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chembl-mcp-server

by io.github.cyanheads

chembl-get-assay

chembl_get_assay
Read-onlyIdempotent

Assay provenance behind a bioactivity row: description, type (binding / functional / ADMET / toxicity), the target it measures, organism, and ChEMBL's 1–9 confidence score (9 = direct assay on the protein target, lower = homologous or indirect). Supply assay_chembl_id from a chembl_get_bioactivities row. Call this to judge whether two measurements are comparable before ranking them together.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assay_chembl_idYesChEMBL assay ID from a bioactivity row's assay_chembl_id, e.g. "CHEMBL674637".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
organismNoAssay organism. Null when unspecified.
assay_typeNoAssay type code: B=binding, F=functional, A=ADMET, T=toxicity, P=physicochemical, U=unclassified. Null when absent.
descriptionNoAssay description text. Null when absent.
assay_chembl_idNoThe ChEMBL assay ID queried.
confidence_scoreNoChEMBL confidence score, 1–9 (9 = direct single-protein assay; lower = homologous/indirect). Null when unscored.
target_chembl_idNoChEMBL target ID the assay measures — chain to chembl_search_targets/chembl_get_bioactivities. Null when unassigned.
assay_type_descriptionNoHuman-readable assay type, e.g. "Binding". Null when absent.
confidence_descriptionNoHuman-readable confidence description, e.g. "Direct single protein target assigned". Null when absent.

TDQS

A4.4/5.0
Behavior4/5

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

The annotations already declare readOnlyHint, openWorldHint, and idempotentHint, which cover safety and idempotency. The description adds behavioral context beyond annotations by explaining the significance of the confidence score (9 = direct assay, lower = homologous/indirect) and how the result maps to data provenance. This helps the agent interpret the output correctly without contradicting annotations.

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?

The description is a single, well-organized sentence that front-loads the core purpose, then provides necessary details. It avoids fluff and each clause adds value. It could arguably be split into two sentences for readability, but it remains concise and structured.

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

Completeness5/5

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

Given the tool has only one parameter, an output schema (which likely lists the returned fields), and the description covers the key semantic details (confidence interpretation, comparability use case), the definition is fully adequate. An agent can correctly invoke and interpret the tool without ambiguity.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents the parameter with an example, but the description reinforces the source of the ID ('from a chembl_get_bioactivities row'), adding practical guidance on how to obtain the value. With 100% schema coverage, this additional contextualization elevates the score above the baseline.

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?

The description clearly states the tool's function: it provides assay provenance (description, type, target, organism, confidence score) for a bioactivity row. It uses a specific verb ('get') on a specific resource ('assay'), and distinguishes itself from siblings by referencing the source (bioactivity row) and the purpose (judging comparability).

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

Usage Guidelines4/5

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

The description explicitly instructs to supply assay_chembl_id from a chembl_get_bioactivities row and gives a concrete use case ('to judge whether two measurements are comparable before ranking them together'). While it does not explicitly state when not to use it, the guidance is clear and sufficient for an agent to decide. It does not name alternative tools, but the context implies this is the sole route for assay provenance.

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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool targets a distinct resource or action: search for molecules and targets, fetch bioactivities, assays, and drug info, plus two dedicated dataframe helpers for analysis. There is no functional overlap—even the two dataframe tools are clearly separated as describe vs query. Agents can easily select the right tool for each step in a workflow.

Naming Consistency5/5

All tools follow a consistent chembl_verb_noun pattern using snake_case (e.g., chembl_search_molecules, chembl_get_bioactivities, chembl_dataframe_query). The naming is uniform and predictable, with the verb always preceding the object. Minor deviation like dataframe_describe vs chembl_get_* still fits the same prefix and style, making it highly consistent.

Tool Count5/5

With 7 tools, the server is well-scoped for its domain of compound-target bioactivity discovery and analysis. Each tool serves a clear purpose without redundancy, covering search, retrieval, provenance, pharmacology, and data manipulation. This is a textbook example of a focused toolset where every tool earns its place.

Completeness5/5

The tool surface covers the full read-only lifecycle: discover compounds and targets, fetch bioactivity measurements, drill into assay provenance, retrieve drug pharmacology, and perform aggregate analysis via SQL. All major workflows (target deconvolution, lead finding, selectivity assessment, and comparative ranking) are supported without dead ends. The only possible gap is write operations, but they are clearly out of scope for a reference database.

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