TokenZero

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 usefully discloses that analysis is static (not executed) and limited to public repos, and it previews the output ('findings with fixes and an estimated recoverable percentage'). It does not address size/time limits, whether the repo is cloned, or permissions, which are material for an unannotated 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?

A single front-loaded sentence with the action, input scope, detection categories, and output all present; nothing is padding. It is dense with a long embedded list, which costs a little readability but not wasted words.

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 inputs and the shape of the result reasonably well, and no output schema exists so it must carry the return-value burden. However, both parameters are optional (required: 0) and the description never says what happens if neither repo nor files is supplied — a real ambiguity for a 2-param unannotated tool.

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%, so both parameters are already documented, including that 'files' is an alternative to 'repo'. The description reinforces the public-repo constraint and the either/or relationship but adds no format or sizing detail 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?

States a specific verb ('Static-analyze'), a named resource ('public GitHub repo (owner/name) or a set of source files'), and the exact defect classes it looks for. That detail distinguishes it from siblings like workspace_metrics or calculate_model_arbitrage without the agent needing to open any schema.

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 only implied by the description of what the tool detects; there is no explicit 'use this when...' guidance, no exclusions, and no routing to siblings such as calculate_model_arbitrage. The 'repo or files' phrasing at least signals the two supported input modes.

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 helpfully discloses that prices are list prices and pins the pricing table version (2026-10-26), which signals staleness assumptions. However it does not state that results are estimates, whether unsupported models error out, or what currency/units apply.

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 dense sentences with the primary action and scope front-loaded, followed by the version stamp. 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?

For a 6-parameter calculation tool with no output schema and thin schema descriptions, the description covers the conceptual outputs well but leaves the return shape, currency, and failure behavior unspecified. Adequate but with clear 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 coverage is only 17% (just static_tokens), so the description must compensate. It does partially: 'per month' implies requests_per_month, 'prompt caching' maps to static_tokens, and 'across models / cheaper sibling' hints at compare_to. But model string format, token units, and compare_to semantics remain undocumented.

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?

Uses a specific verb ('Price a workload at list prices across models') and enumerates the three outputs: per-request cost, per-month cost, and prompt-caching effect. It does not name any sibling tool, so differentiation from optimize_prompt_payload or audit_repo_token_waste is left to inference.

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 a cost-estimation scenario but never states when to use this versus the sibling tooling (e.g. optimize_prompt_payload or audit_repo_token_waste). No prerequisites, exclusions, or alternative routing are given.

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 reasonably well: it discloses the returned artifacts (optimized body, diff of changes, token profile, projected per-request saving) and gives a strong behavioral guarantee that the tokens the model sees are never changed, which tells the agent this is a safe, semantics-preserving transformation. It stops short of saying whether the provider is contacted, whether credentials are required, or whether the input body is mutated in place.

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 tight sentences, zero filler, with the transformation and its target front-loaded before the return contract and the safety guarantee. Every clause earns its place.

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 return values are well covered, which is good given there is no output schema, and the core behavior is clear. But for a tool with two unexplained enum parameters and an opaque nested object body, the description leaves out the information an agent needs to actually construct a correct call.

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 only 33% — 'body' is documented merely as 'The request body' and both 'endpoint' and 'strategy' are bare enums with no meaning attached. The description adds nothing about them: it never explains that 'endpoint' selects the provider dialect or what 'static' versus 'conversation' strategy implies, and the nested body shape is left entirely undocumented.

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?

Specific verb (rewrite) plus a precisely scoped resource (an OpenAI Chat/Responses or Anthropic Messages request body) and the intended effect (making the static prefix provider-cacheable). It also names the concrete mechanisms (cache_control breakpoints, prompt_cache_key), which no sibling tool does, so an agent can distinguish it immediately from the token-accounting 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 the situation (you have a request body with an uncached static prefix) but never states when to call this versus audit_repo_token_waste or calculate_model_arbitrage, nor any preconditions such as needing a valid, complete body. Usage is inferable rather than explicit.

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 well: it discloses that this is a write operation, that the key is returned only once (a critical, non-recoverable behavior), and the exact set of values returned including provider base URLs. It does not address idempotency, name collisions, or key revocation/rotation, which keeps 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?

Two tight sentences, front-loaded on the action and followed by the return payload, with no filler. The phrase 'with no form' is slightly cryptic but conveys that no UI/input flow is required, so it earns its place.

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 the description compensates by enumerating the return payload (key, base URLs, dashboard link, badge URL) plus the billing posture. Remaining gaps are edge cases — whether the key is retrievable later and behavior on duplicate names — that are minor for a one-parameter provisioning tool.

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 single 'name' parameter is fully documented in the schema with its default, so baseline 3 applies. The description adds nothing about naming semantics (e.g., uniqueness, whether the name affects the returned URLs), so it does not exceed 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?

States a concrete verb and resource ('Create a TokenZero workspace and key') and even enumerates the artifacts produced, so the agent knows exactly what the call yields. Sibling tools (audit_repo_token_waste, workspace_metrics, etc.) are clearly different operations, though the description does not explicitly contrast itself with them.

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?

'with no form' and 'Free up to the monthly free tier; nothing is billed' imply this is the frictionless, zero-cost way to bootstrap a workspace, which is useful usage context. However, there is no explicit when-to-use/when-not statement or mention of prerequisites such as whether a workspace must already exist for the sibling metrics/audit 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 full burden, and it does well: the word 'Live' and the scorecard framing make clear this is a read-only observational call, and it discloses the auth requirement plus the demo/sample mode and that the sample is labelled. It stops short of stating rate limits, caching, or whether 'live' data is real-time or periodically refreshed.

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 sentences, front-loaded with the resource and its contents, followed by the call prerequisites. The middle enumeration is long but each item is a distinct reported metric, so it earns its space.

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 output schema, the description compensates by enumerating what the scorecard returns, which is exactly what an agent needs to decide whether to call it. Remaining gaps are the default window and the tension between 'Needs the workspace key' and the schema declaring zero required parameters, which an agent must resolve on its own.

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%, so the schema already documents key and demo; the description mainly restates them, adding only 'tz_live_…' format detail and that the demo output is a labelled sample. The 'window' parameter, whose enum values (24h/7d/30d/all) have no schema description, is only implicitly referenced by 'per window' with no explanation of default or the difference between 'all' and '30d'.

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 names a specific resource (a live value scorecard for a TokenZero workspace) and enumerates the contents: savings, ROI, engine contribution, speed, parity, availability, privacy posture, per-model breakdown and recommendations. That makes the tool's identity unmistakable, though it never explicitly contrasts itself with siblings like audit_repo_token_waste or calculate_model_arbitrage.

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 states the authentication requirement ('Needs the workspace key (tz_live_…)') and gives an escape hatch ('pass demo: true for a labelled sample'), which is useful call-time guidance. However, it offers no when-to-use/when-not guidance relative to the sibling tools, so the choice between this and audit_repo_token_waste or calculate_model_arbitrage is left to inference.

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