Orchestrate Codex
This server is a provider-neutral orchestration plugin that coordinates multiple LLM providers (leaves) for complex document creation and transformation tasks under a supervised, host-model-driven architecture.
Here's what you can do:
List available recipes (
orchestrate_list_recipes): Retrieve all built-in supervised orchestration recipes across document classes (durable,change,transform,direct).Explain a recipe (
orchestrate_explain_recipe): Get detailed information about a specific recipe, including its stages, document class, context policy, and default leaf bindings.Query context policies (
orchestrate_context_policy): Retrieve how context (e.g., Git history, session diary) is handled for a given document class.Build a static execution plan (
orchestrate_plan_recipe): Generate a step-by-step plan with suggested tools for a recipe without executing it.Start a supervised run (
orchestrate_start_run): Kick off a supervised orchestration run, automatically executing local gather stages (e.g.,gather_facts,gather_git) and returning the next required action.Advance a run (
orchestrate_continue_recipe): Progress the orchestration state after a leaf tool returns a result, with built-in fallback chain support (e.g., Claude → Grok → Antigravity) on failure.Retrieve run state (
orchestrate_get_run): Fetch the current state of any active or completed run by itsrun_id.View fallback chains (
orchestrate_fallback_chains): Inspect the default capability-to-fallback-leaf-tool chains to understand how failures are handled.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Orchestrate Codexplan a recipe to draft a release note"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Orchestrate Codex
프로바이더 중립 오케스트레이션 MCP 플러그인입니다. (버전 0.5.4)
개요
다양한 LLM 프로바이더(leaf)를 조율해 복잡한 문서 작성과 변환을 처리합니다. 오케스트레이터는 직접 판단을 내리지 않는 감독형(Supervised) 구조입니다. 대신 호스트 모델에게 작업 메뉴, 최신 모델 ID, 가드레일만 공급합니다. 실제 작업을 어떻게 나누고 실행할지는 전적으로 호스트 모델이 결정합니다.
Related MCP server: Codex Delegate MCP
동작 방식
MCP 서버는 형제 MCP를 직접 호출할 수 없습니다. 따라서 호스트가 지휘자 역할을 맡아 아래 흐름으로 작업을 진행합니다.
advise: 호스트에게 라우팅 정보 제공호스트의 배분 판단
step: 단일 leaf 호출 준비verify: 결과물 검증
설치 및 연결
Python 3.9 이상에서 런타임 의존성 없이 동작합니다.
pip install -e .
pip install -e '.[dev]'
codex plugin marketplace add "/path/to/Orchestrate Codex"MCP 서버 등록을 위한 .mcp.json 설정입니다.
{
"mcpServers": {
"orchestrate-codex": {
"command": "python3",
"args": ["./scripts/orchestrate_codex_mcp.py"],
"cwd": ".",
"env": {
"ORCHESTRATE_CODEX_RUNNING_UNDER_CODEX_MCP": "1"
}
}
}
}MCP 도구 목록
도구 이름 | 설명 |
orchestrate_list_recipes | 내장된 감독형 오케스트레이션 레시피 목록을 반환합니다. |
orchestrate_explain_recipe | 레시피의 단계, 문서 클래스, 컨텍스트 정책, 기본 leaf 바인딩을 설명합니다. |
orchestrate_context_policy | 문서 클래스(durable, change, transform, direct)별 컨텍스트 정책을 반환합니다. |
orchestrate_plan_recipe | 단계와 제안 도구를 포함한 정적 계획을 생성합니다. |
orchestrate_start_run | 감독형 실행을 시작하고 로컬 수집 단계를 자동 실행합니다. |
orchestrate_continue_recipe | leaf 도구 실행 이후의 상태를 진행합니다. |
orchestrate_get_run | run_id로 실행 상태를 조회합니다. |
orchestrate_fallback_chains | 기본 기능에서 폴백 leaf 도구로 이어지는 체인을 보여줍니다. |
orchestrate_advise | 호스트 모델에 라우팅 브리프와 최신 확인된 모델 ID를 제공합니다. |
orchestrate_step | 계획된 단일 위임 leaf 호출을 준비하고 모델을 해석합니다. |
orchestrate_verify | 생성된 텍스트의 환각 도구 사용, 세션 일기 톤, 가드레일을 검증합니다. |
orchestrate_probe_models | 각 leaf별 최신 작동 모델 ID를 실시간으로 확인합니다. |
orchestrate_run | 자율 브로커 모드로 레시피를 처음부터 끝까지 실행합니다. |
orchestrate_check_leaves | 설정된 각 leaf를 스폰해 도구 목록을 확인하는 사전 점검을 수행합니다. |
orchestrate_resolve_bindings | 현재 연결된 leaf 도구 이름을 기반으로 바인딩을 해석합니다. |
레시피 및 실행 상태
recipe는 MCP prompts로 노출합니다. run 상태는 orchestrate://run/<id> 형태의 resources로 노출해 추적할 수 있습니다.
레시피 도메인
durable:
durable_readme,technical_doc,proposal,deep_readmechange:
change_pr,release_notes,review_diff,release_drafttransform:
translate_doc,polish_text,rewrite_text,summarize_text,research_brief,research_then_writedirect:
announcement,blog_post,email_draft,product_copy,generate_image,compare_models,direct_chat
durable과 change 도메인의 초안 작업은 기본적으로 Antigravity write leaf로 보냅니다. 실패하면 chat으로 폴백합니다.
문서 클래스
Class | Git | Session Diary | 용도 |
durable | off | off | 안정적인 제품 문서(README, 기술 문서)용이며 최근 작업 톤을 배제합니다. |
change | on | allowed | Git 변경 사항에 기반한 PR 설명 및 릴리스 노트용입니다. |
transform | off | off | 기존 소스 텍스트의 다듬기, 번역, 요약에만 사용합니다. |
direct | n/a | n/a | 단일 샷 leaf 호출용이며 다단계 오케스트레이션이 없습니다. |
자율 브로커
기본 동작은 감독형이지만 orchestrate_run 도구를 호출하면 자율 브로커 모드로 동작합니다. 브로커가 설정된 leaf MCP 서버를 서브프로세스로 스폰하고 stdio JSON-RPC 클라이언트가 됩니다. 동의(consent)와 인증(auth)은 각 leaf가 강제하므로 브로커가 임의로 우회할 수 없습니다.
leaf 실행 명령은 ~/.orchestrate_codex/leaves.json 파일이나 ORCHESTRATE_CODEX_LEAVES 환경 변수에서 읽습니다. 에러는 auth, rate_limit, timeout, transient, bad_request로 분류합니다. 이 중 bad_request는 폴백 대상에서 제외합니다.
최신 모델 확인
정적 leaf 카탈로그는 금방 낡기 때문에 orchestrate_probe_models 도구로 실제 작동하는 모델을 핑(ping) 쳐서 실측합니다.
설정 파일 예시
leaves.json의 최소 설정 예시입니다.
{
"claude_codex_chat": {
"command": "python3",
"args": ["./scripts/claude_codex_mcp.py"],
"cwd": "/abs/Claude Codex"
},
"google_antigravity": {
"command": "python3",
"args": ["./scripts/google_antigravity_mcp.py"],
"cwd": "/abs/Antigravity Codex"
}
}개발
python3 -m venv .venv
.venv/bin/pip install -e '.[dev]'
.venv/bin/pytest라이선스
MIT
Available Tools
8 toolsorchestrate_context_policyB
Return context policy for a doc_class (durable|change|transform|direct).
| Name | Required | Description | Default |
|---|---|---|---|
| doc_class | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only discloses the basic action and valid parameters. It does not describe side effects, permissions, rate limits, error handling, or output format – leaving 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, well-formed sentence with no wasted words. It is appropriately concise for a simple tool with one 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?
Despite low complexity (one parameter, no nested objects), the description lacks important context: it does not explain what a context policy is, what the return value looks like (no output schema), or how the doc_class values affect the response. This leaves the agent with incomplete understanding.
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 description lists the enum values (durable, change, transform, direct) but these are already present in the input schema. It adds no explanation of what each doc_class means or how they differ, failing to compensate for 0% schema description 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 ('Return') and the resource ('context policy') with a specific qualifier ('for a doc_class'), and lists the allowed enum values, making the purpose unambiguous and distinct from sibling tools which focus on recipes or runs.
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 is provided on when to use this tool versus alternatives (e.g., orchestrate_plan_recipe, orchestrate_get_run). The description only states what it does, leaving the agent to infer usage context without explicit when-to-use or when-not-to-use instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
orchestrate_continue_recipeA
Advance a run after a leaf tool result. Pass run_id and/or full state. On leaf failure set success=false to try fallback_tools (Claude→Grok→AG).
| Name | Required | Description | Default |
|---|---|---|---|
| error | No | ||
| state | No | ||
| run_id | No | ||
| success | No | ||
| stage_id | No | ||
| auto_local | No | ||
| result_text | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for behavioral disclosure. It reveals the fallback behavior on leaf failure and that state/run_id are passed, but it does not disclose side effects (e.g., persistent mutations), required permissions, or response characteristics. The transparency is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no fluff. The first sentence captures the primary purpose and key inputs, and the second adds critical failure behavior. Information is front-loaded and efficiently presented.
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 7 parameters, nested objects, no output schema, and no annotations, the description is incomplete. It omits details about return values, full state structure, and parameters like stage_id, auto_local, and result_text, which are essential for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. It explains only 3 of 7 parameters (run_id, state, success) by mentioning them in context. Missing explanations for error, stage_id, auto_local, and result_text leave significant gaps for the agent.
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 advances a run after a leaf tool result, specifying the core action (advance) and resource (run). It distinguishes from siblings like 'orchestrate_start_run' (starting) and 'orchestrate_get_run' (querying) by focusing on post-leaf continuation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use: after a leaf tool result. It also provides guidance on failure handling by instructing to set success=false to trigger fallback_tools with a specific chain. However, it does not mention when not to use or compare with alternative siblings like 'orchestrate_fallback_chains'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
orchestrate_explain_recipeC
Explain a recipe: stages, doc_class, context policy, default leaf bindings.
| Name | Required | Description | Default |
|---|---|---|---|
| recipe_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It implies read-only behavior ('Explain'), but does not disclose whether there are any side effects, authentication needs, or rate limits. The description lacks behavioral details beyond the basic purpose.
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 very short but not concise in a helpful way; it omits important details. It is under-specified rather than efficiently informative.
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 one parameter, no annotations, no output schema, and multiple sibling tools, the description is incomplete. It does not explain return values, prerequisites, or how to obtain a recipe_id.
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 description must compensate. It does not mention the required 'recipe_id' parameter at all, leaving the agent with no guidance on what value to provide.
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 'Explain' and the resource 'recipe', listing specific aspects (stages, doc_class, context policy, default leaf bindings). However, it does not differentiate from sibling tools like orchestrate_context_policy or orchestrate_plan_recipe, making it less clear when to choose this one.
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 vs alternatives. There is no mention of prerequisites, context, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
orchestrate_fallback_chainsC
Show default capability→fallback leaf tool chains.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description only says 'show', implying a read operation but providing no details on side effects, permissions, or output behavior.
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?
Very concise (one phrase) but lacks structure and informative content; front-loaded but insufficient.
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 no output schema and no annotations, the description is too minimal to cover behavioral context, return format, or what constitutes a 'chain'.
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?
No parameters exist (schema coverage 100% empty), so the description adds no parameter info; baseline 4 applies as per guidelines.
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 'Show default capability→fallback leaf tool chains' uses cryptic notation and vague verb 'show', failing to clearly state what the tool does or distinguish it from siblings like orchestrate_get_run or orchestrate_list_recipes.
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; sibling tools listed but no differentiation provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
orchestrate_get_runA
Fetch run state by run_id (same process only).
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full weight. It discloses the 'same process only' constraint but does not mention side effects, authentication, or error behavior. For a simple read operation, this is minimally adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is front-loaded with the core purpose ('Fetch run state') and includes a critical constraint. No redundant words.
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 one parameter and no output schema, the description is adequate but lacks details on what 'run state' contains and what 'same process only' means in practice. More context on return values would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, and the description only notes that run_id is used to fetch state. It provides no format, example, or additional semantics to compensate for the bare schema. A higher score would require more detail on the parameter's meaning.
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 ('Fetch'), the resource ('run state'), and the identifying parameter ('run_id'), and adds a specific constraint ('same process only'). This distinguishes it from sibling tools like orchestrate_start_run or orchestrate_plan_recipe.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly limits usage to runs within the same process, which guides when to use. However, it does not name alternative tools for cross-process or other scenarios, though the constraint is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
orchestrate_list_recipesA
List built-in supervised orchestration recipes.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description offers no behavioral details beyond the core function. It does not state whether the operation is read-only, idempotent, or if any side effects occur.
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 succinct sentence front-loaded with the verb and resource. Every word is meaningful with no 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?
The description lacks any mention of return format, pagination, or scope. Given no output schema, the description should provide more completeness about what the listing includes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and 100% schema description coverage, so the description does not need to add parameter meaning. Baseline is 4 for zero-parameter tools.
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 (list) and the resource (built-in supervised orchestration recipes). It distinguishes from sibling tools like orchestrate_continue_recipe or orchestrate_plan_recipe by focusing on listing rather than executing or explaining.
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 when one needs to list recipes, but provides no guidance on when not to use it or how it differs from alternatives. No explicit context for selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
orchestrate_plan_recipeC
Build a static plan (steps + suggested tools). Prefer start_run for stateful execution.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| prompt | No | ||
| system | No | ||
| bindings | No | ||
| recipe_id | Yes | ||
| instruction | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only mentions building a static plan, with no information on side effects, permissions, or output format. This is insufficient.
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 short and front-loaded, with two sentences. However, it sacrifices completeness for brevity; many details are missing.
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 6 parameters (none described), no output schema, and no annotations, the description is severely under-informative for an agent to use the tool effectively.
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%, and the description adds no meaning to any of the 6 parameters (model, prompt, system, bindings, recipe_id, instruction). Without parameter descriptions, the agent cannot use them correctly.
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 builds a 'static plan (steps + suggested tools)' and distinguishes it from start_run for stateful execution. However, 'static plan' could be more specific.
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 advises to 'prefer start_run for stateful execution', giving some guidance on when not to use this tool. But it lacks explicit when-to-use scenarios or exclusion criteria beyond that.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
orchestrate_start_runC
Start a supervised run. Auto-executes local gather stages. Returns next_action (call_tool with leaf name+args, or done). Does not call other MCP servers — Codex must invoke the leaf tool.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| prompt | No | ||
| system | No | ||
| bindings | No | e.g. {"chat":"grok_codex_chat"} | |
| recipe_id | Yes | ||
| auto_local | No | ||
| instruction | No | ||
| project_root | No | Workspace root for gather_facts / gather_git. | . |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description reveals that the tool auto-executes gather stages, returns next_action, and does not call other servers. However, with no annotations, it omits details on error handling, idempotency, authentication, or the effect of optional parameters.
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 concise with two sentences, front-loading the main action and adding key behavioral notes. It could benefit from more structured format, but remains efficient.
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 8 parameters, low schema coverage, no annotations, and no output schema, the description lacks completeness. It fails to explain the return format fully, the meaning of 'local gather stages', or how bindings and other parameters interact.
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 only 25%, yet the description adds no parameter-level details. It does not explain how parameters like model, prompt, or bindings affect the run, nor does it clarify the role of recipe_id beyond being required.
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 starts a supervised run and auto-executes local gather stages. It differentiates by noting it does not call other MCP servers, but does not explicitly distinguish from sibling tools like orchestrate_plan_recipe or orchestrate_continue_recipe.
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 what the tool does but not the context for its selection, such as prerequisites or conditions for use.
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. Dates show when Glama detected each change.
8 tool updates
v0.2.0- First observed
orchestrate_context_policy - First observed
orchestrate_continue_recipe - First observed
orchestrate_explain_recipe - First observed
orchestrate_fallback_chains - First observed
orchestrate_get_run - First observed
orchestrate_list_recipes - First observed
orchestrate_plan_recipe - First observed
orchestrate_start_run
TDQS
Each tool targets a distinct aspect of recipe orchestration: context policy, continuing runs, explaining recipes, fallback chains, fetching run state, listing recipes, planning, and starting runs. No overlaps are apparent.
All tools follow the 'orchestrate_' prefix with clear verb_noun or noun phrases, but some like 'orchestrate_fallback_chains' and 'orchestrate_context_policy' use noun-noun patterns while others use verb-noun. Still mostly consistent and predictable.
With 8 tools, the set is well-scoped for a recipe orchestration server, covering planning, execution, state retrieval, and explanation without being overwhelming.
The surface covers core lifecycle operations (plan, start, continue, get run, explain, list) and supporting features (fallback chains, context policy). A minor gap is the absence of a cancellation or stop-run tool, but it's not critical for the stated purpose.
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