Posthell

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the transparency burden. It discloses critical behavior: the post is never published, the user approves it in a dashboard, proposedTime only pre-fills the scheduler, and platforms default to all connected accounts. This is significant and useful, though it omits details like return values or error handling.

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 three sentences, each earning its place: the core action, the non-publishing caveat, and the optional parameters. It is front-loaded with the verb and resource, and there is no unnecessary 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 (3 parameters, no output schema, no annotations), the description covers the essential workflow, defaults, and limitations. It could mention what the tool returns upon success or any prerequisites, but the provided context 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 description coverage is 100%, so the baseline is 3. The description adds little beyond the schema: it paraphrases the schema's notes about platforms defaulting and proposedTime being a suggestion. No additional semantic value is provided.

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: saving a post as a draft in the user's queue, explicitly noting it is never published. This distinguishes it from sibling tools like list_posts (read) and shape_post (edit) by highlighting the draft-and-approve workflow.

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 clear context for when to use this tool: when creating a draft that requires user approval, and clarifies that the tool does not publish. It does not explicitly name alternatives or exclusions, but the context is enough to differentiate from siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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.