_id identifies, that it is optional, or what the default targets. The 'solving kernel' phrasing hints at a default target but is not tied to the parameter.
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?
States a specific verb ('interrupt') and resource ('running code in the solving kernel'), plus a scope qualifier ('state is preserved') that separates it from python_reset. It does not name siblings explicitly, so the differentiation is inferable rather than stated.
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?
Usage is implied by the verb: call it when running code needs to be stopped without losing kernel state. There is no explicit guidance on when to prefer this over python_reset, nor any prerequisite or condition (e.g., only valid while execution is in flight).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new kernel (no kernel_id) or reset one (with kernel_id).
Prefer select_backend, which installs the right solver packages for you. The kernel created or reset here becomes the target of bare python_exec calls.
| Name | Required | Description | Default |
|---|---|---|---|
| packages | No | ||
| kernel_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It usefully discloses that the created or reset kernel becomes the target of bare python_exec calls, but it does not mention that resetting may discard existing kernel state, nor does it explain the packages parameter's effect.
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 three short sentences with no wasted text. The main behavior is front-loaded, followed by the alternative tool guidance and the downstream effect on python_exec.
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 two-parameter, state-changing tool with no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It covers purpose and one parameter but leaves packages undocumented and does not describe reset side effects or state loss.
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 for both parameters. It explains kernel_id's effect well by describing create-without vs reset-with, but it completely omits the packages parameter, leaving half the parameters undocumented.
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 gives a specific verb and resource, and it clearly distinguishes the two modes: no kernel_id creates a new kernel, while a provided kernel_id resets one. It also names the related sibling tool select_backend, so the tool's role is immediately identifiable.
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?
It explicitly says to prefer select_backend when solver packages are needed, which is useful routing guidance. The create-vs-reset condition is also clear, though the description does not explicitly state when not to use python_reset or what prerequisites exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check kernel state: active kernels, defined variables, packages.
| Name | Required | Description | Default |
|---|---|---|---|
| kernel_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. 'Check' implies a read-only operation, but it does not explicitly state that it is non-destructive, nor does it describe side effects, authorization needs, or rate limits. It only lists what is inspected, which is minimal behavioral context.
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?
A single, front-loaded sentence with no filler. It efficiently conveys the core purpose in one clause.
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 no-required-parameter tool with no output schema, the description gives an outline of what is returned (kernels, variables, packages). However, it omits usage context and parameter semantics, leaving gaps an agent must infer.
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 schema has 0% description coverage and the description never mentions the optional kernel_id parameter. It does not explain what the parameter does, when to supply it, or what the default behavior is when omitted.
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 'Check' and resource 'kernel state', and enumerates the inspected items (active kernels, variables, packages). It is clear but does not differentiate this tool from siblings like python_exec or python_interrupt.
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 when-to-use guidance is given. The description does not say when to prefer this over python_exec or python_interrupt, nor does it state any prerequisites like requiring an active kernel.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Start solving a constraint problem: call this FIRST, once per problem.
Sets up a persistent IPython kernel preloaded with the backend's solver library and helper functions, and returns the modeling instructions for that backend. Calling it again recycles the kernel for a new problem (previous session state is cleared).
Backends:
pysat: Boolean satisfiability — feasibility, combinatorial search
maxsat: weighted optimization over Boolean constraints
z3: SMT — integers/reals/bitvectors, proofs, program verification
cpmpy: finite-domain constraint programming — scheduling, assignment, puzzles
clingo: Answer Set Programming — logic rules, defaults, reachability
didp: dynamic programming (state-space search) — routing, sequencing, packing
Unsure which backend fits? Read the resource mcp-solver://guide first.
Then iterate with python_exec (write, run, verify against the problem statement), and finish by calling submit_code with the final, verified, self-contained program. UNSAT / "no solution exists" is a valid outcome.
| Name | Required | Description | Default |
|---|---|---|---|
| solver | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and delivers: persistent kernel lifetime, that re-invocation clears previous session state, that it returns modeling instructions, and that UNSAT is a legitimate terminal outcome. These are exactly the behavioral traits an agent needs and that the schema does not express.
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?
Front-loaded imperative first sentence, then setup behavior, then the backend list, then the workflow tail. The bulleted backend table is dense with information and every line earns its place; no filler.
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?
No output schema exists, but the description says what is returned (modeling instructions) and sketches the full three-stage workflow ending in submit_code. For a single-parameter entry-point tool, an agent has everything required to select and invoke it correctly.
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 single 'solver' parameter has no enum, so the description must supply the value domain — and it does, enumerating all six backends with a one-line semantic gloss each (e.g. 'z3: SMT — integers/reals/bitvectors, proofs, program verification'). It does not explicitly state that these strings are the accepted values of the solver argument, so the mapping is inferred rather than stated.
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?
Specific verb+resource: 'Start solving a constraint problem' and sets up a persistent IPython kernel preloaded with the backend solver library. It explicitly positions itself against siblings by naming the full workflow (python_exec to iterate, submit_code to finish), so an agent can place it without opening other schemas.
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?
Explicit when-to-use ('call this FIRST, once per problem'), re-use semantics ('Calling it again recycles the kernel for a new problem'), an escape hatch for uncertainty ('Unsure which backend fits? Read the resource mcp-solver://guide first'), and the follow-on tools. Nothing is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit the final, verified, self-contained solver program.
Call ONCE at the end, only after executing the program via python_exec and passing your verification. The code is syntax-checked; on success it is stored and linked as a resource (the server keeps the last 50).
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses validation behavior (syntax-checked), the failure path, and what happens on success (stored and linked as a resource, server retains the last 50). It omits whether resubmission overwrites a prior submission and what an error response looks like.
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?
Three short sentences, front-loaded with the action and artifact, then the timing constraint, then the side effect. No filler and nothing repeated from the schema.
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 single-parameter submission tool with no annotations and no output schema, the description covers purpose, timing, validation, and persistence/retention. Remaining gaps (retry semantics, error format) are minor given the tool's simplicity.
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 schema itself only says 'code: string', so the description has to define the payload; it does so by specifying the code must be the final, verified, self-contained solver program. It stops short of stating language/format constraints, but the payload's required qualities are communicated.
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?
States a specific verb (submit) plus the exact resource (the final, verified, self-contained solver program), which is distinct from the sibling execution/status tools. It does not explicitly name which sibling it is contrasted with, but the artifact it accepts is unambiguous.
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?
Gives explicit timing ('Call ONCE at the end'), an explicit prerequisite ('only after executing the program via python_exec and passing your verification'), and names the sibling tool involved. An agent cannot mistake when this should fire.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v4.1.0python_execpython_interruptpython_resetpython_statusselect_backendsubmit_codeScored across 6 tools
Most tools are clearly distinct: python_status, python_interrupt, python_exec, and submit_code each have a unique role. The only mild overlap is between select_backend and python_reset (both create/configure kernels), but descriptions explicitly steer users to prefer select_backend, resolving the ambiguity.
Four tools share a consistent python_<action> prefix (python_status, python_interrupt, python_exec, python_reset), but select_backend and submit_code break this pattern with a verb_noun convention. Two coexisting conventions make it mixed but still readable.
Six tools is well-scoped for a constraint-solving workflow: backend setup, execution, kernel management, and final submission. Each tool earns its place with no redundancy.
The lifecycle (select_backend -> python_exec iterate -> submit_code, with status/interrupt/reset for kernel control) is well covered. Minor gaps: no tool to list or retrieve previously submitted solutions (only a resource mention), but core workflows are complete.
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