Orchestration MCP
Orchestration MCP
Servidor MCP en TypeScript para lanzar y rastrear ejecuciones de agentes de codificación externos.
La superficie de MCP se mantiene estable mientras que el backend de ejecución interno puede dirigirse a:
codexlocalclaude_codelocalremote_a2aremoto
Esto permite que un agente de nivel superior llame a un conjunto de herramientas MCP mientras la capa de orquestación decide si los subagentes son procesos SDK locales o agentes compatibles con A2A remotos.
Instalación y compilación
cd orchestration-mcp
npm install
npm run buildRelated MCP server: mcpcodeserver
Ejecutar el servidor MCP
cd orchestration-mcp
npm startEsto inicia el servidor MCP desde dist/index.js.
Ejemplo de configuración de Codex MCP
Si desea que Codex cargue este servidor MCP, añada una entrada como esta a ~/.codex/config.toml:
[mcp_servers.orchestration-mcp]
command = "node"
args = ["/abs/path/to/orchestration-mcp/dist/index.js"]
enabled = trueEjemplo usando la ruta de este repositorio:
[mcp_servers.orchestration-mcp]
command = "node"
args = ["/Users/fonsh/PycharmProjects/Treer/nanobot/orchestration-mcp/dist/index.js"]
enabled = trueDespués de actualizar la configuración, reinicie Codex para que recargue los servidores MCP.
Lo que expone el MCP
El servidor registra estas herramientas:
spawn_runget_runpoll_eventscancel_runcontinue_runlist_runsget_event_artifact
Flujo típico de MCP
Llame a
spawn_runpara crear una ejecución de subagente.Llame a
poll_eventshasta que vea un evento terminal o un estado de espera.Si la ejecución entra en
input_requiredoauth_required, llame acontinue_run.Llame a
get_runpara obtener el resumen de la ejecución más reciente.Si un evento contiene
artifact_refs, llame aget_event_artifactpara obtener la carga útil completa.
Notas sobre spawn_run
backend:"codex","claude_code"o"remote_a2a"role: etiqueta de rol de orquestación comoplanner,workeroreviewerprompt: instrucción en texto plano para ejecuciones simplesinput_message: mensaje estructurado opcional para entradas de estilo multipart/A2Acwd: directorio de trabajo absolutosession_mode:neworesumesession_id: requerido al reanudar una sesión anteriorprofile: ruta opcional a un archivo de persona/descripción de puesto. Cuando se proporciona, la orquestación carga el archivo y lo inyecta en el contexto del agente. Los backends con soporte nativo de prompt del sistema lo usan allí; otros backends lo anteponen al contexto de ejecución.
A menos que se le indique explícitamente que use un perfil, deje profile vacío.
output_schema: JSON Schema opcional para una salida final estructuradametadata: metadatos de orquestación opcionales almacenados para correlación y auditoríabackend_config: ajustes opcionales específicos del backend. Pararemote_a2a, establezcaagent_urly cualquier encabezado/token de autenticación aquí.
Para todos los backends, cwd es el directorio de trabajo del lado de la orquestación utilizado para el almacenamiento de ejecuciones/sesiones.
Para remote_a2a, spawn_run.cwd también se reenvía al subagente remoto y se convierte en el directorio de ejecución de ese contexto de tarea A2A.
Se requiere al menos uno de prompt o input_message.
Ejemplo simple:
{
"backend": "codex",
"role": "worker",
"prompt": "Inspect the repository and summarize the architecture.",
"cwd": "/abs/path/to/project",
"session_mode": "new"
}Ejemplo de A2A remoto:
{
"backend": "remote_a2a",
"role": "worker",
"prompt": "Inspect the repository and summarize the architecture.",
"cwd": "/abs/path/to/project",
"session_mode": "new",
"backend_config": {
"agent_url": "http://127.0.0.1:53552"
}
}Recursos del flujo de trabajo del revisor
Este repositorio incluye una configuración de revisor lista para usar para flujos de trabajo de codificación multi-agente:
perfil:
./profile/reviewer-remediator.md
Uso recomendado de spawn_run para una ejecución de revisor:
{
"backend": "codex",
"role": "reviewer",
"cwd": "/abs/path/to/project",
"session_mode": "new",
"profile": "/abs/path/to/orchestration-mcp/profile/reviewer-remediator.md",
"prompt": "Review only the latest diff in the current working directory, apply low-risk fixes when clearly correct, validate them, and write a remediation report."
}Notas sobre continue_run
Use continue_run cuando una ejecución entre en input_required o auth_required y el backend admita la continuación interactiva.
Entradas:
run_idinput_message
Notas sobre get_event_artifact
Use get_event_artifact cuando un evento saneado devuelto por poll_events contenga event.data.artifact_refs y necesite la carga útil original completa.
Entradas:
run_idseqfield_path: puntero JSON relativo aevent.data, por ejemplo/stdout,/raw_tool_use_resulto/input/contentoffset: desplazamiento de bytes opcional, por defecto0limit: límite de bytes opcional, por defecto65536
Flujo típico:
Llame a
poll_events.Inspeccione
event.data.artifact_refsen cualquier evento saneado.Llame a
get_event_artifactcon el mismorun_id, laseqdel evento y uno de los valores defield_pathexpuestos.
Valores predeterminados del backend
codex: utiliza los valores predeterminados actuales de@openai/codex-sdkmás los ajustes de ejecución no interactiva ya integrados en el adaptadorclaude_code: utiliza@anthropic-ai/claude-agent-sdkconpermissionMode: "bypassPermissions"para que la llamada MCP no sea bloqueante, y reutiliza los IDs de sesión de backend persistidos pararesumeremote_a2a: se conecta a un agente remoto compatible con A2A usando@a2a-js/sdk, transmite actualizaciones de tareas a eventos de orquestación normalizados y admitecontinue_runparainput_required
Para claude_code, asegúrese de que el entorno local ya tenga una configuración de autenticación de Claude Code funcional antes de realizar pruebas.
Probar agentes A2A
El repositorio incluye módulos auxiliares para agentes de prueba envueltos en A2A locales:
dist/test-agents/codex-a2a-agent.jsdist/test-agents/claude-a2a-agent.jsdist/test-agents/start-a2a-agent.js
Estos exportan ayudantes de inicio que envuelven los SDK locales de Codex y Claude detrás de un servidor A2A para que el MCP de orquestación pueda probar su backend interno remote_a2a contra subagentes realistas.
Para iniciar un lanzador de envoltorio interactivo:
npm run start:a2a-agentEl script preguntará si desea envolver codex o claude_code.
Después del inicio, imprime la agent_url y una carga útil de spawn_run lista para usar para la capa MCP. El envoltorio ya no bloquea un directorio de trabajo al inicio. Cada llamada a remote_a2a utiliza el cwd proporcionado a spawn_run, y el envoltorio mantiene ese cwd fijo durante la vida útil del mismo contextId de A2A.
Almacenamiento
Los datos de ejecución se almacenan en:
<cwd>/.nanobot-orchestrator/
runs/
<run_id>/
run.json
events.jsonl
result.json
artifacts/
000008-command_finished/
manifest.json
stdout.0001.txt
stdout.0002.txt
sessions/
<session_id>.jsonNotas:
events.jsonlalmacena eventos saneados destinados al consumo depoll_events.Las cargas útiles sin procesar de gran tamaño se mueven a archivos de artefactos por evento y se hace referencia a ellas desde
event.data.artifact_refs.run.jsonyresult.jsonmantienen la instantánea de ejecución actual y el comportamiento del resultado final.El nombre del directorio de almacenamiento es actualmente
.nanobot-orchestrator/para compatibilidad con versiones anteriores con la implementación existente.
Available Tools
7 toolscancel_runB
Cancel a running external coding-agent run.
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| run_id | Yes | |
| status | Yes | |
| cancelled_at | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('cancel') but doesn't explain what cancellation entails (e.g., whether it's reversible, if it stops processes immediately, what permissions are required, or any side effects like resource cleanup). For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
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, clear sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the key action and target, making it efficient and easy to parse. Every word earns its place, with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a mutation with one parameter) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and low schema coverage, it lacks details on behavioral aspects like side effects or error conditions. It meets the basic requirement but leaves gaps in understanding the tool's full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter ('run_id') with 0% description coverage, meaning the schema provides no semantic details. The description doesn't add any information about this parameter (e.g., what a 'run_id' is, how to obtain it, or format examples). However, with only one parameter, the baseline is higher; the description implies the parameter identifies the run to cancel but doesn't compensate for the lack of schema details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('cancel') and the target ('a running external coding-agent run'), which is specific and unambiguous. It distinguishes from siblings like 'continue_run' or 'get_run' by focusing on termination rather than continuation or retrieval. However, it doesn't explicitly differentiate from all siblings (e.g., 'spawn_run' is about creation, but this isn't stated).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., that the run must be active), exclusions (e.g., not for completed runs), or comparisons to siblings like 'continue_run' for ongoing runs or 'list_runs' for status checks. This leaves the agent to infer usage context from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
continue_runB
Send an additional input message to a run that is waiting for more input.
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes | ||
| input_message | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| run_id | Yes | |
| status | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it correctly indicates a write operation ('send'), it lacks critical details: required permissions, whether this changes run state, rate limits, error conditions, or what happens after sending input. For a mutation tool with complex nested parameters, this leaves significant behavioral gaps.
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, efficient sentence that front-loads the core purpose without unnecessary words. Every word earns its place, making it maximally concise while still conveying the essential action and 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?
Given the tool's moderate complexity (2 parameters with nested objects), no annotations, but presence of an output schema, the description is minimally adequate. The output schema reduces need to describe return values, but the description should provide more context about run states, prerequisites, and parameter meanings to be truly complete for this interactive operation.
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 but provides no parameter information. It doesn't explain what 'run_id' refers to, the structure of 'input_message', valid roles, or part types. With 2 parameters including complex nested objects, the description fails to add any semantic value beyond what the bare schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('send an additional input message') and target ('to a run that is waiting for more input'), providing specific verb+resource. However, it doesn't explicitly differentiate from sibling tools like 'spawn_run' or 'cancel_run', which would require mentioning it's for existing runs in a waiting state rather than creating new runs or terminating them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'to a run that is waiting for more input', suggesting this tool should be used when a run is in a specific state. However, it doesn't provide explicit when-not-to-use guidance or name alternatives like 'spawn_run' for new runs or 'cancel_run' for termination, leaving some ambiguity about sibling tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_event_artifactC
Read a sanitized event artifact by run_id, seq, and JSON Pointer field_path.
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes | ||
| seq | Yes | ||
| field_path | Yes | ||
| offset | No | ||
| limit | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| seq | Yes | |
| mime | Yes | |
| offset | Yes | |
| run_id | Yes | |
| content | Yes | |
| relpath | Yes | |
| encoding | Yes | |
| has_more | Yes | |
| field_path | Yes | |
| total_bytes | Yes | |
| returned_bytes | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states it's a read operation. It doesn't disclose behavioral traits like authentication requirements, rate limits, error conditions, what 'sanitized' means, or how the JSON Pointer field_path works. The mention of 'sanitized' hints at data transformation but lacks details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise single sentence with zero wasted words. Front-loaded with the core purpose, and every element (verb, resource, parameters) earns its place. No structural issues despite the brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters with 0% schema coverage and no annotations, the description is incomplete—it doesn't explain parameter semantics or behavioral context. However, the existence of an output schema reduces the need to describe return values. For a read operation, this is minimally adequate but leaves important 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 0%, so the description must compensate but only lists parameter names without explaining their meaning. It mentions 'run_id, seq, and JSON Pointer field_path' but doesn't clarify what these identifiers represent, what 'offset' and 'limit' do, or how field_path syntax works. This leaves significant gaps in parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Read') and resource ('sanitized event artifact') with specific identifiers (run_id, seq, field_path). It distinguishes from siblings like get_run or list_runs by focusing on artifacts rather than runs themselves. However, it doesn't explicitly contrast with poll_events which might also retrieve event data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like poll_events or get_run. The description mentions parameters but doesn't provide context about appropriate use cases, prerequisites, or when other tools might be more suitable. This leaves the agent without clear decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_runB
Get the current summary status for a known run.
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| cwd | Yes | |
| role | Yes | |
| run_id | Yes | |
| status | Yes | |
| backend | Yes | |
| summary | Yes | |
| last_seq | Yes | |
| metadata | Yes | |
| remote_ref | Yes | |
| session_id | Yes | |
| started_at | Yes | |
| updated_at | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves a 'summary status,' implying a read-only operation, but doesn't specify whether it's safe, if it requires authentication, rate limits, or what happens if the run_id is invalid. For a tool with no annotations, this is a significant gap in transparency.
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, clear sentence with zero waste: 'Get the current summary status for a known run.' It is front-loaded and efficiently conveys the core purpose without unnecessary elaboration, making it highly concise and well-structured.
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 low complexity (1 parameter, no nested objects) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and incomplete parameter semantics, it lacks details on behavioral traits and usage context, making it only partially complete for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter (run_id) with 0% description coverage, meaning the schema provides no semantic details. The description adds minimal context by implying 'run_id' refers to 'a known run,' but doesn't explain format, examples, or constraints. This partially compensates for the schema gap, but not fully, aligning with the baseline for moderate coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the current summary status for a known run.' It specifies the verb ('Get'), resource ('summary status'), and scope ('for a known run'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'list_runs' or 'poll_events', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'a known run' but doesn't clarify prerequisites (e.g., that the run must already exist from a previous operation) or contrast it with siblings like 'list_runs' (for listing runs) or 'poll_events' (for monitoring events). This leaves the agent without explicit usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_runsC
List runs known to the current orchestration MCP process.
| Name | Required | Description | Default |
|---|---|---|---|
| status | No | ||
| backend | No | ||
| cwd | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| runs | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but discloses minimal behavioral traits. It indicates this is a listing operation but doesn't describe pagination, sorting, default behavior, rate limits, or authentication needs. For a tool with 3 parameters and no annotation coverage, this is inadequate.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality.
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 (which handles return values), the description's minimalism is partially acceptable. However, with 3 parameters, no annotations, and 0% schema coverage, the description should provide more context about filtering behavior and usage scenarios to be truly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but adds no parameter information. It doesn't explain what 'status', 'backend', or 'cwd' mean, their relationships, or how they filter results. With 3 parameters (2 with enums) undocumented, this is a significant gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('runs'), and specifies the scope ('known to the current orchestration MCP process'). However, it doesn't explicitly differentiate from sibling tools like 'get_run' or 'poll_events', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_run' (for single run details) or 'poll_events' (for event monitoring). It mentions the scope but offers no explicit when/when-not instructions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
poll_eventsB
Long-poll incremental events for a run after a known sequence number.
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes | ||
| after_seq | Yes | ||
| limit | No | ||
| wait_ms | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| events | Yes | |
| run_id | Yes | |
| status | Yes | |
| next_after_seq | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It mentions 'Long-poll' and 'incremental events,' hinting at real-time behavior and potential waiting, but lacks details on rate limits, authentication needs, error handling, or what 'events' entail. This leaves significant behavioral gaps for an agent.
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, efficient sentence that front-loads key information ('Long-poll incremental events') without unnecessary words. Every part earns its place by specifying the action, resource, and key parameter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters with 0% schema coverage, no annotations, but an output schema exists, the description is moderately complete. It covers the core purpose and one parameter but misses details on other parameters, behavioral traits, and usage context. The output schema helps, but more guidance is needed for effective tool use.
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 'after_seq' as 'after a known sequence number,' adding context beyond the schema's numeric constraints. However, it doesn't cover other parameters like run_id, limit, or wait_ms, leaving them undocumented. Baseline is 3 due to partial compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Long-poll incremental events') and resource ('for a run'), specifying it retrieves events after a known sequence number. It distinguishes from siblings like get_event_artifact or get_run by focusing on incremental polling rather than direct fetching, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for monitoring incremental events in a run after a specific sequence, suggesting it's for real-time updates. However, it doesn't explicitly state when to use this vs. alternatives like get_event_artifact or list_runs, nor does it mention prerequisites like needing an active run.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
spawn_runB
Start a new external coding-agent run and return immediately.
| Name | Required | Description | Default |
|---|---|---|---|
| backend | Yes | Backend to execute the run. Supported values are "codex", "claude_code", and "remote_a2a". | |
| role | Yes | Supervisor role for this run: planner, worker, or reviewer. | |
| prompt | No | Primary instruction for the coding agent run. | |
| input_message | No | Structured input message for multi-part or A2A-compatible runs. | |
| cwd | Yes | Absolute working directory where the agent should run and where artifacts are stored. | |
| session_mode | Yes | Use "new" to create a fresh session or "resume" to continue an existing one. | |
| session_id | No | ||
| profile | No | Optional path to a profile/persona/job-description file. Leave blank unless explicitly instructed to use a profile. | |
| output_schema | No | Optional JSON Schema for structured final output from the run. | |
| metadata | No | Optional orchestration metadata for task/step correlation. It is stored but not interpreted by the MCP server. | |
| backend_config | No | Optional backend-specific configuration, such as remote_a2a agent_url and auth headers. |
Output Schema
| Name | Required | Description |
|---|---|---|
| role | Yes | |
| run_id | Yes | |
| status | Yes | |
| backend | Yes | |
| session_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. 'Start a new external coding-agent run' implies a write/mutation operation, but the description doesn't disclose permission requirements, rate limits, whether this consumes resources, what happens to previous runs, or how to monitor the started run. 'Return immediately' suggests asynchronous execution but lacks details about how to track completion or retrieve results.
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 extremely concise - a single sentence that communicates the core action and key behavioral trait ('return immediately'). There's zero wasted language, and it's front-loaded with the essential information. Every word earns its place in this minimal description.
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 complex tool with 11 parameters, nested objects, and no annotations, the description is minimal. While an output schema exists (reducing need to describe return values), the description doesn't address the tool's role in the broader workflow with sibling tools, doesn't explain the asynchronous nature hinted by 'return immediately', and provides no context about error conditions or typical usage patterns. It's adequate but leaves significant 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?
With 91% schema description coverage, the schema does most of the parameter documentation work. The description adds no parameter-specific information beyond what's in the schema. It doesn't explain relationships between parameters (like how 'session_mode' interacts with 'session_id'), typical values, or usage patterns. The baseline of 3 is appropriate given the high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Start a new external coding-agent run') and resource ('coding-agent run'), with the specific behavioral detail 'and return immediately' distinguishing it from potentially blocking operations. However, it doesn't explicitly differentiate from sibling tools like 'continue_run' or 'get_run' beyond the 'start new' aspect.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance - only that it starts a new run and returns immediately. There's no explicit guidance on when to use this versus alternatives like 'continue_run' for resuming sessions, 'cancel_run' for stopping runs, or 'poll_events' for monitoring. No prerequisites, constraints, or typical use cases are mentioned.
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.
7 tool updates
v0.1.0- First observed
cancel_run - First observed
continue_run - First observed
get_event_artifact - First observed
get_run - First observed
list_runs - First observed
poll_events - First observed
spawn_run
TDQS
Scored across 7 tools
Each tool has a clearly distinct purpose with no overlap: cancel_run stops a run, continue_run sends input, get_event_artifact reads artifacts, get_run retrieves status, list_runs enumerates runs, poll_events monitors events, and spawn_run initiates runs. The descriptions make it easy to distinguish between status retrieval, event handling, run management, and input/output operations.
All tool names follow a consistent verb_noun pattern with snake_case, such as cancel_run, get_run, list_runs, and spawn_run. This uniformity makes the set predictable and easy to navigate, with no deviations in style or structure across the seven tools.
With 7 tools, the count is well-scoped for an orchestration server focused on managing external coding-agent runs. Each tool serves a specific function in the lifecycle of runs, from creation to monitoring and termination, without being overly sparse or bloated, fitting typical orchestration needs.
The tool set provides complete coverage for the orchestration domain: it supports the full lifecycle of runs with spawn_run (create), get_run and list_runs (read), continue_run (update input), and cancel_run (delete/terminate), plus event handling with poll_events and get_event_artifact. There are no obvious gaps, enabling agents to manage runs effectively from start to finish.
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