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search_chembl_id_lookup

Read-only

Resolve a name or keyword to ChEMBL IDs across compounds, targets, cell lines, tissues, and assays in one call.

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 (its own rdfs:label OR its component's skos:altLabel — gene symbols and protein names), CELL_LINE and TISSUE (rdfs:label, e.g. "Liver", "CCRF S-180"). Not fuzzy/substring — fix typos before calling, or use search_chembl_target, which falls back to a substring pass. 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). ⚠️ On a default (cross-kind) search, has_more=true can also mean an entire entity_type is missing from the page: the kinds are UNIONed and the limit is applied to the whole, so e.g. "Liver" at limit=5 returns 5 TARGET rows and no TISSUE row, though both exist. Do NOT conclude a kind is absent from a truncated page — raise limit or re-run with entity_type set. 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).

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
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?

Goes far beyond the readOnlyHint and openWorldHint annotations. It explains matching regimes (EXACT vs KEYWORD), pagination behavior (has_more semantics), empty-result hint, and error handling (returns error key instead of raising). It clarifies that total_count is capped by limit, and warns against misinterpreting truncated results. This is exemplary behavioral disclosure.

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?

Although lengthy, the description is well-organized with bullet points, clear sections for matching regimes, return structure, and warnings. It front-loads the core purpose and then systematically adds details. Every sentence carries useful information; no filler. The structure aids readability for a complex tool.

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 (9 params, multiple entity types, intricate matching semantics, detailed return structure), the description is remarkably complete. It covers all necessary aspects: input handling, behavior differences per entity type, pagination nuances, empty results, error handling, and return dict structure. The presence of an output schema doesn't reduce the value of the description's explanations.

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 33%, but the description compensates extensively. It documents the multiple aliases for the query parameter (query, search, term, keyword, keywords, search_term, name) and explains the entity_type values and their matching semantics. It also clarifies the limit's effect on cross-kind results. The description provides far more parameter context than the schema alone.

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 purpose: 'Resolve a name to ChEMBL IDs across several entity kinds in one call.' It explicitly distinguishes itself as a cross-entity convenience wrapper over the ChEMBL RDF graph, and contrasts with siblings like search_chembl_target (substring fallback) and search_chembl_molecule. The verb+resource+scope are specific and unambiguous.

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: when to use entity-specific tools (if you know the kind), when to use search_chembl_target (for fuzzy/substring matching), and when to use this tool (unsure of kind, want cross-kind search). It also details the ASSAY opt-in rationale and warns about implications of the has_more flag on truncated pages. Usage boundaries and alternatives are clearly stated.

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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