Social media scheduler for AI agents: draft posts into a human-approved queue for 15 networks.
Scored across 4 tools
Each tool has a clear action: create_draft writes, get_growth reads metrics, list_posts reads post history, shape_post generates angles. The two read tools (get_growth vs list_posts) and the two post-related tools (shape_post vs create_draft) are distinguishable by their descriptions, though a novice could momentarily confuse the ideation and drafting steps.
All four tools follow a clean, consistent verb_noun snake_case pattern: create_draft, get_growth, list_posts, shape_post. No mixed conventions or vague verbs.
Four tools is small but well-scoped for a focused social posting assistant, with each tool earning its place in the workflow. It sits at the thinner end but is not an extreme mismatch for the domain.
The core flow (check growth, review posts, shape angles, draft) is covered, but there is no update/edit or delete for drafts and posts, and no direct scheduling/publish control beyond proposing a time, leaving notable editing lifecycle gaps.
Save a post as a DRAFT in the user's posthell queue. It is never published by this tool - the user approves it in the dashboard. Optionally propose a publish time (pre-fills their scheduler) and limit which platforms it targets (default: every connected account).
| Name | Required | Description | Default |
|---|---|---|---|
| body | Yes | The post text | |
| platforms | No | Limit to these platforms (default: all connected accounts) | |
| proposedTime | No | Suggested publish time, ISO 8601. Only a suggestion - pre-fills the user's scheduler. |
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 does not publish, that the user approves in the dashboard, that proposedTime pre-fills the scheduler, and that platforms default to all connected accounts. It could still mention permissions or whether drafts can be edited, but the core mutation behavior is well explained.
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?
Three sentences, zero waste, and the key constraint (never published by this tool) is front-loaded after the purpose. No repetition or filler.
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 three parameters, 100% schema coverage, no output schema, and no annotations, the description covers the essential behavior: what the tool does, what it does not do, and the effect of optional parameters. It could be improved with notes on validation or error handling, but it is sufficient 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?
Schema coverage is 100%, and the schema already documents each parameter with descriptions. The description adds some meaning about the effect of proposedTime and platforms (pre-fills scheduler, default all accounts), but it largely repeats what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.
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?
Clearly states a specific verb and resource: 'Save a post as a DRAFT in the user's posthell queue.' It distinguishes this from sibling tools by specifying the queue and the 'never published by this tool' constraint, so an agent can tell it apart from list_posts, get_growth, or shape_post.
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 states when to use this tool (to save a draft for approval) and when not (it is never published by this tool). It also implies the alternative route (user approves in the dashboard), giving the agent a complete picture.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The user's follower counts and 7-day growth per connected network, plus which network is growing fastest. Use this to decide where a post matters most or to report progress.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 the return content (follower counts, 7-day growth, fastest-growing network) but does not explicitly state that it is a read-only operation or describe any potential limitations such as data freshness or network availability. For a simple read tool, this is acceptable but not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first states the data returned, the second gives the intended use case. Both sentences are concise, front-loaded, and free of filler, making it highly 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?
For a zero-parameter read tool with no output schema, the description adequately covers return semantics (counts, growth, fastest network) and intended use. It lacks a detailed return format or network details, but the tool is simple enough that this is not a major gap.
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 the schema coverage is 100% (empty properties). The description adds context about what data the tool returns, which supplements the empty schema. Per the baseline for 0 params, a score of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (follower counts and growth per connected network) and distinguishes it from content-creation siblings by specifying the analytics focus. Although it uses a noun phrase rather than an explicit verb like 'retrieves', the intent is 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 description explicitly states when to use the tool: 'to decide where a post matters most or to report progress.' It does not mention alternatives or when not to use it, but given the sibling tools are unrelated (drafting, listing, shaping posts), the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read the user's recent posts and drafts with per-platform status. Published posts include engagement metrics (likes, comments, shares, engagementRate - impressions on plans with full analytics) once analytics have synced. Use it to avoid duplicating a topic AND to learn which topics performed before drafting the next post.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 15) | |
| status | No | Filter by status (posted and published are synonyms) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the read-only nature via 'Read', notes that metrics are only for published posts, and conditions them on 'once analytics have synced'. It could mention pagination but is adequately transparent for a read tool.
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?
Three sentences with the primary function front-loaded, metrics caveat in the second, and use case in the third. Every sentence earns its place with no filler.
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 covers what the tool returns, when to use it, and the analytics syncing caveat. For a simple list tool with two optional parameters and no output schema, this is sufficient, though sorting or pagination could be mentioned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for both limit and status, including defaults and enum meanings. The description adds minimal extra context like 'per-platform status' but does not substantially enrich parameter semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: 'Read the user's recent posts and drafts with per-platform status' and mentions engagement metrics. This specific verb+resource combination distinguishes it from siblings like create_draft and shape_post.
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 provides usage context: 'Use it to avoid duplicating a topic AND to learn which topics performed before drafting the next post.' It does not explicitly name alternatives or exclusions, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Turn rough notes about what the user did (shipped a feature, fixed a bug, hit a number) into 2-3 finished social-post angles in the user's voice. Grounded only in the notes - never invents facts. Uses one AI generation from the user's monthly quota.
| Name | Required | Description | Default |
|---|---|---|---|
| notes | Yes | Raw notes about what the user did. Rough is fine. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well: it discloses the output (2-3 angles), the fidelity constraint ('never invents facts'), and a concrete side effect ('Uses one AI generation from the user's monthly quota'). It doesn't explicitly state non-mutating behavior, but the transformation framing implies it. This is strong transparency for a simple tool.
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, front-loaded with the primary function, followed by essential constraints. No redundant information. Every sentence adds value, and it is neither too terse nor overly verbose.
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 simplicity of the tool (one parameter, no output schema, no annotations), the description covers the essential context: input, output, constraints, and quota impact. It could elaborate on the exact structure of the returned angles, but that is not critical for a transformation tool. Overall, it is sufficiently complete for most AI agents.
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 already provides a description for 'notes' (raw notes, rough is fine), giving 100% coverage. The tool description adds meaning by giving examples of what the notes can include ('shipped a feature, fixed a bug, hit a number') and clarifies the grounding constraint ('Grounded only in the notes - never invents facts'), which enriches the parameter semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Turn' and clearly states the resource and outcome: rough notes become '2-3 finished social-post angles in the user's voice'. It also mentions grounding constraints, which helps distinguish it from sibling tools like create_draft (likely creates drafts) and get_growth (analytics). The title reinforces the purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use this tool: when you have rough notes about the user's actions and need social-post angles. It provides context (shipped a feature, fixed a bug, hit a number) but does not explicitly mention alternatives or when not to use it. This is a clear context with no exclusions, fitting the '4' level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
create_draft1 field changedInput schema / properties / platforms / items / enumPrevious value: -[
- "x",
- "linkedin",
- "instagram",
- "threads",
- "bluesky",
- "facebook",
- "tiktok",
- "youtube",
- "pinterest",
- "reddit",
- "telegram",
- "discord",
- "snapchat",
- "googlebusiness",
- "whatsapp"
-]New value: +[
+ "x",
+ "linkedin",
+ "instagram",
+ "threads",
+ "bluesky",
+ "facebook",
+ "tiktok",
+ "youtube",
+ "pinterest",
+ "reddit",
+ "telegram",
+ "discord",
+ "googlebusiness",
+ "whatsapp"
+]create_draftget_growthlist_postsshape_postSocial post scheduling for AI agents: queue or draft posts to 12 connected networks.
Draft, schedule and publish social media posts from any AI agent.
Social media scheduler your AI agent can drive: plan, publish, inbox, analytics on 12+ networks.
Draft, schedule and publish social posts to nine platforms from any AI agent.
Add one secure layer between your agents and this server.
