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chembl-mcp-server

by io.github.cyanheads

chembl-search-targets

chembl_search_targets
Read-onlyIdempotent

Resolve a protein/gene/UniProt accession to the ChEMBL target ID that chembl_get_bioactivities needs for the target→leads workflow. Supply at least one of accession (UniProt, e.g. P00533), gene_symbol (e.g. EGFR), or query (free-text name); filter further by organism and target_type. Returns each target with its type, organism, and component UniProt accessions + gene symbols. A UniProt accession from the uniprot/protein server is the most precise input. A capped result carries nextCursor — pass it back as cursor with the same filters to read the next page.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum targets to return. Defaults to the server default (25) when omitted.
queryNoFree-text name match against the target preferred name, e.g. "kinase" or "growth factor receptor".
cursorNoOpaque continuation token from a previous call's nextCursor — resumes where that page ended. Omit for the first page. Re-send the same accession/gene_symbol/query/filters that minted it (only limit may change; it sets this page's size); redeeming it against different filters walks a different result set.
organismNoRestrict to a source organism, e.g. "Homo sapiens" (case-insensitive exact match).
accessionNoUniProt accession of a target component, e.g. "P00533". The most precise resolver — from the uniprot/protein server.
gene_symbolNoGene symbol of a target component, e.g. "EGFR" (case-insensitive exact match).
target_typeNoRestrict to a target class, e.g. "SINGLE PROTEIN" or "PROTEIN COMPLEX".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
capNoThe limit that was applied.
errorNoPresent when the call failed. Absent on success.
shownNoNumber of targets returned.
noticeNoGuidance when no target matched — echoes the filters and suggests how to broaden.
targetsNoMatching targets (up to the limit).
truncatedNoTrue when the result was capped at the limit.
nextCursorNoOpaque token for the next page — pass it back as cursor with the same filters. Absent when this page is the last one.
totalCountNoTotal targets matching the filters before the limit was applied.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and idempotentHint=true, covering safety and non-destructiveness. The description adds value by detailing the return payload (each target's type, organism, component UniProt accessions, and gene symbols) and the pagination behavior (nextCursor, pass back with same filters). This goes beyond annotations without contradicting them.

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

Conciseness5/5

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

The description is compact—three sentences—yet delivers the purpose, required inputs, optional filters, return structure, and pagination guidance. It is front-loaded with the core purpose and workflow link, and every sentence contributes. No fluff or repetition of schema details.

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?

For a 7-parameter search/resolution tool with no required parameters, the description is complete. It covers input options, precision ranking, filtering, return fields, and pagination. The existing output schema absorbs the need to describe return values in detail, and annotations already indicate safe read-only idempotent behavior. Nothing an agent needs to invoke it correctly is missing.

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?

Schema description coverage is 100%, so each parameter is already documented. The description adds meaningful nuance beyond the schema: it flags accession as the most precise resolver (from the uniprot/protein server), clarifies that gene_symbol and organism are case-insensitive exact matches, and explains the cursor semantics in detail (only limit may change when redeeming). This enriches the schema's bare parameter definitions.

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 explicitly states the verb 'Resolve' and the resource (protein/gene/UniProt to ChEMBL target ID), and ties it to a specific downstream workflow (needed by chembl_get_bioactivities). This clearly distinguishes it from sibling search tools like chembl_search_molecules or chembl_get_drug_info, which serve different resolution needs.

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 gives clear guidance on when to use this tool (to resolve an identifier before the bioactivities step) and specifies that at least one of accession/gene_symbol/query is required, with a hint that UniProt accession is the most precise. It also explains pagination via cursor. However, it does not explicitly name alternatives or state when NOT to use this tool in favor of a sibling, so it's slightly shy of a 5.

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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