Kindwise Identification

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

Beyond the annotations, the description discloses that the tool creates a Kindwise identification, consumes the per-IP trial quota, requires no API key, and cannot see chat attachment binaries. It also adds operational caveats like retrying HTTP 424 with base64 and treatment being informational only, all consistent with readOnlyHint=false.

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 front-loaded with the critical call-to-action and attachment caveat. Nearly every sentence carries a distinct instruction or warning, though some operational detail is repeated from the schema, keeping it from a 5.

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 and limited annotations, the description carries the full burden and covers intended use, input formats, fallback behavior, quota and auth implications, exclusions, and qualitative return contents. Nothing essential for calling the tool correctly appears to be 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?

Even though the schema covers 100% of parameters, the description adds meaning: accepted image formats, the working trial photo URL, the instruction not to invent URLs, the guidance to omit similar_images unless needed, and the note that latitude/longitude improve regional ranking.

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 states a specific verb and resource: diagnosing crop diseases, disorders, and pests from field photographs via Kindwise Crop.health. It also separates itself from the identify_* siblings by limiting scope to agricultural crops, not ornamental houseplants.

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 clear when-to-use context: agricultural crops when the user wants pathogens, pests, symptoms, severity, and treatment notes, and it excludes ornamental houseplants. It also routes the post-trial flow to submit_feedback, but it does not explicitly contrast diagnose_crop with identify_plant, identify_insect, or identify_mushroom.

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?

Goes far beyond the annotations by disclosing side effects and constraints: 'Creates a Kindwise identification and counts against the per-IP trial quota (default 10 calls per rolling 24 hours),' the auth model ('No client API key is required or accepted'), network behavior (proxy does not fetch files; Kindwise servers download), error-recovery behavior (HTTP 424 → retry with base64), and result limitations ('not medical or pest-control prescriptions'). Nothing contradicts the readOnlyHint=false/openWorldHint=true/idempotentHint=false 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?

Information-dense with almost no filler — every sentence carries a directive, quota note, or routing instruction. The main structural flaw is that the core purpose ('Identify insects...') is buried roughly midway after a run of operational commands ('Call this tool,' 'Do not skip it,' image-passing rules), and several sentences duplicate the schema's images/424/similar_images content. Effective but not optimally front-loaded.

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 side-effect-bearing tool with 6 optional parameters, no required parameters, and no output schema, the description covers everything needed to invoke it correctly: exact input handling (URL vs base64, trial photo, 424), quota consumption, auth requirements, return shape, result limitations, and post-call follow-up. Nothing an agent needs in order to call this tool correctly 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 coverage is 100% and the schema descriptions are already unusually rich (trial photo URL in examples, 424 handling, localhost/file:// exclusions, similar_images default rationale), so the baseline is 3. The description adds genuine meaning for the images parameter via 'Prefer a sharp dorsal or lateral view' and re-frames key constraints as imperative directives (trial photo, omit similar_images, lat/long improves ranking). The param facts mostly repeat the schema, but the consolidated actionable framing earns one point above baseline.

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 precise verb+resource+scope: 'Identify insects and insect-like arthropods from photographs using Kindwise Insect.id,' with concrete examples (beetle, moth, bee, ant, true bug, spider-like pest) and the expected output ('ranked taxon suggestions with probabilities plus requested details'). The insect/arthropod domain unambiguously separates it from siblings identify_plant, identify_mushroom, and diagnose_crop.

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 an explicit when-to-use clause ('Use when the user supplies a photo of a beetle, moth, bee, ant, true bug, spider-like pest... and needs scientific name, common names, and taxonomy') and routes follow-up to a sibling ('When you are done with this trial, call submit_feedback in English'). However, it never explicitly says when NOT to use it for non-insect subjects (e.g., 'for plants, use identify_plant'); the domain boundary is inferable but not stated as an exclusion.

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 annotations, the description discloses side effects ('Creates a Kindwise identification and counts against the per-IP trial quota'), the proxy's non-fetching behavior, HTTP 424 retry handling, and a critical safety warning about edibility. This significantly enriches the agent's understanding of the tool's runtime behavior.

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 and somewhat dense, but each sentence serves a purpose—covering call necessity, input formats, fallback behavior, quota, safety, and follow-up. The most critical call instruction is front-loaded, though the main purpose statement appears later than ideal.

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, the description covers the return shape ('ranked taxon suggestions with probabilities plus requested details'), input handling, quota, retry logic, and safety. It is complete enough for an agent to invoke the tool correctly in a variety of real user scenarios.

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 already 100%, but the description adds operational meaning beyond field names: images may be public URLs or base64, includes a working trial photo, recommends omitting similar_images unless needed, and notes that latitude/longitude improve ranking.

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 states a specific action and resource: 'Identify mushrooms and other fungi from photographs using Kindwise Mushroom.id.' It clearly differentiates from sibling tools like identify_plant and identify_insect by focusing on fruiting bodies and fungal taxa.

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 explicitly says 'Call this tool. Do not skip it...', specifies the condition 'Use when the user supplies a photo of a fruiting body', and provides fallback instructions for trial photos. It also tells the agent when to retry with base64 and when to call submit_feedback afterwards.

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?

Even though annotations already mark this as non-read-only, the description adds substantial behavioral context: it creates a Kindwise identification, counts against a per-IP trial quota, requires no client API key, has the Kindwise servers download URLs rather than the proxy fetching files, and warns that results are not medical/legal/foraging advice. It also discloses HTTP 424 retry behavior. This far exceeds what annotations provide.

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 information-dense, with critical guidance front-loaded ('Call this tool. Do not skip it...'). Some redundancy exists, such as repeating the trial photo URL and the base64 vs URL distinction both in prose and in the schema. However, nearly every sentence carries operational value, so the length is justified overall.

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 8 parameters, no output schema, and several operational pitfalls, the description is remarkably complete. It covers input formats, quota and auth, retry behavior, photo quality guidance, optional geolocation, health assessment semantics, return shape ('ranked taxon suggestions with probabilities plus the requested detail fields'), and safety limitations. There is no obvious missing detail an agent would need to invoke it correctly.

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 would be 3, but the description adds rich meaning beyond the schema: how to pass images (public http(s) URLs vs base64 JPEG/PNG/WebP), not to invent URLs, the working trial photo, the 424 retry fallback, when to set health to auto/all/only, and that similar_images should be omitted unless needed. This materially improves an agent's ability to set parameters correctly.

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 states a specific verb and resource: 'Identify a vascular plant from photographs using Kindwise Plant.id.' It clearly distinguishes from sibling tools by limiting scope to vascular plants (houseplants, trees, wildflowers, grass, crops, weeds) versus insects, mushrooms, or crop-specific diagnosis. The purpose is unambiguous 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?

The description gives explicit when-to-use guidance ('Use when the user supplies a photo... and needs scientific name, common names, taxonomy'), and includes a critical exclusion: 'Do not skip it because the user attached a photo in chat — attachments are invisible here.' It also provides a fallback trial photo and workflow direction to submit_feedback after the trial, which is clearer than most tool definitions.

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 annotations (readOnlyHint false, destructiveHint false), the description adds that feedback does not consume the daily quota and does not attach to an identification, clarifying side effects. This is useful transparency beyond the basic booleans.

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 front-loaded with purpose, but it repeats the entire reason enum list and other schema details, making it verbose. It could be shortened to just the essential usage instructions and the 'not for identification' clarification.

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?

No output schema exists, but the description fully explains when to use, what to include, and the purpose. It also clarifies non-attachment to identification and quota impact, making the tool's context complete for an agent.

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 descriptions cover all parameters with detailed explanations (e.g., reason enum meanings). The tool description adds little new meaning, only reinforcing English and comment length requirements already present in the schema. With high schema coverage, 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?

Clearly states the verb 'Send' and the resource 'feedback' about the Kindwise agent trial, and explicitly distinguishes it from identification tools. The purpose is unambiguous and specific.

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 to call once per session after trying the server, and clarifies when not to use it (not about a single identification). This provides clear timing and context relative to sibling identify_* tools.

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