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search_chembl_target

Resolve biological targets to ChEMBL IDs using exact match on UniProt accession, gene symbol, or protein name. Filter results by organism or target type for specificity.

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 on the target component's skos:altLabel synonyms. Not fuzzy/substring — fix typos before calling.

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'}. 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'. On 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?

With no annotations, the description fully discloses behavior: deterministic SPARQL query, exact matching rules, filter behavior (unrecognized target_type raises), pagination details (total_count capped by limit, has_more), and error handling (returns error dict). This is comprehensive.

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 well-structured with bullet points, warnings, and clear sections. It front-loads the main purpose and critical caveat. Every sentence adds value without redundancy or fluff.

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, no annotations, but output schema exists), the description covers all necessary aspects: purpose, input types, behavior of filters, error handling, pagination, and return format. It is thorough and leaves no gaps.

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 40% (only 4 of 10 params have descriptions). The description compensates by listing all equivalent parameter names (query, search, term, etc.) and explaining their interchangeability. It also elaborates on the effect of organism and target_type filters, adding meaning beyond the 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 clearly states the tool resolves biological targets to ChEMBL IDs, with a specific verb-resource pair. It distinguishes from sibling tools by explicitly warning against looking up drugs/compounds and directing to search_chembl_molecule.

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 guidance (protein/receptor/enzyme targets and when to use alternatives for drugs/compounds. It details input types (UniProt accession, gene symbol, protein name) and explains disambiguation via organism/target_type. It also warns against fuzzy matching for names.

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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