This server is an automation engine for publishing finalized articles to external platforms (WeChat, Juejin, CSDN, Zhihu) and managing their full publication lifecycle—without generating or editing content.
Check platform authentication with platform_auth_probe without exposing credentials.
Preflight content with publish_content_preflight against official platform rules before publishing.
Create idempotent publish jobs with publish_job_create, including optional scheduling, tagging, and categorization.
Run publish jobs immediately or on schedule with publish_job_run.
Retrieve job status and details with publish_job_get.
Verify publication and resolve public URLs with publish_job_verify.
Check article liveness with publish_liveness_check and manage lifecycle states (provisional, stable, rejection, timeout, removal detected).
Check verification due with publish_verification_due based on the next verification timestamp.
It also supports third-party platform adapters, an HTTP API for job management and system monitoring, durable audit events and webhooks, JSON/SQLite storage, and an authenticated loopback bridge for real platform execution.
Provides publishing tools for the CSDN platform, including preflight checks, publish job creation and execution, and verification via an authorized local bridge or custom transport. Supports mock and real modes.
Provides publishing tools for the Juejin platform, including preflight checks, publish job creation and execution, and verification via an authorized local bridge or custom transport. Supports mock and real modes.
Provides publishing tools for the WeChat platform, including preflight checks, publish job creation and execution, and verification via an authorized local bridge or custom transport. Supports mock and real modes.
Provides publishing tools for the Zhihu platform, including preflight checks, publish job creation and execution, and verification via an authorized local bridge or custom transport. Supports mock and real modes.
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., "@content-publish-engine-mcp Preflight my WeChat draft and show official rule coverage"
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.
An automation engine that takes a completed article, publishes it to external platforms, discovers the public URL, and keeps verifying that the article remains live.
It deliberately does not generate, rewrite, plan, or score content. Your CMS, editor, AI agent, or Markdown workflow owns the article; this engine owns reliable publication.
completed article -> scheduled job -> authenticated publish -> platform acceptance
-> public URL discovery -> provisional public observation
-> 24h check -> 72h stable publication -> removal detectionUnlike draft-sync tools, a successful job means the engine performed the publish action and entered verification. Draft creation alone is not treated as publication.
Related MCP server: mcp-server-wechat-mp
Adapter registry for WeChat Official Account, Juejin, CSDN, Zhihu, and third-party plugins.
Idempotent jobs, content hashes, platform write locks, worker leases, and duplicate protection.
Immediate and scheduled publication through one PublishOrchestrator.
Read-only verification that never repeats an ambiguous publish action.
Public URL discovery, verification backoff, 24/72-hour survival checks, stable publication, rejection, timeout, and removal states.
JSON and SQLite repositories plus a distributed lock/repository contract.
Long-running worker, MCP server, HTTP API, and authenticated local bridge.
Webhook events, durable audit events, and in-process telemetry primitives.
Versioned official-rule registry with source metadata.
Generic asset resolution/upload contracts.
Versioned browser-selector bundles and structure-change detection.
An included WeChat Official Account executor using the official draft and free-publish APIs.
Node.js 22.5 or newer. Node 24 LTS is recommended.
npm ci
npm run checkimport {
PublishOrchestrator,
SqlitePublishRepository
} from "content-publish-engine/publish-engine";
const repository = new SqlitePublishRepository(".data/publish.db");
const engine = new PublishOrchestrator({ repository });
const { job } = await engine.createJob({
jobId: "request-42",
platform: "wechat",
article: {
sourceId: "cms-article-42",
title: "A completed article",
markdown: "<p>Final HTML or Markdown body</p>",
scheduledAt: new Date().toISOString()
}
});
await engine.runPublishJob(job.id);npm run mcp:start
npm run http:start
npm run worker:start
npm run bridge:startThe worker processes due verification jobs before new publish jobs. SQLite is the default runtime repository; set PUBLISH_REPOSITORY=json for low-volume local JSON storage.
HTTP endpoints:
POST /v1/jobs
GET /v1/jobs
GET /v1/jobs/:id
POST /v1/jobs/:id/run
POST /v1/jobs/:id/verify
POST /v1/run-due
GET /v1/reliability
GET /v1/telemetry
MCP tools include platform discovery/auth, preflight, job creation/execution/read-only verification, due-job execution, liveness checks, and reliability metrics.
The built-in transport calls an authenticated loopback bridge:
PUBLISH_ENABLED=true
PUBLISH_BRIDGE_URL=http://127.0.0.1:9528
PUBLISH_BRIDGE_TOKEN=replace-with-a-local-secretThe repository includes a WeChat Official Account executor. Other platforms use the same plugin and transport contracts and require an authorized executor supplied by the operator. The project never ships account cookies or attempts to bypass CAPTCHA or risk controls.
Before the first live write, record the operator's explicit authorization locally:
npm run authorize -- wechat operator-nameThe authorization record contains no platform credential and is ignored by Git through .data/.
For the included WeChat executor, also set WECHAT_APP_ID and WECHAT_APP_SECRET, start the bridge, then run the worker or call the HTTP/MCP run endpoint. The executor creates the official draft when needed, submits the free-publish action, and queries the same task for its public URL; it never treats draft creation as successful publication.
See adapter guide, official rule policy, and live authorization.
JSON: single-process local use.
SQLite: durable local service with WAL and transactional claims/locks.
Distributed deployments: implement PublishRepository and DistributedLockProvider with your database and lock service.
Check whether a platform adapter is authenticated without returning credentials.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It adds the key behavioral disclosure that it does not return credentials, which is not inferable from the name alone. However, it does not specify return format, error behavior, or side effects, so it does not fully cover all transparency needs.
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, focused sentence with no redundant words. It immediately states the action and the key limitation, earning every word.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description covers the core behavior but omits expected output details (e.g., boolean vs error) and any context about why one would probe auth. Since there is no output schema, the agent is left guessing about the return format, making this moderately complete but not fully sufficient.
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 does not mention the 'platform' parameter or its possible values. While the schema provides an enum, the description adds no additional meaning about how to select or interpret the platform values, leaving the agent to infer from the tool name.
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 the specific verb 'Check' with a clear resource ('platform adapter') and outcome ('whether authenticated'). It also distinguishes itself from siblings by adding the safety constraint 'without returning credentials' and the focus on auth state, which is distinct from the publishing workflow tools.
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 its use case (checking auth before publishing) but provides no explicit when-to-use vs alternatives or exclusions. Context signals show siblings, but the description does not reference them. For a simple one-parameter tool, the usage is fairly obvious, but the lack of explicit guidance keeps it at a minimum viable score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check publish-payload completeness and verified official platform rules without applying editorial preferences.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| tagIds | No | ||
| markdown | Yes | ||
| platform | Yes | ||
| categoryId | No | ||
| coverMediaId | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses a non-obvious behavioral trait ('without applying editorial preferences') and the verb 'Check' suggests a read-only operation. However, it does not state whether the tool mutates state, requires authentication, or how it reports incompleteness or rule violations.
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. Every word contributes to defining the tool's purpose and boundary, making it highly concise and easy to parse.
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 six parameters, three required fields, no annotations, and no output schema, the description is too sparse for an agent to fully understand invocation expectations. It does not mention return behavior, error conditions, or what 'completeness' concretely involves, leaving 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?
Schema description coverage is 0% and the description provides no parameter-level details. It alludes to payload completeness and platform rules, but it does not explain the meaning or role of title, markdown, platform, tagIds, categoryId, or coverMediaId beyond what the raw schema already shows.
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 ('Check') and names a concrete resource ('publish-payload completeness' plus 'verified official platform rules'). It also clarifies scope by explicitly excluding editorial preferences, distinguishing it from content-editing or publishing pipeline tools among the 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 implies this is a preflight validation step ('Check publish-payload completeness and verified official platform rules') but gives no explicit when-to-use vs. alternatives, such as publish_liveness_check or publish_job_create. It does not name alternatives or exclusion criteria, leaving usage largely inferred from the tool name and sibling context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create an idempotent publish job from a final draft. Returns a durable job handle.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| tagIds | No | ||
| draftId | No | ||
| markdown | Yes | ||
| platform | Yes | ||
| categoryId | No | ||
| scheduledAt | No | ||
| coverMediaId | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the operation is idempotent and returns a durable job handle, which are useful behavioral traits. However, it does not explain side effects, prerequisites, or error conditions, leaving some ambiguity about the actual publish flow.
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, using two short sentences to convey the core purpose and key behavior. Every word earns its place, with no redundancy or irrelevant detail.
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 (8 parameters, no output schema, no annotations), the description is insufficient. It fails to explain parameter usage, the expected return value beyond 'durable job handle', or any constraints on the input, leaving the agent with significant gaps in understanding how to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for its 8 parameters, and the description does not compensate. It only vaguely mentions 'final draft' without linking to specific parameters like draftId or markdown, providing no meaningful semantic guidance for the parameters.
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 a specific action: creating an idempotent publish job from a final draft. It distinguishes this from sibling tools like publish_job_run, publish_job_get, and publish_job_verify by focusing on creation and returning a durable handle.
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 'from a final draft' implies when this tool should be used, but there is no explicit guidance on alternatives or exclusions. Sibling tools are not referenced, and no conditions for choosing this over others are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read one publish job and its current status from the local job store.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavioral traits itself. It clearly states the operation is a read and returns current status, implying no modification. However, it does not disclose details like error behavior, required authentication, or whether the job store is local-only, leaving some 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 is front-loaded with the verb and core concept. Every word adds value, with no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with one parameter and no output schema, the description covers the essential purpose but lacks richer context like error handling, prerequisites, or why the local store matters. It is adequate but not thorough, given no annotations or output schema to fill 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%, and the description does not mention the 'jobId' parameter at all. Despite the parameter name being self-explanatory, the description fails to add any semantic meaning, such as format or required context, placing the full burden on 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 uses a specific verb 'read' to indicate retrieval, specifies the resource 'publish job', and adds scope 'one' and source 'local job store'. It clearly distinguishes from sibling tools like publish_job_create, publish_job_run, and publish_job_verify, making the purpose unambiguous.
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 usage context is implicitly clear: use this to read a publish job's status. However, it does not explicitly state when to prefer this over alternatives or mention any exclusions. No when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute one publish job by calling the platform adapter directly. Returns the publish result.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing side effects and behavioral traits. It states that the tool 'Returns the publish result' and mentions the direct adapter call, but it does not disclose whether this operation is mutating, whether it requires authentication, what happens to the job state, or potential errors. For a tool that 'executes' an action, this is a significant gap.
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 concise sentences. Both provide distinct, useful information: the action/mechanism and the return value. There is no redundancy or filler, making it appropriately sized for a tool with a single 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?
The tool is simple (one parameter, no output schema), but the description lacks context about the publish job lifecycle. It does not explain what a 'publish job' is, how this tool fits with siblings like publish_job_create or publish_job_verify, or what 'publish result' entails. The minimal information leaves the agent guessing about the tool's role and expected usage flow.
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 0% description coverage, and the description does not explain the meaning or format of 'jobId'. The tool description only mentions 'one publish job' but never ties it to the parameter. Since the schema does not document the parameter and the description makes no attempt to clarify it, the agent has no guidance beyond the parameter name.
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 ('Execute one publish job') and the resource ('publish job'), distinguishing it from siblings like publish_job_create, publish_job_get, and publish_job_verify. The phrase 'calling the platform adapter directly' adds specificity about how it executes.
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 through the tool name and the phrase 'calling the platform adapter directly,' but it does not explicitly state when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. There is no direct comparison with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify a previously submitted publish job and resolve its public URL.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool resolves a public URL, but does not indicate whether the operation is read-only, whether it blocks until verification completes, what permissions are required, or what error conditions might occur. This lack of behavioral detail is a significant gap.
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, concise sentence that front-loads the primary action and result. It contains no redundant information or filler, making it efficient and easy to parse.
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 conveys the basic purpose and expected result. However, given the absence of annotations, it lacks important context about safety, retry behavior, and potential failure modes. The description is adequate but not comprehensive.
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 single parameter jobId has 0% schema description coverage. The description adds the hint that it refers to a 'previously submitted' job, which gives some semantic context. However, it does not explain how to obtain or format the ID, though the parameter name is self-explanatory.
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 (verify), the resource (publish job), and the outcome (resolve its public URL). It is specific enough to distinguish from sibling tools like publish_job_get, which likely retrieves job details without providing the public URL.
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 'previously submitted publish job' provides clear context that this tool should be used after job submission, not for creating or running jobs. It does not explicitly mention alternatives or exclusions, but the context is reasonably clear for an agent to choose it over sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check whether an observed public article is still live and advance its lifecycle state.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes | ||
| schedule | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses the core behavior (liveness check and lifecycle state advancement) but omits key details such as network I/O, failure handling, or the exact state transition. It is not misleading, but it lacks depth about side effects and preconditions.
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-structured sentence that is immediately actionable. Every word contributes to the core purpose, with 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?
Given a nested object schema and no output schema, the one-sentence description is insufficient. It does not explain the role of the schedule object, define return values, or offer any operational context, making it incomplete for an agent to invoke safely and interpret results.
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 does not explain any parameter meaning. jobId and the nested schedule object fields are left entirely to inference, with no help from the text. The description fails to compensate for the missing schema documentation.
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 a specific action ('Check whether an observed public article is still live') and a distinct consequence ('advance its lifecycle state'). This differentiates it from siblings like publish_job_verify or publish_verification_due by emphasizing the liveness check and state transition.
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 does not mention conditions like 'when verification is due' or exclude cases better suited for publish_job_verify, leaving the agent without decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check whether a publish schedule is due for verification based on its nextVerificationAt.
| Name | Required | Description | Default |
|---|---|---|---|
| now | No | ||
| nextVerificationAt | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description implies a read-only check but does not explicitly state side effects, return value, or error behavior. With no annotations, it fails to disclose whether the tool is pure, what it returns (e.g., boolean), or how edge cases (invalid timestamps, missing 'now') are handled.
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, grammatically correct sentence with no wasted words. It front-loads the verb and clearly states the core functionality, achieving maximum conciseness for the information 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?
Given the absence of annotations, output schema, and parameter descriptions, the description should compensate by providing comprehensive context. It fails to explain the return value, the role of 'now', or any behavioral nuances, leaving significant gaps for a tool with only two parameters.
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%. The description mentions 'nextVerificationAt' but does not explain its format or semantics, and completely ignores the optional 'now' parameter. The agent receives no help understanding parameter meaning, especially since the schema provides no descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Check') and resource ('publish schedule'), and specifies the condition ('due for verification based on its nextVerificationAt'). This distinguishes it from sibling tools like publish_job_verify, which likely verifies job status rather than schedule due-ness.
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. The description does not mention workflow context, prerequisites, or any exclusions. Sibling tools exist but are not referenced, leaving the agent without decision criteria for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Most tools target distinct actions (preflight, create, run, get, verify, liveness, due-check, auth-probe). However, publish_liveness_check and publish_job_verify both involve verifying published state, which could cause confusion despite differing contexts.
Naming is inconsistent: publish_job_create/run/get/verify follow verb_noun, but publish_liveness_check, publish_verification_due, and publish_content_preflight are noun phrases, and platform_auth_probe breaks the publish_ prefix. This mixed pattern reduces predictability.
With 8 tools, the set is well-sized for a content publishing engine, covering the main workflow without being bloated. Each tool addresses a clear step in the process.
Core lifecycle (preflight, create, run, verify, liveness) is covered, enabling end-to-end publishing. Minor gaps exist: no job deletion/listing or schedule management, but these are not essential for the stated purpose.
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