CovaSyn Chemistry Tools

covasolve_predict

SOLUBILITY PREDICTION — gradient-boosted model (ensemble_v12) trained on 115,862 experimental solubility rows (BigSolDB and other sources).

**Always use this tool when the user asks about solubility, log S, dissolution,
or "how much X dissolves in Y".** Much more accurate than the heuristic logS
in `covachem_profile` and explicitly accounts for the solvent (covachem only
gives intrinsic water-logS).

Returns: log S (mol/L), mg/mL, g/L, mole fraction (dilute approximation with the
solvent's real density; null with a warning when the solvent's density is not in
the solvent table), applicability-domain verdict, warnings, and model_version.

Uncertainty: use `conformal_interval_90` — a calibrated 90 % interval on log S
(measured coverage about 0.90 on held-out data). With v12, `confidence` and
`confidence_interval_95` are null: the model's seed replicates do not give a
meaningful spread, so no such number is reported.

Solvent SMILES examples:
  water = "O"
  ethanol = "CCO"
  methanol = "CO"
  DMSO = "CS(C)=O"
  acetone = "CC(C)=O"
  ethyl acetate = "CCOC(C)=O"

Cost: 5 credits, available on Free tier.

Input Schema

Name	Required	Default
`api_key`	No
`solute_smiles`	Yes
`temperature_k`	No
`solvent_smiles`	No	O

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 9, 2026

TDQS

A4.5/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so: it enumerates returned quantities, discloses the density-table limitation and the resulting null-plus-warning behavior, explains why confidence/confidence_interval_95 are null for v12, and states cost (5 credits) and tier availability. These are exactly the non-obvious behaviors an agent needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the one-line purpose and the routing rule, then organized into clearly labeled blocks (Returns, Uncertainty, Solvent SMILES, Cost). The solvent example list is the only stretch, but it earns its place by preventing SMILES formatting mistakes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, yet the description still usefully characterizes the return payload and its edge cases (null mole fraction when density is missing, null confidence on v12), plus cost and applicability-domain verdict. The remaining gap is the unaddressed temperature and api_key parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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. It does well for solvent_smiles by supplying concrete SMILES examples (water "O", DMSO "CS(C)=O"), which prevents a common formatting error. However, temperature_k, api_key, and the solute_smiles input are never addressed, leaving three of four parameters with no semantic guidance anywhere.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific verb+resource (solubility prediction), identifies the underlying model (gradient-boosted ensemble_v12) and its training corpus, and explicitly distinguishes itself from the heuristic logS in covachem_profile. An agent can route to it without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

States the trigger condition plainly ("Always use this tool when the user asks about solubility, log S, dissolution, or 'how much X dissolves in Y'") and names both the alternative (covachem_profile) and the companion tool for uncertainty (conformal_interval_90). Explicit when-to-use and when-to-prefer-something-else.

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

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