Persimmon

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

With no annotations provided, the description carries the full burden of disclosure. It reveals important behaviors: filtering out signals without a citable source URL, and inclusion of a coverage_status field. However, it does not mention authorization requirements, rate limits, or whether the operation is read-only (though it's clearly a read). Overall, it provides good behavioral context beyond a mere operation statement.

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, front-loaded with the core purpose. It is concise without unnecessary words. Every sentence adds value: purpose, filtering behavior, and coverage_status note. Could be slightly more compact but is well-structured.

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 3 parameters, no output schema, and no annotations, the description adequately covers the tool's behavior, including the filtering logic and response field. It does not specify the time range for 'recent' or pagination details, but for a simple retrieval tool, it is sufficiently complete.

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 input schema already documents all three parameters (brand, limit, tier_floor) with their types, defaults, and constraints. The description adds no additional parameter-specific meaning beyond what the schema provides. Baseline of 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?

The description clearly states what the tool does: 'Return recent signals tracked for a brand'. It specifies the data source (Persimmon's public-source harvest substrate), a filtering behavior (signals without citable source URL are removed), and that the response always includes a coverage_status. This is sufficient to distinguish it from sibling tools like 'get_competitive_context' and 'get_momentum_index'.

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 does not provide explicit guidance on when to use this tool versus its siblings. It describes the tool's behavior but lacks scenarios, prerequisites, or exclusions. The context of retrieval is implied but not contrasted with alternatives.

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 must carry the full burden of behavioral disclosure. It describes the return items and exclusion of scaffold tier, but lacks details on side effects, authentication needs, rate limits, or error conditions. The description is adequate but not thorough.

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 consists of two concise sentences that are front-loaded with the main action. Every sentence adds value, and there is no redundancy or fluff.

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 (2 params, no output schema, no annotations), the description provides sufficient information about what is returned and what is excluded. However, it could be more explicit about the default behavior of the limit parameter and the structure of the response, potentially improving completeness.

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 has 100% description coverage, so the schema already documents both parameters (brand and limit). The description adds no new meaning beyond what is in the schema; it only restates the context. Baseline score of 3 applies.

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 returns competitor list, peer-momentum snapshots, and a competitive-position one-liner for a brand, which is a specific verb+resource. It also mentions excluding scaffold-tier peers, adding clarity. The purpose is distinct from sibling tools that focus on signals or momentum index.

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 no guidance on when to use this tool versus its siblings (get_brand_signals, get_momentum_index). It does not mention alternatives, prerequisites, or context for selection, leaving the agent without enough information to choose appropriately.

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 description carries full burden. It mentions 'deterministic' and 'No LLM calls', which are positive behavioral traits, but lacks details on data freshness, caching, or authorization requirements. For a read operation, transparency is adequate but not comprehensive.

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 concise sentences: first states the core operation, second lists what's included and a key note. Every sentence earns its place with no fluff.

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 no output schema, description lists all return components (composite, four dimensions, tier, signal count, timestamp), which is good. Could be more complete about output format or ordering, but for the complexity (two optional params) it suffices.

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?

Input schema has 100% description coverage, but the description reinforces the meaning of 'brand' (omission gets full index) and 'limit' (capping defaults). This adds context 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?

Description clearly states it returns a Persimmon momentum score for one brand or the full index, listing all components (composite, four dimensions, tier band, signal count, timestamp) and adds a key differentiator: no LLM calls. This is specific and distinguishes from sibling tools which are for brand signals and competitive context.

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?

Description explains when to use: for one brand or full index, and provides guidance on omitting brand for full index with capping. However, it does not explicitly compare with siblings or state when not to use this tool.

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