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search_chembl_target

Read-only

Resolve a protein, receptor, or enzyme to its ChEMBL ID using UniProt accession, gene symbol, or exact protein name. Returns organism and target type for disambiguation.

Instructions

Resolve a biological TARGET (protein/receptor/enzyme) to a ChEMBL ID.

⚠️ DO NOT use this tool to look up drugs, compounds, or molecules by name. For drug/compound/molecule names (e.g., "sorafenib", "imatinib", "aspirin"), use search_chembl_molecule instead.

Resolution is deterministic SPARQL against the ChEMBL RDF graph, not a lexical search — there is no ranking to second-guess: • UNIPROT ACCESSION (e.g. "P00533") → the structured skos:exactMatch link. Returns every target containing that protein (the single protein plus any complex/family/chimera it participates in) — filter target_type to get just one. • GENE SYMBOL / PROTEIN NAME (e.g. "EGFR", "epidermal growth factor receptor") → EXACT (case-insensitive) match, tried against BOTH the target's own name and its protein component's skos:altLabel synonyms. • If that finds nothing, ONE substring pass over target names runs as a fallback (e.g. "dehydrogenase"). Still never fuzzy — fix typos.

Every result carries organism and type, so a symbol shared across species or complexes is disambiguated by inspecting those fields (or by passing the organism/target_type filters) — NOT by trusting order.

Target-type values (for type and the target_type filter): SINGLE PROTEIN, PROTEIN COMPLEX, PROTEIN FAMILY, PROTEIN-PROTEIN INTERACTION, CHIMERIC PROTEIN, NUCLEIC-ACID, CELL-LINE, TISSUE, ORGANISM, SELECTIVITY GROUP, SMALL MOLECULE, OLIGOSACCHARIDE, LIPID, METAL, and other rarer kinds. An unrecognized target_type raises rather than silently matching nothing.

The search string can be passed as any of: query (canonical), search, term, keyword, keywords, search_term, or name.

RETURNS a dict {'total_count', 'has_more', 'results', 'match_mode'}. total_count is rows RETURNED (capped by limit), not the full match count; has_more is true if more exist beyond this page. Each result has 'chembl_id', 'name' (rdfs:label), 'organism', and 'type'. match_mode is 'exact', 'substring', or 'none' — 'substring' means the exact pass found nothing and these are looser, UNRANKED matches, so verify 'name' before using them; 'none' means both passes ran and neither matched.

An EMPTY 'results' additionally carries 'hint'. Read it: an empty result is NOT an endpoint failure, and must not be reported as one. On a real endpoint failure this tool does NOT raise — it returns a dict with a single 'error' key instead; CHECK FOR 'error' BEFORE READING 'results'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
termNo
limitNoMax results. Defaults to 20.
queryNoUniProt accession (preferred), gene symbol, or exact protein name. Examples: "P00533", "EGFR", "Thrombin".
searchNo
keywordNo
keywordsNo
organismNoCase-insensitive substring filter on organism, e.g. "Homo sapiens". Applied inside the query.
search_termNo
target_typeNoExact (case-insensitive) filter on target type, e.g. "SINGLE PROTEIN" — collapses an accession/symbol match to the canonical single protein and drops complexes/families.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations declare readOnlyHint and openWorldHint, but the description adds substantial behavioral detail beyond them: deterministic SPARQL with no ranking, exact versus substring match modes, unrecognized target_type raising, empty results carrying a hint, and endpoint failures returning an 'error' dict without raising. No contradiction with annotations exists.

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 long but highly structured: a clear purpose line, warning callout, bulleted resolution rules, filter guidance, return dict explanation, and failure-mode instructions. Every section adds necessary operational context, and the most critical caveats are front-loaded.

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's complexity—10 parameters, multiple search-string aliases, fallback matching, result disambiguation, and non-raising failure modes—the description is exceptionally complete. It explains return structure, match_mode semantics, empty-result hints, and error checking, leaving little ambiguity for an agent.

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

Parameters5/5

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

Schema coverage is only 40%, so the description must compensate, and it does thoroughly. It defines the canonical `query` parameter (UniProt accession, gene symbol, or exact protein name), lists all accepted alias parameters, explains `target_type` accepted values, and clarifies `limit` affects returned rows. This goes well beyond the sparse input schema.

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 opens with a specific verb and resource: 'Resolve a biological TARGET (protein/receptor/enzyme) to a ChEMBL ID.' It further distinguishes itself from siblings by explicitly warning not to use it for drugs/compounds and directing users to `search_chembl_molecule` instead.

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?

The description provides explicit when-to-use and when-not-to-use guidance: 'DO NOT use this tool to look up drugs, compounds, or molecules by name... use search_chembl_molecule instead.' It also explains deterministic matching behavior, fallback substring mode, result disambiguation, and filtering via organism/target_type.

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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