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Unity MCP Efficient

CI Python 3.10+ License: MIT

Una fachada MCP independiente y eficiente en tokens, con una skill de Codex para MCP for Unity.

Mantiene todo el conjunto de funciones de Unity detrás de una API pequeña orientada al modelo. El modelo ve seis herramientas estables, busca en el catálogo upstream bajo demanda, recibe respuestas compactas adaptadas a Unity y puede encadenar trabajo rutinario sin incorporar todos los resultados intermedios a la conversación.

[!IMPORTANT] Este proyecto no sustituye al paquete de Unity ni a su servidor Python. MCP for Unity sigue siendo el backend. Registra la fachada, no el servidor upstream, con el cliente de IA. Exponer ambas superficies reduce la mayoría del ahorro de contexto.

Reducción de contexto medida

On 2026-08-24, the scripts in benchmarks/ produced these results against MCP for Unity v10.1.0 (c14de1e6).

Medición

Superficie upstream

Fachada eficiente

Reducción

Herramientas visibles para el modelo

48

6

87.50%

Esquemas de herramientas serializados

93,119 caracteres

3,657 caracteres

96.07%

Tokens de esquema aproximados¹

23,280

915

96.07%

Jerarquía sintética de 250 objetos²

628,110 caracteres

875 caracteres

99.86%

Tokens de jerarquía aproximados¹

157,028

219

99.86%

Superficie de argumentos de acción

5,566 campos

1,798 campos

67.70%

El catálogo dinámico indexó 377 operaciones de Unity. Una pequeña suite de búsqueda determinista en inglés/ruso devolvió la operación esperada en el rango 1 en los 15 casos y dentro del top 3 en los 15 casos. La candidata a versión pasa 35 pruebas de la fachada. Una prueba de humo real no modificó el proyecto y verificó la superficie de seis herramientas, editor.refresh en el puesto 1, el estado del editor y un lote de inspección de escena en dos pasos contra un Editor de Unity abierto.

¹ Los recuentos de tokens usan una estimación transparente de cuatro caracteres por token. Describen la huella de contexto, no la facturación de la API. La tokenización real varía según el modelo y la carga útil.

² El benchmark de jerarquías modela una respuesta ruidosa con 250 objetos, 300 índices de vértice por objeto y los mismos datos en cargas útiles de texto y estructuradas. Este caso extremo no garantiza la misma reducción en todas las escenas. Consulta BENCHMARKS.md para el método y los valores brutos.

Related MCP server: SimpleMcpServer

Qué hacen las seis herramientas

Herramienta

Propósito

search_capabilities

Encuentra las mejores operaciones de Unity a partir de una breve frase de tarea en inglés o ruso. Los esquemas completos son opcionales.

call_operation

Ejecuta una operación exacta y devuelve una vista previa acotada más un identificador de resultado recuperable.

batch_operations

Ejecuta hasta 50 pasos acotados de tipo call, select, assert, poll, foreach y emit en una sola ida y vuelta del modelo.

inspect_unity

Lee el estado del proyecto, del editor, de la escena, de la consola o de los objetos seleccionados, con supresión orientada a revisiones.

get_result

Pagina, filtra, toma nota o selecciona datos ya producidos sin repetir el trabajo de Unity.

get_viewport

Devuelve una imagen acotada de la vista Scene o Game sin duplicarla en el JSON estructurado.

El modelo sigue llegando a las 377 operaciones indexadas detrás de la fachada. Esas operaciones ya no ocupan el prompt todas a la vez.

Problemas gestionados en el límite

Modo de fallo común

Lo que cambia la fachada

El cliente envía decenas de esquemas de herramientas de gran tamaño antes de empezar el trabajo útil

Una superficie de seis herramientas con descubrimiento de operaciones bajo demanda

Las herramientas de management exponen una gran unión de argumentos para cada acción, ejecutando todos

Esquemas específicos por acción; 41 67.70% menos campos de argumento en el catálogo medido

Las respuestas de escena, consola, pruebas y activos inundan el conversation

Procesado específico para Unity, presupuestos de salida estrictos, paginación y identificadores de resultados

Un objeto Root de FastMCP o Pydantic no puede serializarse en JSON

Normalización JSON recursiva antes de construir la vista previa

Unity completa una mutación pero se desconecta durante la recarga de dominio

Persistencia del resultado bruto, metadatos de reintento explícitos y sin replay automático de mutaciones

Una prueba surge, pero su gran envoltorio oculta o pierde job_id

Un recibo asíncrono compacto diseñado para el polling

La respuesta upstream oculta success: false bajo un éxito a nivel de transporte

Un un ok externo refleja el resultado de Unity real

Repetir una mutación con timeout puede duplicar el trabajo

Recibos con request_id estable que se sustituyen comúnmente

El trabajo multobjeto consume un turno del modelo por operación

Flujos de trabajo secuenciales acotados y paralelización conservadora solo para lecturas

stale_status o is_changing provocan polling sin fin

Comprobaciones con retraso, control de revisiones y una regla de parada clara

Arquitectura

Codex or another MCP client
        |
        | sees 6 tools
        v
Unity MCP Efficient (stdio by default)
        |-- capability search over the live upstream catalog
        |-- compact Unity-specific post-processing
        |-- bounded workflow runtime
        |-- local SQLite result and request receipts
        |
        | HTTP, default http://127.0.0.1:8080/mcp
        v
MCP for Unity server
        |
        v
Unity Editor package

Una skill por sí sola no puede esconder los esquemas de herramientas que un cliente MCP ya ha cargado. Por eso este repositorio contiene dos piezas:

  • la fachada impone la API más pequeña y las respuestas compactas;

  • la skill enseña a Codex cómo buscar, agrupar, recuperar y verificar de forma eficiente.

Instalación

1. Iniciar MCP for Unity en modo HTTP

Instala CoplayDev/MCP for Unity usando el método crudo del proyecto. En Unity, abre Window → MCP, for Unity, selecciona transporte HTTP local y arranca el servidor.

El endpoint por defecto upstream es:

http://127.0.0.1:8080/mcp

Si tu proyecto usa otro puerto, pásalo mediante UNITY_MCP_BACKEND_URL incluida más abajo.

2. Registrar la fachada en Codex

Instala uv si es necesario, y luego, ejecuta:

codex mcp add unity-efficient \
  --env UNITY_MCP_BACKEND_URL=http://127.0.0.1:8080/mcp \
  -- uvx --from git+https://github.com/Vangardo/unity-mcp-efficient.git unity-mcp-efficient

En PowerShell, usa el mismo comando en una sola línea:

codex mcp add unity-efficient --env UNITY_MCP_BACKEND_URL=http://127.0.0.1:8080/mcp -- uvx --from git+https://github.com/Vangardo/unity-mcp-efficient.git unity-mcp-efficient

Elimina o desactiva cualquier entrada directa a MCP for Unity en el mismo cliente de Codex. Mantén el servidor HTTP upstream en ejecución, pero no registres su superficie de 48 herramientas para este modelo.

3. Instalar la skill de Codex

La vía más fácil es pedir a Codex:

$skill-installer Install the skill from https://github.com/Vangardo/unity-mcp-efficient/tree/main/skills/unity-mcp-efficient

Para installación manual a nivel de usuario, clona el repositorio y copia skills/unity-mcp-efficient en:

$HOME/.agents/skills/unity-mcp-efficient

Codex detecta automatic los cambios de skill. Reinícialo si la skill no aparece.

Otros clientes MCP

Usa esta forma de configuración stdio:

{
  "mcpServers": {
    "unity-efficient": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/Vangardo/unity-mcp-efficient.git",
        "unity-mcp-efficient"
      ],
      "env": {
        "UNITY_MCP_BACKEND_URL": "http://127.0.0.1:8080/mcp"
      }
    }
  }
}

Instala la skill empaquetada si el cliente es compatible con Agent Skills. La fachada funciona sin ella, pero la skill sea la capacidad básica para mejorar la selección y recuperación de errores.

Bucle de agente recomendado

  1. Lee una sola vez el estado por defecto de Unity.

  2. Haz una búsqueda con una frase concreta.

  3. Solicite el esquema completo de la operación cuando sus argumentos no estén claros.

  4. Agrupa en lote secuencial el trabajo dependiente. Paraleliza solo lecturas independientes, no mutaciones de Unity.

  5. Mantén la salida compacta y expande los resultados guardados por página o por ruta.

  6. Haz la revisión semántica del resultado, no de cada objeto brutal intermedio.

Este bucle ya se describe en skills/unity-mcp-efficient/SKILL.md.

Configuración

Variable

Por defecto

Significado

UNITY_MCP_BACKEND_URL

http://127.0.0.1:8080/mcp

Endpoint HTTP del MCP for Unity upstream

UNITY_MCP_OPERATION_TIMEOUT

60

Tiempo de espera por operación, en segundos

UNITY_MCP_RESULT_DB

Directorio de caché del usuario

Ruta donde almacenar de resultados SQLite; usa memory para almacenamiento de un solo proceso

UNITY_MCP_EFFICIENT_TRANSPORT

stdio

Transporte de la fachada: stdio, http o sse

Usa el transporte stdio por defecto a menos que tengas una razón para exponer la fachada por red. Lee SECURITY.md antes de usar HTTP o SSE.

Desarrollo y verificación

git clone https://github.com/Vangardo/unity-mcp-efficient.git
cd unity-mcp-efficient
uv sync --extra dev
uv run pytest -q

Con el servidor HTTP upstream en ejecución:

uv run python benchmarks/evaluate_facade.py
uv run python benchmarks/measure_surface.py
uv run python benchmarks/live_smoke.py

evaluate_facade.py y measure_surface.py leen el catálogo upstream en vivo. live_smoke.py hache mutaciones, pero requiere un Editor de Unity abierto y conectado.

Límites conocidos

  • La salida compacta es intencionadamente con pérdidas. El resultado crudo queda disponible a través de get_result durante un tiempo acotado.

  • La fachada no hace seguras las mutaciones arbitrarias de Unity. Permisos y revisiones siguen siendo una tarea de cliente MCP y del usuario.

  • El modo paralelo es conservador, ya que Unity puede serializar internamente el trabajo del editor.

  • Evita las capaces opcionales desplegables, como Soporte de Roslyn, aunque no estén presentes en un proyecto Unity concreto.

  • La captura de las vistas Scene y Game puede omitan la superposición IMGUI o solapas del editor.

  • Las pruebas de compatibilidad usan MCP for Unity v10.1.0. El catálogo es dinámico; ejecuta comparativas de versiones futures antes de afirmar compatibilidad.

Origen del diseño

Tomamos la idea de divulgación progresiva del proyecto Vangardo/mcp_hub, una puerta MAC más amplia que redirige un catálogo grande de integraciones mediante una interfaz pequeña de búsqueda y llamada. Unity MCP Efficient aplica esa disciplina de contexto en Unity y, además, añade compresiones específicas de Unity, revisiones de estado, protección tras mutativas, capturas de pantalla de visión y flujos locales acotados.

Si necesitas este patrón para Slack, Teamwork, Telegram, calendario, memoria, automatización o agentes integrados con varios marketplaces, consulta MCP Hub.

El backend de compatibilidad es CoplayDev/MCP for Unity, distribuido con la Licencia MIT. Este repositorio es un proyecto independiente y no incluye su código fuente. Ve [NOTICE.md] y [THIRD_PARTY_NOTICES.md].

Licencia y marcas comerciales

El código original de este repositorio está disponible con la Licencia MIT.

Unity es una marca comercial o registrada de Unity Technologies o sus filiales en partes de todo el mundo. Este proyecto no está afiliado ni permanentizado por Unity Technologies ni por CoplayDev. Los demás nombres y marcas pertenecen a sus respectivos propietarios.

Available Tools

6 tools
batch_operationsB
Destructive

Run calls or local select/assert/poll/foreach/emit workflow steps.

NameRequiredDescriptionDefault
executionNosequential
max_charsNo
operationsYes
request_idNo
output_modeNocompact
response_modeNosummary
stop_on_errorNo
unity_instanceNo

TDQS

B3.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare the safety profile (destructiveHint=true, readOnlyHint=false, idempotentHint=false), which the description does not contradict. The description adds only the 'local' qualifier for workflow steps and the step-type list; it does not disclose error behavior, execution-mode effects, or what summary/steps/emits responses contain, so added value beyond annotations is minimal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Twelve words in a single sentence, verb-front-loaded, with no filler or redundant content. Every word earns its place by naming either the action or the specific step types.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a complex tool — eight parameters, a required nested operations array with no item schema (additionalProperties only), three enum parameters, a destructive annotation, and no output schema. The description does not explain how to structure an operation item, what the response looks like, or how the response_mode/output_mode options differ, leaving an agent without enough information to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate, and it does clarify the key required parameter: operations can be either calls or local select/assert/poll/foreach/emit steps. But the other seven parameters (execution, max_chars, output_mode, response_mode, stop_on_error, request_id, unity_instance) receive no semantic explanation anywhere, so the compensation is only partial.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action ('Run') and a concrete resource ('calls or local select/assert/poll/foreach/emit workflow steps'), so an agent can tell that this tool executes operations rather than inspecting state. However, it does not explicitly contrast with the sibling call_operation, leaving the batch-vs-single distinction to be inferred from the tool name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to choose batch_operations over call_operation, get_result, or the other siblings. There are no use cases, prerequisites, or exclusions stated; an agent must infer from the name alone that this is for running multiple operations at once.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

call_operationB
Destructive

Execute once; request_id safely deduplicates retries of mutations.

NameRequiredDescriptionDefault
argumentsNo
max_charsNo
operationYes
request_idNo
output_modeNocompact
unity_instanceNo

TDQS

B3.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark destructiveHint=true and idempotentHint=false; the description adds useful behavioral context by stating execution is once-only and that request_id deduplicates retries. It does not disclose failure modes, side effects beyond mutation, or result-handling behavior, but it goes beyond what the annotations already convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single efficient sentence with no filler, and it front-loads the key execution behavior. It is admirably concise for a tool with six parameters, though arguably too terse to fully educate an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the destructive hint, six parameters, no output schema, and zero schema coverage, one line is not enough context. The request_id guidance is valuable, but the agent is left without sufficient details on how to specify the operation, shape arguments, choose output_mode, or interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 six parameters, but it only explains request_id. Critical parameters such as operation, arguments, output_mode, max_chars, and unity_instance are left wholly undocumented, making correct invocation harder than it should be.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies the tool as a single-execution operation invoker ('Execute once') and references mutations, which helps distinguish it from sibling tools like batch_operations. However, it never explains what an 'operation' actually is or what domain it operates on, so some clarity is left to inference from the tool name and parameters.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for single mutations and mentions safe deduplication of retries, but it gives no explicit when-to-use guidance or when-not-to-use alternatives. It does not tell the agent to prefer read-oriented siblings for inspection or batch_operations for multi-step work.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_resultB
Read-onlyIdempotent

Page, select, or search a prior result without repeating Unity work.

NameRequiredDescriptionDefault
pathNo
limitNo
offsetNo
patternNo
max_charsNo
result_idYes
output_modeNostandard

TDQS

B3.1/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful behavioral context by indicating that the tool works over prior results and avoids re-running Unity work, but it does not reveal details about output modes, pagination behavior, or any hidden side effects. This is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no filler, and the key behavioral point about not repeating Unity work is front-loaded. It earns its place, though the brevity contributes to the lack of parameter clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With seven parameters, no parameter descriptions, no output schema, and an already-terse tool description, an agent has insufficient context to call the tool confidently. The description does not explain how paging, search, output modes, or character limits behave, leaving important invocation details to inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 the seven parameters, but it does not. 'Page, select, or search' loosely maps to limit/offset, result_id, and pattern/path, yet parameters such as max_chars, output_mode, and path remain unexplained. The description provides only weak semantic hints and does not carry the parameter-documentation burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies a specific resource ('a prior result') and a set of verbs ('Page, select, or search'), which makes the tool's purpose reasonably clear. It also adds the motivational context of avoiding repeated Unity work, but it does not explicitly differentiate itself from sibling tools like search_capabilities or call_operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'without repeating Unity work' implies this tool should be used when a previous result already exists and should be retrieved rather than recomputed. However, there is no explicit guidance on when to prefer get_result over siblings such as call_operation or search_capabilities, and no exclusions or alternative conditions are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_viewportB
Read-onlyIdempotent

Return one bounded inline viewport image for visual verification.

NameRequiredDescriptionDefault
sourceNoscene_view
targetNo
max_resolutionNo
unity_instanceNo

TDQS

B3.1/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish that the tool is read-only, idempotent, and non-destructive, so the description need not repeat that. It adds a small behavioral detail—the result is a single inline bounded image—but leaves the semantics of 'bounded' and the meaning of source/target selection unexplained. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One short sentence, front-loaded with the action and resource, with no filler. It is concise and readable, though it could afford a second sentence to explain key parameters or use cases without becoming bloated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with four parameters, no output schema, and no parameter descriptions, this description is incomplete. An agent cannot infer what 'target' means, how max_resolution behaves, or how to select scene_view versus game_view, and there is no guidance on how to interpret the returned image.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description names none of the four parameters (source, target, max_resolution, unity_instance). The enum for source and default values in the schema provide some hints, but the description adds no parameter-level meaning and fails to compensate for the zero coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Return') with a clear resource ('one bounded inline viewport image') and states the purpose ('for visual verification'). It is distinguishable from generic sibling names like get_result or inspect_unity, though it does not explicitly contrast with them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'for visual verification' implies this tool is for obtaining an image to verify a scene or game view, but the description does not state when to use it instead of alternatives like inspect_unity or get_result, nor are exclusions given. Usage context is inferred rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

inspect_unityC
Read-onlyIdempotent

Inspect Unity semantically; unchanged revisions return no repeated payload.

NameRequiredDescriptionDefault
scopeNostate
detailNostandard
targetsNo
max_charsNo
since_revisionNo
unity_instanceNo

TDQS

C2.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and idempotentHint, and the description adds the concrete behavior that unchanged revisions produce no repeated payload, which clarifies what idempotency means here. It also lets the agent infer that since_revision is tied to change detection. No statement contradicts the read-only/idempotent annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and the behavioral note does earn its place, but 'semantically' is cryptic and the text is under-specified for a tool with six parameters. It is concise in word count, yet not 'appropriately sized' for the complexity the schema reveals.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With low schema coverage, no output schema, and six parameters, the description leaves important context missing: what a successful inspection returns, which scopes are involved, what targets selects, and how unity_instance affects execution. The single revision-related sentence is useful but far from complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description bears the burden, but it only loosely clarifies since_revision via 'unchanged revisions'. The other five parameters—scope, detail, targets, max_chars, unity_instance—are left entirely to name/enum inference, and targets in particular is ambiguous.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a verb and resource ('Inspect Unity') but 'semantically' is undefined, and nothing in the text distinguishes it from siblings like get_viewport or search_capabilities. The second clause describes revision behavior rather than clarifying what inspection covers. It is not a complete tautology, but the core phrasing largely restates the tool name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given for when to use inspect_unity instead of sibling tools such as get_viewport, call_operation, or search_capabilities. There are no exclusions, prerequisites, or context triggers. An agent must infer usage from the schema alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_capabilitiesC
Read-onlyIdempotent

Find operation names and only the argument hints needed next.

NameRequiredDescriptionDefault
limitNo
queryNo
refreshNo
categoryNo
include_schemaNo

TDQS

C2.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark it read-only, idempotent, and non-destructive; the description adds a scoping claim: results contain operation names and only the argument hints needed next. This is useful but vague, and it does not disclose output shape, refresh behavior, or query semantics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The definition is one short sentence and front-loads the main action, but it is under-specified rather than economically complete; important behavioral and parameter information is missing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With five optional parameters, no output schema, and no parameter descriptions, the description must carry more weight. It gives the broad purpose but omits the query semantics, return shape, and how the result connects to call_operation, so the tool is not sufficiently contextualized.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across five parameters (limit, query, refresh, category, include_schema), yet the description names none of them and only references 'argument hints' as output. This is a serious gap: an agent cannot know what query string, limit, or include_schema controls.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('Find operation names') and the scope of results ('argument hints needed next'), which distinguishes it from sibling tools that call/get/batch operations. However, it does not explicitly differentiate from 'inspect_unity' or clarify what 'capabilities' means in context, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'needed next' implies this is a discovery step before invoking an operation, giving some context. But it offers no explicit guidance on when to choose this over call_operation, get_result, inspect_unity, or batch_operations, and no exclusions.

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.

  1. 6 tool updatesv0.2.0
    • First observedbatch_operations
    • First observedcall_operation
    • First observedget_result
    • First observedget_viewport
    • First observedinspect_unity
    • First observedsearch_capabilities

TDQS

A3.5/5.0

Scored across 6 tools

Disambiguation5/5

Each tool targets a distinct phase of interaction: discovery, execution, result retrieval, batching, semantic inspection, and visual verification. There is no meaningful overlap that would cause an agent to misselect between them.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern: search_, call_, get_, batch_, inspect_, get_. This makes the set predictable and easy to navigate.

Tool Count5/5

Six tools is a well-scoped count for an efficiency-focused server. Each tool earns its place and the set avoids both bloat and thinness.

Completeness4/5

The surface covers the main lifecycle well: discover capabilities, execute operations, retrieve results, batch workflows, inspect Unity state, and verify visually. Minor gaps exist, such as no explicit cancellation or full listing tool, but search_capabilities and batch_operations cover most practical needs.

Maintenance

ActivityStale
ResponsivenessNo issues

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