Patina

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

The description is unusually rich: it documents return fields (`pass`, `reason`, `found`), explains missing-profile semantics, and clarifies that Patina does not prove uniqueness/liveness. However, this contradicts the listed annotation `openWorldHint: false`. The description explicitly instructs that an absent profile be treated as UNKNOWN rather than false, which is open-world behavior, while the annotation tells the opposite global assumption. Per the instructions, a direct contradiction forces a score of 1.

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 longer than the typical tool note, but it is front-loaded with the core task and invocation requirements before the background on score bands and inputs. Some parts, like the exact point weights for Age, Continuity, Corroboration, etc., are arguably beyond what an AI agent needs to call the tool, making it less compact than a top-tier definition.

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, but the description still tells the agent exactly what to expect (`pass`, `reason`, and `found`), how to handle the missing-profile case, and when Patina is not the right source. It leaves no ambiguity for correct invocation or interpretation of results.

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 schema already documents all three parameters with clear descriptions (100% coverage). The description adds important non-schema context: that at least one of min_score or min_years must be provided, that the score is an indicator of provable history rather than popularity, and the meaning of the score bands. This extra context transcends the raw parameter list.

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 immediately states a concrete verb and resource: 'Decide whether somebody clears a trust bar, as a plain true or false.' It also clearly distinguishes this from a numeric lookup by explaining the boolean pass/fail return, making it easy to tell apart from get_patina_score and similar 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?

Explicitly says 'This is the right tool for gating decisions' and gives concrete examples like 'has this person been verifiably around for at least five years'. It also provides a clear when-not: Patina is wrong for uniqueness/liveness/personhood checks, which helps the agent route to another signal if needed.

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?

Annotations already state the tool is read-only, idempotent, and non-destructive. The description adds rich behavioral context beyond that: the unknown-username behavior (found: false is a normal answer, not an error), the attestation details, the scoring meaning, and the explicit limitations (does NOT prove uniqueness or liveness). No contradiction with annotations.

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 long but well front-loaded: purpose first, then score interpretation, components, limitations, and routing. Each section adds value for an agent deciding whether to call the tool and how to interpret the result. Some of the detailed component weightings could be compressed without loss of core usability, so it is concise overall but not maximally tight.

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 one parameter, no output schema, and a clear annotation set, the description covers everything an agent needs: what the score means, how it is calculated, when to use this instead of check_threshold, and how to handle the not-found case. It also discloses limitations. Nothing important is missing.

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 the single parameter, including the exact URL pattern patinadata.xyz/u/<username>. The description merely says 'by their Patina username' and adds no further syntax or format details, so the schema already carries this weight. 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: 'Look up a person's full Patina score by their Patina username.' It also clearly delineates the tool from sibling check_threshold by describing what 'the whole picture' includes and pointing to the sibling for y/n checks. The distinction is explicit and actionable.

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?

Provides explicit when-to-use guidance: 'Call this when you want the whole picture...' and explicit when-not-to-use guidance: 'If you only need a yes or no about whether somebody clears a bar, call check_threshold instead.' It also warns against using Patina as a proof-of-personhood signal. This is excellent usage guidance.

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?

Beyond the readOnlyHint and idempotent hints, the description details behavioral traits: it never returns the Patina username, never reveals connected platforms (to avoid de-anonymization), refuses emails, and provides a full interpretation of score bands. This goes far beyond what annotations convey and is fully consistent with them. It also explains why certain platforms are excluded from lookup, giving honest constraints.

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 long (200+ words) but well-structured: it front-loads the core purpose and constraints, then explains the scoring interpretation and weightings. Every section serves a purpose for correct usage—the score bands and input weights are essential for interpreting results. While it could be more concise, the length is justified given the tool's complexity, and it maintains clear topical separation.

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 is fully self-contained for an agent to call this tool correctly: it names the expected inputs (source, handle with format examples), the exact output structure (three things: linked profile, score, years), and the full meaning of the score. No output schema exists, so this is the sole source of return-value expectation, and it provides enough. It also covers edge cases like unsupported sources and email refusals, making it impossible for an agent to be surprised.

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 the baseline is 3, but the description adds significant meaning: it clarifies that handles can be a bare handle, @handle, or full URL (though the schema already shows this), and it deeply explains the score meaning and weighting factors (Age, Continuity, etc.) that are not in the schema. This enriches parameter understanding for the agent, even if it doesn't add new facts about the source parameter beyond what the schema states.

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 opens with a clear, specific purpose: resolve a platform handle to a Patina score without revealing the person's identity. It names the supported sources (github, instagram, linkedin) and states precisely what it returns (profile link existence, score, years of history). This distinguishes it from sibling tools like get_patina_score or verify_attestation, which serve different functions, even without naming 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?

The description provides strong usage boundaries: it lists supported platforms, explicitly states unsupported ones (youtube, spotify, amazon, uber) and why, refuses email addresses, and warns against asking for data it cannot return. However, it does not explicitly mention alternative sibling tools or when to prefer them, leaving some routing to inference. It clearly says 'do not ask it for those; it cannot provide them,' which guides agent behavior well.

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?

The description discloses that the tool returns the recovered address rather than a bare boolean, enabling independent comparison, and explains why that design exists. It also reveals expiration semantics: a genuine signature past expiresAt means the score was valid when issued and should be refetched, not that it was forged. Annotations already mark readOnly and idempotent; the description adds meaningful behavioral context without contradicting them.

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 longer than typical, but every sentence earns its place: core purpose, return value, why to avoid the tool, offline alternatives, and expiration caveat. It is front-loaded with the primary check and scoping, and the warning is presented as 'IMPORTANT, AND THE WHOLE POINT,' making the structure easy to follow. There is no filler or repetition.

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 tool with no output schema, the description covers the return values, the rationale for the return shape, and the expiration edge case, giving an agent enough to decide whether and how to call it. It omits explicit error behavior for malformed signatures or mismatched addresses, but the strong 'do not use this tool' guidance reduces the impact of that gap. Overall it is highly complete for its complexity.

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%, with both message and signature already described precisely ('newlines and all', '0x-prefixed'). The description restates the parameters but adds no new format or syntax details beyond the schema. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

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 opens with a specific verb and resource: 'Check whether a Patina attestation really was signed by Patina.' It clearly scopes the tool to signature verification and distinguishes it from siblings like check_threshold or get_patina_score by stating the return tuple: recovered address, published address, and match. This is unambiguous and immediately tellable apart from other tools.

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?

Explicitly instructs when not to use the tool: 'you do not need this tool, and where you can, you should not use it,' and names offline alternatives (viem's recoverMessageAddress, ethers' verifyMessage). It explains the reasoning—server-side verification reintroduces trust in Patina—giving a clear condition for avoidance. This is strong usage guidance beyond any sibling description.

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