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

With no annotations, the description carries the full burden. It usefully discloses that the call is non-mutating ('does not start new work') and free of cost, but says nothing about what a returned job looks like, possible job states, or behavior when the id is unknown or the job failed.

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?

Two short sentences, front-loaded with the action and followed by the key usage constraint. Every clause earns its place with no 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?

For a simple one-parameter lookup with no output schema or annotations, the description covers what it is, when to call it, and its cost/side-effect profile. The only gap is guidance on interpreting or polling the returned job status.

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% and the single job_id parameter is already documented in the schema, so the description adds only the reinforcing phrase 'by id'. Baseline 3 is appropriate when the schema does the parameter work.

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?

States a specific verb (look up) and resource (a social post generation job) with the lookup key (id), which is unambiguous. It is clearly distinguished from the sibling generate_social_post, which it explicitly references.

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?

Gives an explicit when-to-use condition: 'only when generate_social_post timed out and returned a job id.' It also clarifies when-not by noting it does not start new work, so the agent knows not to use it for initiation.

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 and does a good job: it discloses blocking behavior with a concrete latency range (20-90s), that credits are consumed, and the non-obvious side effect that output is rewritten to read as human-authored. It does not cover auth requirements or error behavior, keeping it short of a 5.

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?

Four tight sentences with zero filler, front-loading what the tool does before the behavioral facts (blocking time, cost). Efficient, though the final 'Costs credits' sentence is terse enough that it could carry more cost 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?

For a 13-parameter generation tool with no output schema and no annotations, the description covers the essentials an agent needs: latency, blocking semantics, credit cost, and the multi-step pipeline. The main gap is that it never indicates what is returned (single post vs. quantity variations with scores/images), which an agent might want given no output schema.

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 schema already documents all 13 parameters, including defaults and platform word counts. The description adds no parameter-level detail beyond the summary sentence, so the baseline 3 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?

States a specific verb and resource (write a social media post) scoped to a platform and topic, and enumerates the pipeline steps (platform conventions, human-rewrite, AI detection, image). It is distinguishable from check_social_job and list_social_platforms by name and intent, though it never explicitly names those 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 usage (generate a post for a platform/topic) and adds two useful operating facts — it blocks until completion and costs credits. However, it never states when to prefer this tool over check_social_job or how it relates to an async job flow, so the routing guidance is only implied.

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 and does disclose meaningful behavior: zero cost, no network call (i.e., a safe, side-effect-free read), and the shape of what is returned. It stops short of stating read-only guarantees formally or whether results are cached/static, but for a trivial list tool this is solid disclosure.

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?

Two short sentences, front-loaded with what is listed and followed by the cost/network guarantee. Every clause carries information; 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?

There is no output schema, so the description must characterize the return value, and it does by naming the platform list plus per-platform length and emoji policy. A simple enumeration tool is adequately covered, though the exact response format (array vs object, field names) is left unspecified.

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 takes zero parameters, so the baseline of 4 applies; there is nothing for the description to clarify beyond confirming no arguments are needed. The description adds the useful detail that the listed items carry attributes (length, emoji policy) rather than being bare names.

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?

States a specific verb (list) and resource (platforms) and explicitly ties the resource to the sibling generate_social_post, so the agent can distinguish it from check_social_job. It also names the returned fields (default post length, emoji policy), removing ambiguity about what 'platform' data means.

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 the natural use case (discover what generate_social_post supports before calling it) and notes it is free and offline, but never states an explicit when-to-use or when-not-to-use rule relative to check_social_job. Usage is inferable rather than spelled out.

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