ivive.app

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 does disclose that the result includes an observed-CL band and applicability warnings, and warns when results rest on predicted inputs, which is real behavioral context. It omits permissions, units, failure behavior, and how the warnings are surfaced.

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 no filler; the core computation is front-loaded and the conditional instruction follows. Slightly telegraphic, but 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?

For a 9-parameter, no-annotation, no-output-schema domain tool, the description conveys the computation, the key returned artifacts (band, warnings), and a cross-tool caveat. It is largely sufficient, though it could say more about output units and structure since no output schema exists.

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% across 9 parameters, with rich per-field documentation (units, ranges, model semantics, clint_censored behavior). The description adds only a provenance caveat for fu_p/fu_mic, so the schema does the heavy lifting and the baseline 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 names a specific computation (hepatic and plasma clearance via IVIVE) and its inputs (CLint, fu,mic, fu,p), plus what comes back (observed-CL band, applicability warnings). It does not distinguish this tool from siblings like scale_to_human or target_clint, so it stops short of a 5.

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 by the IVIVE framing, and there is one conditional instruction about fu,p/fu,mic provenance from predict_inputs. However, there is no explicit guidance on when to choose this tool over scale_to_human or target_clint, nor any prerequisites or exclusions.

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 the description carries the full behavioral burden. It says nothing about what happens when a range is supplied (how the sweep is applied, what shape of result comes back, whether sensitivity indices or bands are produced), nor any read-only/compute-only assurance; only the paragraph-length description could have covered this.

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 a concrete example and zero filler. Its brevity is a virtue, though for a 12-parameter tool it leaves little structure to hang the remaining context on.

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 12-parameter tool with no annotations and no output schema, the description is thin: it does not describe the result of a sensitivity run, how ranges are combined, or the consequences of correction_id / clint_censored interacting with a sweep. Much of this burden falls on the schema text, leaving the description incomplete.

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 83%, so the schema already documents nearly every parameter (rb, clint, assay_type, correction_id, clint_censored, etc.). The description adds only a motivational gloss on fu_p_range via its example and does not explain the semantics of the range objects or how multiple simultaneous ranges interact.

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-like concept and target: how far plasma CL moves as each input is varied over a range, with a concrete example (predicted fu,p over its 50% range). This clearly distinguishes it from a plain calculation tool like calc_clhep, though it never names the sibling 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?

Usage is only implied: vary an input over a range to see the effect on CL. There is no explicit when-to-use vs calc_clhep, no when-not, and no statement of prerequisites (the schema's correction_id note hints at matching species/assay_type but the description does not route the agent).

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 the description carries the full behavioral burden. It usefully discloses that outputs are predictions rather than measurements and that an applicability verdict should be checked, but it omits failure modes, permissions, output format, and what the verdict actually entails.

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 tightly written sentences with no filler. The core prediction task is front-loaded, and the critical caveat follows immediately.

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 3-parameter tool with no output schema and no annotations, the description names the predicted quantities and adds a key usage caveat. However, it leaves assay_type semantics unexplained and does not describe the return format or the applicability verdict, leaving clear gaps for correct invocation.

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 only 33% (only concentration has a description), so the description should compensate but does not. It only mentions 'from a SMILES' and adds no meaning for assay_type (microsomes vs hepatocytes) or concentration beyond what the schema already provides.

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 ('Predict') and names the exact predicted quantities (fu,p, fu,inc) plus secondary outputs (human CL and Vss) from a SMILES. The purpose is clear and distinguishable from a generic prediction tool, but it does not explicitly contrast with siblings like target_clint or calc_clhep.

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?

No guidance is given on when to choose this tool over alternatives such as target_clint, scale_to_human, or calc_clhep. The caveat 'Predictions, not measurements; check the applicability verdict' implies an interpretation context but does not state conditions for use, exclusions, or sibling comparisons.

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 the description carries the full burden, and it does disclose the meaningful behavior for a computation tool: it returns every applicable scaling method together with its measured error, and it rejects predicted (as opposed to observed) animal CL. It does not state which methods are included, minimum species requirements, or failure modes, 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 dense, front-loaded sentences with no filler; the output guarantee and the input restriction each earn their place. Slightly telegraphic phrasing ('every applicable method with its measured error') costs a little clarity but not size.

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 usefully covers the return shape (all applicable methods plus their error), and the nested parameter schema covers the input structure. The one remaining gap is the role of fu_p_human in enabling fu-corrected methods, though the schema description partially covers it.

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?

With 50% schema description coverage (animals items are well documented via $defs, fu_p_human has a description), the schema already does substantial work. The description adds the species constraint (rat, dog, monkey) and reinforces that 'cl' must be observed, but says nothing about fu_p_human or how binding data selects the fu-corrected methods. 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 precise verb+resource combination (predict human plasma CL) and the exact input class (observed animal in-vivo CL from rat, dog, monkey). The sibling set (calc_clhep, target_clint, similar_drugs, input_sensitivity, predict_inputs) contains no other allometric-scaling tool, so the scope is unambiguous.

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 clause 'Observed animal CL only, never predicted' functions as a negative usage condition for the input data, which is genuinely useful. However, it never says when to prefer this tool over calc_clhep or the other siblings, nor what prerequisites (e.g. species coverage, binding data) make it applicable. Usage is implied rather than stated.

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 the description carries the full behavioral burden. It hints at the return content (similar drugs plus observed vs. IVIVE-predicted clearance) but says nothing about cost, latency, failure modes for invalid SMILES, or whether results are cached — significant gaps for a tool the agent must call blind.

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?

One sentence, front-loaded with the core action and the returned data. The trailing colon clause ('how IVIVE fared on this kind of structure') adds useful framing about intent rather than waste, though the phrasing is slightly informal.

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 small two-parameter tool with no output schema and no annotations, the description conveys the purpose and the gist of the response, which is the minimum viable level. It still omits the `n` parameter's semantics and any indication of result shape or ordering, so it is adequate but not 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 0%, so the description must compensate, and it only implicitly covers the SMILES input. The `n` parameter (count of similar drugs, default 3, max 10) is never mentioned in the description and has no schema description either, leaving its meaning 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?

States a specific operation (retrieve most similar reference drugs for a given SMILES) and names the payload (observed and IVIVE-predicted clearance), which is clearly distinct from the computational siblings like calc_clhep or scale_to_human. It is delivered as a noun phrase rather than a verb, and it never explicitly differentiates itself from siblings, so it falls short of a 5.

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?

There is no when-to-use, when-not-to-use, or alternative guidance. An agent must infer from the name alone that this is the retrieval/comparison step rather than one of the five sibling computation 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?

No annotations are provided, so the description carries the full behavioral burden, yet it says nothing about the underlying model (well-stirred vs parallel-tube), units, assumptions, or whether the result is a pure calculation with no side effects. Only the conceptual relationship to plasma clearance is conveyed.

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 short sentence with no padding, which is efficient, but for a 7-parameter calculation tool it is under-specified rather than genuinely concise. The core idea is front-loaded, so structure is acceptable.

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 7 parameters, 4 required, three enums, low schema coverage, no annotations, and no output schema, the description is far too thin. It omits the required inputs, the model/assay choices, and any hint of return format, leaving substantial gaps for the calling 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 description coverage is only 14% (only target_clp is documented), and the description does not name or explain fu_mic, fu_p, rb, species, model, or assay_type. It references the concept of target plasma clearance but adds no syntax, units, or constraint detail beyond what the schema partially provides.

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 quantity (in-vitro CLint) and the driving input (target plasma clearance), so an agent can tell it computes a derived assay threshold rather than measuring or scaling anything. It does not, however, differentiate itself from siblings like calc_clhep or scale_to_human, which also produce derived PK values.

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?

There is no when-to-use statement, no prerequisite description, and no mention of alternatives such as calc_clhep or scale_to_human. The agent must infer the use case purely from the domain phrasing.

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