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

With no annotations, the description carries the burden and does add useful context: the data is scoped to this month, failed events are counted, deduplication is applied, and bot traffic is billed the same as human traffic. It does not discuss permissions, freshness, or return format, but for a read-only stats tool the disclosed behavioral nuances are substantial.

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 definition is a single dense sentence that front-loads the time scope and then enumerates the metrics. It is efficient though somewhat packed; every clause adds relevant content.

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 and no annotations, so the description must convey what the tool returns. It names the key metrics and billing split, which is enough for an agent to understand the response's substance, though it does not describe response structure or units.

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, so there are no parameter semantics to explain; the baseline for 0 params is 4. The description does not need to compensate for any schema gaps.

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 specifies the resource clearly: this month's accepted, deduplicated, and failed events, plan/free allowance, and human/app vs agent/bot usage split. It is specific enough to distinguish from track_event (which records events) and test_meta_connection, though it uses a noun phrase rather than an explicit verb.

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?

Usage is implied: this is the tool for checking current-month usage stats. There is no explicit when-to-use guidance, no exclusions, and no direct mention of when to choose it over the sibling tools.

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 burden and does well: it discloses the exact destination (Meta Events Manager test events), that it relies on already-saved workspace pixel/token/test_event_code, and that production data is untouched. It omits what happens when credentials are missing or why a result would be 'skipped'.

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 short sentences, each doing distinct work: what is sent, what comes back, and what is not affected. The key safety fact is stated last but not buried.

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?

With no parameters and no output schema, the description must cover the return shape, and it does ('sent/skipped/failed with Meta's error and a hint'). The meaning of 'skipped' — presumably missing or invalid workspace credentials — is left for the agent to infer.

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 is 4. The description correctly notes that configuration comes from the workspace's saved Meta pixel, token and test_event_code rather than from inputs, which is the useful fact for an 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?

States a concrete verb and resource ('Send one synthetic Lead to Meta Events Manager → Test events') plus the credentials it uses, so the action is unambiguous. The word 'synthetic' and 'Does not affect production data' implicitly separate it from track_event, but no sibling is named explicitly.

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 framing ('Test events', 'synthetic Lead', 'does not affect production data') makes the intended use — verifying the Meta connection without touching production — clear. It stops short of explicitly saying 'run this before track_event' or naming what to use instead when validating real payloads.

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 so well: it discloses the processing pipeline (normalize, SHA-256 hash, dedupe) and, critically, that Meta sends live while Google is validate-only and LinkedIn is a dry-run — side-effect facts an agent would otherwise get badly wrong. It also states billing impact and dedupe economics, leaving only rate limits and auth mechanics unspecified.

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 core action is front-loaded in the first clause, followed by side-effect disclosure, retry rule, and cost in descending order of importance. Dense but every sentence carries distinct information; the parenthetical event list is the only mild redundancy with the event_name schema examples.

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 9-parameter mutation with a nested user object and no output schema, it covers the highest-risk unknowns: per-destination behavior, dedupe, and billing. The remaining gap is what a successful call returns (accepted/rejected per destination) and how per-destination failures are surfaced, which an agent would want before treating the call as done.

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 67%, and the description adds real meaning on top of it by explaining what the destinations enum values actually do per platform and why event_id must be stable across retries. The value/currency pairing and timestamp default are left to the schema, which covers them adequately.

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 opening clause states a specific verb and resource ('Send one server-side conversion') and enumerates the event kinds it accepts. It is unmistakably distinct from the siblings get_stats and test_meta_connection, which are read/verification tools rather than event ingestion.

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?

It gives explicit operational guidance for retries ('Reuse the same event_id on retries; duplicates are free and not re-sent') and surfaces the cost model that selects for careful batching. It does not, however, tell the agent when to set test=true versus calling the sibling test_meta_connection to validate credentials first.

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