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search_chembl_id_lookup

Find ChEMBL IDs by name across compound, target, cell line, and tissue (exact match) or by keyword in assay descriptions.

Instructions

Resolve a name to ChEMBL IDs across several entity kinds in one call.

Cross-entity convenience wrapper over the ChEMBL RDF graph. Two matching regimes, because the entity kinds carry different searchable text:

• EXACT (case-insensitive) NAME match — COMPOUND (skos:altLabel: brands, generics, synonyms), TARGET (component skos:altLabel: gene symbols, protein names), CELL_LINE and TISSUE (rdfs:label, e.g. "Liver", "CCRF S-180"). Not fuzzy/substring — fix typos before calling. Prefer the entity-specific tools (search_chembl_molecule / search_chembl_target) when you know the kind; they carry extra fields (organism/type).

• KEYWORD-IN-DESCRIPTION — ASSAY. Assays have no name; their searchable text is a free-text dcterms:description, so ASSAY does a keyword (token) match on that description, NOT an exact match, e.g. entity_type="ASSAY", query="acetylcholinesterase" → every assay whose description mentions it. ASSAY results are relevance-ranked (best description match first).

Default (no entity_type) searches the four EXACT-name kinds and UNIONs them. ASSAY is opt-in via entity_type="ASSAY" — its keyword semantics and high hit counts would otherwise swamp a name lookup. (DOCUMENT is not supported; query SPARQL directly for it.)

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 the number of rows RETURNED (capped by limit), NOT the full match count; check has_more (true = more results exist beyond this page — relevant mainly for ASSAY, whose keyword search can have many hits). Each result carries 'chembl_id', 'entity_type', and 'organism' (null for COMPOUND / where absent — use it to tell e.g. human from mouse targets). Name kinds also carry 'name' (rdfs:label); ASSAY rows instead carry 'description' (the free-text assay description, name=null) and a relevance 'score' (higher = better match). 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
limitNoThe maximum number of results to return.
queryNoThe query string to search for.
searchNo
keywordNo
keywordsNo
entity_typeNoOptional: COMPOUND, TARGET, CELL_LINE, TISSUE, or ASSAY. Omit to search the four name kinds together. ASSAY (keyword match on the assay description) is opt-in only.
search_termNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations provided, but the description fully discloses two matching regimes, default behavior, ASSAY keyword search, return format including error handling (returns error key instead of raising), total_count capping, and has_more flag. Completely transparent.

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?

Description is long but well-structured with sections and bullet points. Every sentence adds value, though some redundancy could be trimmed (e.g., multiple search string aliases). Still, highly organized and readable.

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 (multiple entity types, two search modes, return format, error handling), the description is comprehensive. It even details the return schema, which is not provided in output schema. Fully complete.

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 description coverage is only 33%, but the description compensates by documenting that any of the multiple string parameters can be used as the search query, and explains the entity_type parameter in detail. Adds significant 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?

Clearly states it resolves a name to ChEMBL IDs across multiple entity kinds. Distinguishes from sibling tools by mentioning entity-specific alternatives like search_chembl_molecule and search_chembl_target, which carry extra fields.

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?

Provides explicit guidance on when to use exact match vs keyword, advises fixing typos before calling, recommends entity-specific tools when entity kind is known, and notes that ASSAY is opt-in with different semantics. Also mentions DOCUMENT not supported.

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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