L-Dopa
L-Dopa
Un servidor MCP para ayudar a los agentes de IA a recuperarse, reenfocarse y hacer que las cosas se hagan.
L-Dopa me arregló, ¿vale??
L-Dopa es un servidor pequeño y orientado a la producción del Model Context Protocol (MCP) que ayuda a un agente a recuperarse cuando un enfoque está fallando, el contexto está disperso o los reintentos se están convirtiendo en un bucle. No ejecuta comandos, no muta sistemas externos ni reemplaza el juicio de un agente. Analiza la evidencia que se le proporciona, conserva un estado de recuperación acotado y propone un siguiente paso más seguro.
El nombre es una broma. El bucle de recuperación no lo es.
Qué hace
L-Dopa v0.1 proporciona seis herramientas MCP que permiten a un agente diagnosticar fallos, reducir el alcance, restaurar el contexto relevante y gestionar los reintentos de forma deliberada.
Herramienta | Úsalo cuando | Devuelve |
| Una operación ha fallado y el agente tiene un error o un extracto de registro. | Causa probable, confianza calibrada, evidencia censurada, siguientes acciones y orientación sobre reintentos. |
| El agente está dando vueltas sin hacer una observación útil. | Un reinicio conciso que se centra en una suposición y en una comprobación mínima y segura. |
| Una tarea es demasiado amplia o enmarañada. | Una lista priorizada de una a cinco acciones concretas; tres es el valor predeterminado. |
| El agente necesita contexto de recuperación relevante de su sesión de L-Dopa. | Un resumen compacto de fallos recientes, intentos, éxitos, hechos y problemas no resueltos. |
| El agente está considerando o informando de un reintento. | Un reintento registrado y acotado, o un bloqueo con una recomendación de estrategia alternativa. |
| El agente está atascado y quiere una secuencia de recuperación concisa. | Diagnóstico, acciones enfocadas, un empujón de recuperación y orientación sobre reintentos. |
Related MCP server: SentinelMCP
Principios de diseño
Principio | Implementación en v0.1 |
Sin certeza mágica | La confianza del diagnóstico es |
Sin bucles ciegos | Los fallos repetidos materialmente similares, las propuestas de reintento sin cambios y los límites de reintento por operación bloquean nuevos reintentos. |
Memoria acotada | El estado respaldado por JSON conserva solo el número configurado de registros por categoría para cada sesión. |
Seguro por defecto | L-Dopa solo ofrece diagnóstico y planificación. Nunca ejecuta comandos de shell ni acciones externas. |
Salida consciente de credenciales | Los patrones comunes de token, cabecera de autorización, contraseña, clave API, JWT, clave AWS y token de GitHub se censuran antes del estado, los registros y la salida de las herramientas. |
Despliegue simple | El servidor usa el transporte stdio estándar de MCP y requiere Node.js 18 o superior. |
Instalación
Clona el repositorio e instala las dependencias:
git clone https://github.com/mshanghai570/L-Dopa.git
cd L-Dopa
npm install
npm run buildInicia el servidor stdio manualmente con:
npm startnpm start parece esperar entrada a propósito. Los servidores MCP se comunican mediante JSON-RPC a través de la entrada y salida estándar, así que normalmente un cliente MCP lo lanza por ti.
Conectar un cliente MCP
Compila L-Dopa primero y luego usa su punto de entrada ejecutable. La siguiente configuración genérica de MCP es compatible con clientes que admiten servidores stdio locales:
{
"mcpServers": {
"l-dopa": {
"command": "node",
"args": ["/absolute/path/to/L-Dopa/dist/index.js"],
"env": {
"LDOPA_STATE_FILE": "/absolute/path/to/l-dopa-state.json",
"LDOPA_MAX_HISTORY": "50",
"LDOPA_RETRY_LIMIT": "3",
"LDOPA_LOG_LEVEL": "info"
}
}
}
}Para un paquete instalado, el comando puede ser en su lugar l-dopa, dependiendo del entorno del cliente. Mantén la salida estándar reservada para los mensajes del protocolo MCP. L-Dopa escribe sus propios registros operativos estructurados y concisos en el error estándar.
Configuración
L-Dopa se ejecuta con valores predeterminados seguros y puede configurarse mediante un archivo JSON y/o variables de entorno. Copia el ejemplo proporcionado para empezar:
cp l-dopa.config.example.json l-dopa.config.json
LDOPA_CONFIG=./l-dopa.config.json npm startLas variables de entorno sobrescriben los valores del archivo.
Configuración | Propiedad JSON | Variable de entorno | Valor predeterminado | Significado |
Ruta de estado |
|
|
| Ubicación del almacén de sesiones JSON acotado. |
Límite de historial |
|
|
| Máximo positivo de registros conservados para cada categoría en una sesión. |
Límite de reintentos |
|
|
| Máximo positivo de reintentos planificados/informados conservados para una operación antes de que se bloqueen nuevos reintentos. |
Nivel de registro |
|
|
| Uno de |
Archivo de configuración | — |
| — | Ruta opcional a un archivo de configuración JSON. |
La configuración no contiene ningún ajuste de credenciales de proveedor porque esta versión no realiza llamadas a modelos ni a proveedores. Si una extensión futura requiere credenciales, pásalas mediante variables de entorno; no las añadas a un repositorio, archivo de configuración o prompt de recuperación.
Referencia de herramientas
Todos los argumentos de texto están acotados y se les censuran las credenciales antes de que L-Dopa los persista o los devuelva. sessionId tiene "default" como valor predeterminado, pero los agentes deberían usar un ID estable por tarea o conversación para evitar que se mezclen historiales de recuperación no relacionados.
diagnose
Usa diagnose después de un fallo con todo el contexto útil que esté disponible. errorMessage, recentOperation, logs, attemptedSolution, expectedResult y actualResult son opcionales, pero un error o un resultado real preciso hace que la respuesta sea más útil.
{
"sessionId": "deploy-2026-08-27",
"recentOperation": "Deploy version 0.1.0",
"errorMessage": "429 Too Many Requests",
"attemptedSolution": "Immediately retried the deployment",
"expectedResult": "Deployment accepted",
"actualResult": "The API rejected the request"
}La respuesta incluye likelyCause, confidence, evidence, recommendedNextActions, retryAppropriate, tryDifferentStrategy y un indicador redacted. La detección es deliberadamente heurística en lugar de falsamente autoritativa.
stimulate
Usa stimulate cuando un agente necesite dejar de narrar y empezar a aprender. Proporciona un task obligatorio y un context opcional de alta señal.
{
"sessionId": "deploy-2026-08-27",
"task": "Repair the deployment",
"context": "The health check timed out twice after a successful build"
}La estrategia de recuperación enfatiza una acción mínima y verificable, y advierte contra los bucles sin cambios.
focus
Usa focus para convertir una tarea amplia en una secuencia deliberadamente corta. maxSteps es opcional y va de uno a cinco; tiene tres como valor predeterminado.
{
"sessionId": "deploy-2026-08-27",
"task": "Repair the deployment and verify availability",
"context": "Health checks time out",
"maxSteps": 3
}reuptake
Usa reuptake cuando el agente necesite contexto relevante de la sesión sin volcar una transcripción. limit tiene cinco como valor predeterminado y está limitado a veinte.
{
"sessionId": "deploy-2026-08-27",
"limit": 5
}Devuelve la tarea actual, el estado de recuperación, los fallos recientes, las soluciones intentadas, los enfoques exitosos, los hechos descubiertos, los problemas no resueltos y el contador de reintentos. Las herramientas de v0.1 todavía no exponen una herramienta dedicada al registro de hechos; discoveredFacts está reservado para extensiones y sigue presente en el esquema compacto.
retry
Usa retry para crear un registro de reintento explícito o informar de su resultado. Un proposedChange debe indicar qué es diferente. L-Dopa permite registros planificados, exitosos y fallidos, pero nunca realiza el reintento por sí mismo.
{
"sessionId": "deploy-2026-08-27",
"operation": "Deploy version 0.1.0",
"previousFailure": "429 Too Many Requests",
"proposedChange": "Wait for Retry-After and submit only one request",
"result": "planned"
}L-Dopa bloquea un reintento si el límite de operación configurado está agotado, el mismo fallo se ha repetido o un reintento existente se repite sin una propuesta modificada. Su respuesta de bloqueo recomienda una vía alternativa acotada, que puede incluir entregar una subtarea bien delimitada y la evidencia recopilada a otro agente capaz.
fix_me
Usa fix_me para la versión corta de diagnose → focus → stimulate → retry guidance. Registra un fallo proporcionado, si lo hay, y luego devuelve un plan; no lo ejecuta.
{
"sessionId": "publish-0.1.0",
"task": "Publish the package safely",
"recentOperation": "npm publish",
"errorMessage": "401 Unauthorized",
"attemptedSolution": "Re-ran the same command"
}Estado y privacidad
El almacén de estado es un archivo JSON plano escrito atómicamente con modo 0600. Tiene una forma versionada y almacena sesiones separadas por clave sessionId. Dentro de cada sesión, los registros de fallos, los registros de reintentos, las soluciones intentadas, los enfoques exitosos y los hechos descubiertos se recortan a maxHistory.
El estado es intencionadamente ligero, no un sistema de memoria a largo plazo. Es local a la máquina del usuario que lo ejecuta y L-Dopa no lo transmite. Revisa o elimina el archivo de estado configurado siempre que necesites borrar el historial de recuperación.
Importante: La censura cubre varios patrones comunes de credenciales, pero es una conveniencia defensiva, no una licencia para enviar secretos. No coloques intencionadamente contraseñas, tokens, claves privadas o cabeceras de autorización completas en la entrada de diagnóstico.
Desarrollo
npm install
npm run build
npm testEl proyecto es deliberadamente modular:
src/
config.ts Configuration loading and validation
index.ts Executable stdio MCP entry point
logger.ts Structured, redacted standard-error logging
redaction.ts Credential-detection and output redaction
recovery.ts Diagnostic, focus, stimulation, and plan logic
server.ts MCP server and tool registrations
state.ts Bounded, atomic JSON session storage
types.ts Shared contracts
tests/
l-dopa.test.ts End-to-end MCP and state behavior testsCobertura de pruebas
El conjunto de pruebas automatizadas conecta un cliente y un servidor MCP reales a través del transporte en memoria del SDK. Cubre la inicialización del servidor, el descubrimiento de herramientas MCP, cada herramienta, la persistencia del estado, la retención acotada, los límites de reintento, los reintentos sin cambios, la detección de fallos repetidos y la censura de credenciales.
Ejecútalo con:
npm testLimitaciones y hoja de ruta
L-Dopa v0.1 usa heurísticas deterministas, por lo que reconoce clases de fallos comunes, pero no es un depurador omnisciente. No tiene almacén vectorial, persistencia remota, integración con proveedores de modelos ni capacidad de ejecución de comandos por diseño. No inspecciona la cadena de pensamiento oculta de un agente; solo trabaja con el contexto operativo proporcionado.
Las incorporaciones futuras deberían preservar estos límites: añadir una herramienta solo cuando aporte un beneficio claro de recuperación, mantener la ejecución de comandos en un componente separado controlado por permisos y mantener el estado acotado e inspeccionable.
Licencia
Available Tools
6 toolsdiagnoseDiagnose a failureA
Analyze an error or failed operation, record it in bounded session state, and recommend safe next actions.
| Name | Required | Description | Default |
|---|---|---|---|
| logs | No | Relevant log excerpt. Credentials are redacted. | |
| sessionId | No | A stable identifier for this agent recovery session. | default |
| actualResult | No | What happened instead. | |
| errorMessage | No | The exact error message, if available. | |
| expectedResult | No | What should have happened. | |
| recentOperation | No | The operation that most recently failed. | |
| attemptedSolution | No | A solution already attempted. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With annotations providing only broad flags, the description adds meaningful behavioral detail: it records state in a bounded session and recommends rather than executes actions. This clarifies side effects and scope without contradicting the annotations.
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, tightly worded sentence front-loads the core verb and object, then packs in two meaningful behavioral outcomes. There is no filler or redundancy.
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 tool with seven optional but fully documented parameters and no output schema, the description conveys the essential flow: analyze, record, and recommend. It stops short of describing the exact shape of the recommendation output, but it is sufficiently complete for an agent to understand the tool's role.
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 100%, and the input schema already documents each parameter, so the tool description does not need to repeat parameter details. The description adds no parameter-specific meaning beyond the schema, so the baseline of 3 is appropriate.
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 names a specific verb ('Analyze') and resource ('an error or failed operation'), and adds two concrete outcomes: recording in bounded session state and recommending safe next actions. It is clear about what the tool does, though it does not explicitly differentiate it from sibling tools like retry or fix_me.
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 phrase 'Analyze an error or failed operation' implies the intended trigger, and 'recommend safe next actions' positions it as an advisory step before acting. However, it gives no explicit guidance on when not to use it or how it compares to sibling alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fix_meCreate a complete recovery sequenceA
Combine diagnosis, focus, stimulation, and retry guidance into a concise recovery plan without executing commands.
| Name | Required | Description | Default |
|---|---|---|---|
| logs | No | Relevant log excerpt. Credentials are redacted. | |
| task | Yes | The task that is stuck or needs recovery. | |
| sessionId | No | A stable identifier for this agent recovery session. | default |
| actualResult | No | Actual result. | |
| errorMessage | No | The exact error message, if available. | |
| expectedResult | No | Expected result. | |
| recentOperation | No | The operation that failed. | |
| attemptedSolution | No | A solution already tried. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already tell the agent the operation is not read-only, not idempotent, and not destructive. The description adds valuable behavioral context beyond those annotations by explicitly stating that no commands are executed, meaning the tool only produces guidance/planning content. This helps the agent set expectations about side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. It states the action, the inputs, the output, and a key behavioral constraint ('without executing commands') in under 15 words. Every phrase earns its place.
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 8-parameter schema with full coverage, annotations, and no output schema, the description provides enough context: it defines the output as a concise recovery plan and clarifies the non-execution behavior. It does not detail the plan's format or how to handle partially supplied inputs, but the required task parameter and well-described optional parameters make the tool usable.
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 100%, so all eight parameters already have descriptions. The tool description adds conceptual meaning by mapping the plan components to diagnosis, focus, stimulation, and retry guidance, but it does not explain how specific parameters map to those components. Baseline 3 is appropriate because the schema carries the parameter-documentation burden.
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 ('Combine') and resource ('diagnosis, focus, stimulation, and retry guidance into a concise recovery plan'), so an agent knows what the tool produces. It also distinguishes itself from the sibling tools diagnose, focus, stimulate, and retry by framing those as components rather than the tool's own action. The phrase 'without executing commands' further differentiates it from the more action-oriented siblings.
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 clearly conveys that this tool is for synthesizing a recovery plan rather than executing recovery actions, which gives the agent a clear decision context. It does not explicitly enumerate exclusions or say 'use X when...', but naming the component tools implies when this orchestrator is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
focusFocus the next actionsA
Reduce a complicated task to a short, prioritized sequence of concrete, verifiable next actions.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | The task to reduce into next actions. | |
| context | No | Relevant blocker, constraint, or diagnostic finding. | |
| maxSteps | No | Number of actions to return; defaults to three. | |
| sessionId | No | A stable identifier for this agent recovery session. | default |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no positive behavioral hints (all false), so the description itself must convey behavior. It does state the transformation and output nature, but it does not disclose aspects like whether actions are executed, how sessions affect behavior, or any side effects. This is adequate but not richly transparent.
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 wasted words. It efficiently conveys the core purpose and expected output without redundancy.
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?
With no output schema, the description does indicate the return shape ('sequence of concrete, verifiable next actions'), which helps. However, it lacks guidance on how the tool relates to the provided sessionId/context and does not address when it should be preferred over sibling tools, leaving some practical gaps.
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 100%, so the input schema fully documents all four parameters. The description adds no parameter-specific meaning beyond the schema, hitting the baseline expected when the schema carries the load.
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 names a specific verb ('reduce') and resource ('a complicated task') and states the output: 'a short, prioritized sequence of concrete, verifiable next actions.' This clearly communicates the tool's purpose, though it does not explicitly contrast it with siblings like diagnose or retry.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used when a task is complicated and needs to be broken into manageable next actions. However, it gives no explicit when-to-use vs. alternative guidance and does not mention any exclusions or sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
retryPlan or record an intelligent retryA
Record a bounded retry with its specific change and result. Blocks unchanged or exhausted retry loops.
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | Short result or verification note. | |
| result | No | Whether the retry is planned, succeeded, or failed. | planned |
| operation | Yes | The operation to retry. | |
| sessionId | No | A stable identifier for this agent recovery session. | default |
| proposedChange | No | What will be changed before retrying. | |
| previousFailure | No | The previous failure for this operation. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only say the tool is not read-only, not idempotent, and not destructive. The description adds a real behavioral trait beyond those annotations: retries must be bounded, and unchanged or exhausted retry loops are blocked. This gives the agent useful expectations about side effects and constraints, with no contradiction against the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler: the first states the core action and required components, the second states the key constraint. It is front-loaded and every phrase earns its place. The description is appropriately sized for what it conveys.
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 schema covers all parameters well and the description gives the core retry constraint, so the tool is basically usable. However, the title mentions 'Plan or record' while the description only explains recording, and there is no output schema or description of what happens when a retry is blocked or succeeds. The exact meaning of 'bounded' and the return behavior remain under-specified.
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 100%, and each parameter already has a meaningful description, so the baseline is 3. The description only echoes 'change and result' which maps to proposedChange/result, and it does not add operational detail about sessionId, previousFailure, or notes. It does not need to compensate for missing schema docs.
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 action and resource: 'Record a bounded retry with its specific change and result.' It also adds the distinguishing guardrail—'Blocks unchanged or exhausted retry loops'—which separates it from sibling tools like diagnose or fix_me. The title's 'Plan or record' is somewhat broader, but the description still gives a clear, non-tautological purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this is for retrying an operation after a change and warns against unchanged or exhausted retry loops, so there is some embedded when-not guidance. However, it never explicitly states the triggering conditions for using this tool over a sibling, nor does it name alternatives. The usage context remains mostly inferred rather than directly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
reuptakeRecover relevant session contextBRead-onlyIdempotent
Retrieve a compact, relevant summary of failures, attempts, facts, successes, and unresolved issues from this session.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum recent entries per context category. | |
| sessionId | No | A stable identifier for this agent recovery session. | default |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds useful output composition details (failures, attempts, facts, successes, unresolved issues) but does not disclose additional behavioral traits such as staleness, scoping caveats, or return formatting.
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?
One front-loaded sentence states the action, the resource, and the scope with no wasted words. It is appropriately sized for the tool's simplicity.
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 read-only, side-effect-free retrieval tool with fully documented parameters, the description gives an agent enough to call it and understand the returned content. It could briefly mention when to use it relative to siblings, but the essential invocation context is present.
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 fully documents limit and sessionId with defaults, bounds, and descriptions, so schema coverage is 100%. The description adds no parameter-specific meaning or usage examples, keeping this at the baseline.
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 ('Retrieve') and clearly identifies the resource: a compact session summary containing failures, attempts, facts, successes, and unresolved issues. It is unambiguous about what the tool does, though it does not explicitly contrast with sibling tools like diagnose or focus.
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?
There is no guidance on when to use reuptake versus its siblings such as diagnose, focus, or fix_me. The intended use case is only implied by the title and description, not explicitly stated as a condition or recommendation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
stimulateNudge a stuck agentA
Return a concise, structured recovery nudge that emphasizes reassessment, minimal checks, and changed approaches.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | The task on which the agent is stuck. | |
| context | No | The most relevant constraint, failed assumption, or observation. | |
| sessionId | No | A stable identifier for this agent recovery session. | default |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is transparent about the primary behavior: it returns a nudge rather than performing the task, and it specifies the nudge's 'concise, structured' form and themes. With no output schema and annotations that provide no positive safety signal, this is meaningful disclosure, though side effects and session-state behavior are not addressed.
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 sentence that front-loads the action ('Return'), the deliverable ('recovery nudge'), and the key content directives. Every phrase earns its place, with no filler or duplicated schema information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The definition is adequate for a simple text-generation tool with fully documented parameters and a clear title. However, it lacks explicit sibling differentiation, usage timing, and an output-format preview, so an agent choosing among the six siblings gets only partial 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?
Schema description coverage is 100%, with clear descriptions for task, context, and sessionId, so the baseline of 3 applies. The description's mention of reassessment and changed approaches hints at how context might be used but adds no concrete parameter-level meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific deliverable—a 'concise, structured recovery nudge'—and defines its content emphasis (reassessment, minimal checks, changed approaches), so an agent knows what the tool produces. It does not explicitly distinguish this from sibling tools like diagnose or retry, 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied rather than stated: the title and the task parameter describe an agent that is stuck, and context mentions failed assumptions. However, the description never says when to prefer stimulate over diagnose, reuptake, focus, retry, or fix_me, nor does it offer exclusion conditions.
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.
6 tool updates
v0.1.0- First observed
diagnose - First observed
fix_me - First observed
focus - First observed
retry - First observed
reuptake - First observed
stimulate
TDQS
Scored across 6 tools
Several tools have unclear boundaries: diagnose, focus, and fix_me all produce recommended next actions or plans, while stimulate and focus both generate recovery-oriented output. Only reuptake and retry are clearly distinct, making tool selection genuinely ambiguous.
The tools mostly follow a consistent lowercase imperative-verb style: diagnose, stimulate, focus, retry. The main deviation is fix_me, which introduces an underscore and an object pronoun, but the overall naming pattern is still recognizable.
Six tools is a reasonable size for a session-recovery-focused server. However, fix_me is an aggregate of other tools and adds some redundancy, so the set is slightly less lean than it could be.
The tool surface covers the core recovery loop well: diagnose failures, focus on next actions, retry with bounded state, and retrieve session context. Minor gaps exist around explicit session reset or state-clearing operations, but agents can generally work around them.
Maintenance
Related MCP Connectors
Context and control for any AI. Agent identity, scoped access, and proof across tools.
Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.
Persistent memory and drift detection for AI agents across session restarts.
Memory, reflection and safety for AI agents. Hosted or local, no vector database.
Related MCP Servers
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to retain memory of past interactions and detect behavioral drift, preventing repeated mistakes without LLM token extraction.31 npm58 PyPI1,254MIT
- FlicenseAqualityDmaintenanceEnables LLM-driven agents to autonomously detect, diagnose, repair, verify, and prevent software and hardware failures on local and remote systems. Includes built-in safety checks and automatic rollbacks.15-
- AlicenseBqualityCmaintenanceEnables programming agents to capture errors and conversation signals, reflect on root causes, consolidate reusable skills, and retrieve relevant context for future tasks, providing a self-learning memory loop.15MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to automatically capture durable knowledge and retrieve only relevant, token-bounded context from a secure local-first long-term memory, with support for progressive disclosure, snapshots, health diagnostics, and background tasks.Apache 2.0