Ein MCP-Server, der die kanonische Spezifikation für Tank Fight bereitstellt: Feld- und Fenstergröße, Panzer- und Geschossstatistiken, Spawn-Regeln, Punktevergabe, Steuerung, Menüverhalten, KI-Strategien, die Schwierigkeitskurve und ein zerstörbares Labyrinth pro Level.
Es läuft als lokaler Dienst auf einem Port. Jeder MCP-Client kann sich verbinden, fragen, welche Tools angeboten werden, und die Spezifikation abfragen – während das Spiel in Java, TypeScript oder in etwas anderem entwickelt wird.
Zwei Aufgaben, und die zweite ist wichtiger, als es zunächst scheint.
1. Unabhängige Implementierungen in Einklang halten. Zwei Builds desselben Spiels, getrennt geschrieben, driften in dem Moment auseinander, in dem sie eine Zahl brauchen, die niemand aufgeschrieben hat. Wenn beide denselben Dienst fragen, wie groß ein Panzer ist, entfällt das Rätselraten.
2. Verhindern, dass das Modell Anforderungen erfindet. Jedes Detail, das eine Spezifikation auslässt, ist eine Stelle, an der ein KI-Codierungsassistent stillschweigend etwas Plausibles wählt und weitermacht – bleiben Geschosse an Wänden hängen? können Verbündete sich gegenseitig beschießen? was passiert, wenn ein Spieler mitten in der Runde zerstört wird? Er erwähnt selten, dass er sich entschieden hat. Jede dieser Fragen wird hier beantwortet, sodass die Frage zu einer Nachschlageoperation wird statt zu einer Vermutung, und open_questions benennt, was wirklich nicht entschieden ist, sodass die ehrliche Antwort „einen Menschen fragen" ist, statt einer selbstbewussten Erfindung.
Related MCP server: 17-0 MCP Server
Erfordert uv.
uv syncuv run server.pyDas stellt die Spezifikation unter http://127.0.0.1:8082/mcp bereit. Verwenden Sie --port, um woanders zu lauschen, --host, um eine andere Schnittstelle zu binden, und --stdio für Clients, die nur stdio sprechen.
Jeder MCP-Client, beliebig viele gleichzeitig. Für Claude Code:
claude mcp add --transport http tank-spec http://localhost:8082/mcpFügen Sie --scope project hinzu, um es im Repository zu speichern, in dem Sie entwickeln, damit alle, die an diesem Build arbeiten, dieselbe Spezifikation erhalten. Dann /mcp, um die Verbindung zu bestätigen, und der Client kann den Server fragen, welche Tools existieren und wie man sie aufruft – hier muss nichts pro Client konfiguriert werden.
Tool | Antworten |
| Was in der Spezifikation steht, jeweils eine Zeile – hier beginnen |
| Ein Abschnitt oder ein Punktpfad wie |
| „Passiert X?" – findet die Regel, die das entscheidet |
| Was Level N bedeutet: Gesamtzahl der Gegner, maximale gleichzeitige, Spawn-Intervall, welches Labyrinth |
| Das Ziegellayout dieses Levels, in Kacheln expandiert, mit einem ASCII-Bild |
| Was die Spezifikation bewusst nicht entschieden hat |
Er hat keine Ahnung, wer verbunden ist oder was sie bauen, und das ist beabsichtigt. Er kennt die Spezifikation; Clients kennen den Server. Nichts zeigt in die andere Richtung.
Die Überprüfung, ob eine Implementierung noch der Spezifikation entspricht, ist also die Aufgabe des Clients. Ein Client kann die Werte aus seinem eigenen Code lesen und mit dem vergleichen, was diese Tools zurückgeben – er sitzt in diesem Repository, der Server nicht. Ein Server, der in einen Checkout greifen würde, um ihn zu inspizieren, müsste die Sprache, die Dateistruktur und den Pfad kennen und müsste jedes Mal aktualisiert werden, wenn eine weitere Implementierung auftaucht.
Aus demselben Grund wird die Spezifikation bereitgestellt und nicht als Datei mitgeliefert:
Sie liegt außerhalb jeder Implementierung. Eine Sitzung, die in einem Repository arbeitet, kann die Dateien eines anderen nicht lesen, aber sie kann einen Dienst aufrufen.
Sie wird abgefragt, nicht ausgeschüttet. search_spec("friendly fire") liefert eine Regel. Die gesamte Spezifikation in den Kontext eines Modells zu laden, um eine Frage zu beantworten, ist genau die Verschwendung, die das vermeidet.
Einige Antworten werden berechnet, nicht gespeichert. Level werden aus einer Rampenformel generiert – Level 6 existiert in keiner Datei. resolve_level ist der einzige korrekte Weg zu fragen, und seine Ganzzahl-Kürzung ist tragend: Eine Gleitkommaversion weicht bei vier der acht Level ab.
Alles innerhalb der Grenze ist Ziegel, und Ziegel kann weggeschossen werden – von beiden Seiten, da Seiten für Panzer wichtig sind, aber nie für Wände. Eine Kachel ist 20x20 und nimmt 40 Schaden, sodass zwei Standardgeschosse ein Loch öffnen, durch das Panzer dann fahren. Die Grenze selbst ist immun.
Jedes Level hat sein eigenes Labyrinth, sodass die Levelnummer der einzige Schwierigkeitsregler ist: Sie legt fest, wie viele Gegner ankommen, wie schnell, und das Gelände, in das sie ankommen. Level 1 ist fast offen; Level 8 ist so dicht, dass das Schießen eines Weges normalerweise schneller ist als das Finden eines.
get_maze(level) gibt die Rechtecke, die expandierte Kachelliste und ein Bild zurück:
########################################
#EE................EE................EE#
#EE................EE................EE#
#......................................#
#......BBBBBBBBBBB....BBBBBBBBBBB......#
...
#.............AA...AA...AA.............#
########################################Verwenden Sie die expandierten Kacheln, anstatt die Rechtecke selbst zu expandieren – dieser Schritt und die Regel, welche Kachel ein Geschoss beschädigt, wenn es zwei überbrückt, sind die Stellen, an denen zwei Builds am leichtesten unterschiedliche Löcher aus denselben Schüssen graben.
Das Hinzufügen eines Labyrinths ist nur das Hinzufügen von Rechtecken unter mazes.levels in der YAML. Die Testsuite hält es dann an die Invarianten, die ein Level spielbar machen: kachelausgerichtet, innerhalb des Bereichs, nicht überlappend, Spawn- und Verbündeten-Boxen frei, und jeder Einstiegspunkt zu Fuß erreichbar, ohne etwas zu zerstören – Graben ist eine Abkürzung, nie eine Anforderung.
spec/game-spec.yaml ist das Ganze, und es ist dafür gedacht, von Hand bearbeitet zu werden – das ist der Punkt. Der Server liest es bei jedem Aufruf erneut, sodass eine Änderung sofort wirksam wird, ohne etwas neu zu starten.
Die Spezifikation wurde aus einer funktionierenden Implementierung gespeist und ist von diesem Punkt an unabhängig davon. Sie führt: Ändern Sie zuerst einen Wert hier, dann lassen Sie die Implementierungen folgen.
uv run pytesttests/test_spec.py fixiert die Werte der Spezifikation und hält jedes Labyrinth an die Spielbarkeits-Invarianten. tests/test_server.py treibt den Server über einen MCP-Client – Tools entdecken, ihre Beschreibungen lesen, sie aufrufen. tests/test_http.py startet den echten Prozess auf einem Port und verbindet sich über HTTP, so wie ein Client es tut.
spec/game-spec.yaml the specification — the actual source of truth
server.py MCP server: tool definitions, their descriptions, and the transport
tank_spec/spec.py loading, searching, level maths, maze expansion
tests/ spec and maze invariants, MCP integration, HTTP transportGet a level's brick layout: the rectangles, and those rectangles expanded into the exact 20x20 tiles the level starts with.
Every level has its own maze, and all of the brick in it is destructible — bullets from either side chew through it, so the terrain changes as a round is played. Border walls are separate and indestructible.
Use the expanded tile list rather than expanding the rectangles yourself: that expansion is the step two implementations most easily get subtly different, and a one-tile disagreement changes which shots open a route.
Args: level: The level number. Out-of-range values are clamped. ascii_map: Include a picture of the layout — '#' border, 'B' brick, 'E' enemy entry point, 'A' ally start. Useful for checking a build renders the level you think it does; set false to save context.
| Name | Required | Description | Default |
|---|---|---|---|
| level | Yes | ||
| ascii_map | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 discloses that all bricks are destructible and terrain changes during play, and that border walls are indestructible. It also notes that out-of-range level values are clamped, which is a behavioral trait. However, it doesn't mention the return format or any potential side effects, but given the tool is a read operation, this is adequate.
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 well-structured with a clear opening, a rationale for using the expanded tiles, and a concise Args section. Every sentence adds value, and it's appropriately sized for the tool's complexity.
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 tool has an output schema (not shown but mentioned), the description doesn't need to explain return values. It covers the key aspects: what the tool returns, why to use it, parameter semantics, and behavioral notes. The description is complete for an agent to select and invoke the tool 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 description coverage is 0%, so the description must compensate. It explains the 'level' parameter (level number, out-of-range clamped) and 'ascii_map' (include a picture, useful for checking build, set false to save context). This adds meaning beyond the schema's basic types and defaults.
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's purpose: to get a level's brick layout as rectangles and expanded 20x20 tiles. It specifies the resource (level maze) and the action (get), and distinguishes it from siblings by focusing on the brick layout, while siblings like list_sections and get_spec handle other aspects.
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 explicitly advises using the expanded tile list rather than expanding rectangles manually, explaining the risk of subtle differences. It also provides guidance on when to set ascii_map to false to save context, and mentions that border walls are separate and indestructible, which helps the agent understand the tool's output.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return one section of the Tank Fight specification as JSON.
Use this to get the authoritative numbers and rules before implementing a feature — screen size, tank and bullet stats, wall layout, controls, AI parameters, and so on. Do not infer these values from an existing implementation and do not invent them; this file is what the implementations are supposed to agree with.
Args: section: A section name from list_sections, or a dotted path to go straight to a value, e.g. "entities.bullet" or "ai.enemy.turn_chance_per_tick".
| Name | Required | Description | Default |
|---|---|---|---|
| section | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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. It adds crucial context: the spec is authoritative, and it explicitly warns against inferring values from existing implementations. However, it does not mention error handling or behavior for invalid section names, so it's not fully transparent on edge cases.
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 compact and front-loaded, with a clear one-sentence summary followed by usage guidance and a well-formatted Args section. Every sentence adds value, and the formatting makes it easy to scan.
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 single-parameter tool with an output schema, the description is complete: it explains the purpose, parameter format, and authoritative nature of the data. It appropriately references list_sections for valid section names and provides examples of dotted paths. No additional return-value details are needed given the output schema.
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 only declares 'section' as a string with no description, so the description provides all parameter semantics. It explains that the value can be a section name from list_sections or a dotted path, with concrete examples like 'entities.bullet'. This is essential information not available from 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 opens with 'Return one section of the Tank Fight specification as JSON,' which is a specific verb+resource statement. It also explains the use case ('before implementing a feature') and distinguishes itself from sibling tools by focusing on retrieving a named section of the specification.
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 clearly states when to use the tool ('before implementing a feature') and warns against inferring or inventing values, which provides strong context. However, it does not explicitly name alternative tools or exclusions beyond referencing list_sections for section names, so it stops short of the explicit alternative guidance found in a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the sections of the Tank Fight specification, with a one-line summary of each, so you can fetch only the part you need.
Start here when you don't yet know where a rule lives. Fetching the whole spec is rarely necessary and wastes context.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It clearly discloses that the tool returns a section list with summaries rather than full content, and frames itself as a navigation helper. For a simple read-only listing tool, this is sufficient 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?
The description is two sentences long, front-loaded with the core purpose, and every phrase earns its place. It avoids filler while adding practical usage context.
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 zero-parameter listing tool with an output schema, the description is complete: it says what the tool returns, why to use it, and when to start with it. It sufficiently covers the low-complexity 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 tool has zero parameters and schema coverage is effectively 100%, so no parameter explanation is needed. The baseline of 4 applies because there is no param information to compensate for.
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 identifies the action ('List the sections'), the resource ('Tank Fight specification'), and the output form ('with a one-line summary of each'). It also differentiates from siblings by positioning this as the starting point for locating rules before fetching a specific part.
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 gives explicit guidance: 'Start here when you don't yet know where a rule lives' and warns that 'Fetching the whole spec is rarely necessary and wastes context.' It lacks an explicit contrast with search_spec, which is a sibling that may be a better alternative when users know search terms.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the design decisions the specification deliberately has NOT made.
Check this before inventing an answer to something the spec is silent on. If your question is here, the honest answer is "not decided yet" — say so and ask, rather than picking something and moving on. Anything on this list is a place where two implementations will otherwise diverge.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context: the tool returns a list of open items and hints that these are places where implementations diverge. However, no annotations are provided, so the description carries the burden. It does not state whether the list is static or generated, or any caching/rate limits, but for this kind of read-only tool, it is adequate. No contradiction.
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 paragraphs, each earning its place: purpose, timing, action on hit, consequence. No fluff, front-loaded with the core action.
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 zero-parameter tool, the description fully explains when and how to use it and what it returns. It doesn't describe output format, but an output schema exists, so that's not needed. It is complete for its complexity.
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 tool has zero parameters and schema coverage is 100%, so the description has little to add. The description explains what the output represents (open design decisions), adding semantic meaning beyond the empty schema. Baseline for 0 params is 4, and it meets that.
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 lists design decisions left open by the specification. It uses specific verb 'List' and resource 'design decisions', and distinguishes it from sibling tools like list_sections or get_spec by focusing on unresolved questions.
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?
Provides explicit guidance: check this tool before inventing an answer, and what to do if a question is here (say 'not decided yet' and ask). It implies when not to use (when question is not listed, it may be intentionally handled elsewhere).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Resolve what a level number actually means: how many enemy tanks the round fields in total, how many may be alive at once, and how often they arrive.
Levels are GENERATED from a ramp formula, not stored anywhere, so this cannot be answered by reading a file — call this tool instead of computing it yourself, because the formula truncates with integer division and a floating-point version disagrees at several levels.
The result also names the level's maze; call get_maze for its actual layout.
Args: level: The level number. Out-of-range values are clamped to the supported range rather than rejected.
| Name | Required | Description | Default |
|---|---|---|---|
| level | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals important traits: integer division with truncation, potential disagreement with floating-point calculations, and clamping behavior for out-of-range values. It also states what the result includes without exposing unnecessary details. This goes beyond a generic description and provides genuinely useful edge-case information.
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 appropriately concise, with a clear lead sentence, a paragraph explaining rationale, and a compact Args section. Every sentence earns its place—no filler or redundancy. The structure (purpose, why, related tool, parameter details) is logical and easy to scan.
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 tool's simplicity (one parameter) and the presence of an output schema, the description covers all necessary aspects: what it computes, why it's needed, how parameters behave, and what the output references (maze). It does not omit critical context and does not need to detail return types because an output schema is provided.
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 no description for the 'level' parameter (0% coverage), so the description must compensate. The Args section does this effectively by explaining the parameter's meaning and its clamping behavior, which is not present in the schema. This fully addresses potential confusion and guides correct usage.
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's purpose with a specific verb ('Resolve') and a detailed resource ('what a level number actually means: how many enemy tanks the round fields in total, how many may be alive at once, and how often they arrive'). It distinguishes itself from siblings by mentioning get_maze for maze layout and contrasting with reading a file. This is more than just a restatement of the name—it provides concrete capabilities.
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 explicitly tells when to use this tool instead of alternatives: 'Levels are GENERATED from a ramp formula, not stored anywhere, so this cannot be answered by reading a file — call this tool instead of computing it yourself'. It also directs to a sibling ('call get_maze for its actual layout'). This gives clear decision guidance, exceeding basic when-to-use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the specification for the rule or value that answers a question.
Use this whenever you are about to assume how the game behaves — "do bullets stop at walls?", "can allies shoot each other?", "how fast is a bullet?", "what happens when a player is destroyed?". Searching costs far less context than loading whole sections, and the answer is authoritative.
If the search comes back empty, the spec may genuinely not cover it: check open_questions() before deciding anything yourself.
Args: query: Plain words describing what you need, e.g. "friendly fire" or "spawn interval" or "health bar colour".
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It explains the tool is a read-only search ('costs far less context than loading whole sections') and the behavior on empty results (spec may not cover it, check open_questions). This adds transparency about the search's scope and fallback, though it doesn't detail return format or limits.
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 appropriately concise, using a short intro, a usage guideline paragraph, and a parameter explanation. Every sentence adds value, and it is front-loaded with the 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?
The tool is simple (one parameter) and the description covers purpose, usage, fallback behavior, and parameter semantics. The output schema exists, so return values don't need explanation. It is complete for a search tool.
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 'query' parameter with examples: 'Plain words describing what you need, e.g. "friendly fire" or "spawn interval" or "health bar colour".' This adds meaningful guidance beyond the schema's bare string type.
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's purpose: 'Search the specification for the rule or value that answers a question.' It uses a specific verb ('search') and resource ('the specification'), and provides concrete examples of questions it answers, distinguishing it from sibling tools like list_sections or get_spec.
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 explicitly guides when to use this tool: 'Use this whenever you are about to assume how the game behaves'. It contrasts with alternatives by noting that searching costs less context than loading whole sections. It also provides a clear fallback: if the search comes back empty, check open_questions() before deciding anything yourself.
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.
v0.1.0get_mazeget_speclist_sectionsopen_questionsresolve_levelsearch_specScored across 6 tools
Each tool has a distinct purpose: listing sections, getting a section, searching the spec, resolving level semantics, retrieving maze layouts, and listing open questions. No overlaps or ambiguity.
All tool names follow a consistent verb_noun pattern (list_, get_, search_, resolve_, open_), making the API predictable and easy to navigate.
Six tools is well-scoped for a specification server, covering all necessary access patterns without unnecessary redundancy or bloat.
The tool set fully covers the domain: discovering, retrieving, searching, interpreting, and visualizing spec details, plus handling open questions. No obvious gaps.
Read-only MCP server for the OPERANT AI operating-agent calibration benchmark.
Read-only MCP server for the WebAssembly spec: instructions, types, sections, search, proposals.
Public, read-only MCP server for FarmNeural company facts, packages, and capabilities.
Agent-native MCP server over the public saagarpatel.dev corpus. Read-only, stateless.