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chembl-mcp-server

by io.github.cyanheads

chembl-dataframe-query

chembl_dataframe_query
Read-onlyIdempotent

Run a read-only SQL SELECT over the bioactivity rows chembl_get_bioactivities spilled to a canvas — rank, group, dedupe, and aggregate across the FULL set, not the inline preview. Reference each staged table by the name chembl_get_bioactivities returned — bioactivities for its potency_ranked view, bioactivities_null_potency for null_potency; discover the staged tables and their columns with chembl_dataframe_describe. Compute honest aggregates here (e.g. SELECT molecule_chembl_id, MEDIAN(pchembl_value) AS med FROM bioactivities WHERE standard_type = 'IC50' GROUP BY 1 ORDER BY 2 DESC). Two independent bounds apply, each reported on its own field: truncated is true when the SQL result exceeded the canvas row cap, and rendered_rows says how many of the returned rows the markdown table holds once its character budget is reached (below row_count on a wide or long result). Page past either bound with SQL LIMIT/OFFSET — append e.g. LIMIT 500 OFFSET 500 and re-call; offsets reach rows beyond the canvas row cap. Requires CANVAS_PROVIDER_TYPE=duckdb.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYesA read-only SELECT against the staged tables. Reference tables by the names chembl_get_bioactivities returned.
canvas_idYesCanvas ID returned by chembl_get_bioactivities (spilled: true).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsNoResult rows (capped at the canvas row limit). Each row is a column→value map.
errorNoPresent when the call failed. Absent on success.
row_countNoNumber of rows materialized in this response.
truncatedNoTrue when the SQL result exceeded the canvas row cap and was truncated — the engine bounding the result set itself, not the rendering. Independent of rendered_rows; page past it with LIMIT/OFFSET.
rendered_rowsNoHow many of those rows the markdown table in content[] holds. Below row_count when the rendered table reached its character budget — a rendering bound, INDEPENDENT of truncated: a response can be truncated:false and still render fewer rows than row_count. Re-run the same SQL with LIMIT/OFFSET to read the rows past it.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description reinforces this by calling it a 'read-only SQL SELECT'. Beyond that, it transparently discloses the two output bounds (truncated and rendered_rows), explains how they relate to row caps and character budgets, and states the dependency on CANVAS_PROVIDER_TYPE=duckdb. No contradiction with annotations.

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?

The description is long but information-dense, with each sentence serving a purpose. It is front-loaded with the core purpose, then details table naming, example SQL, bounds, and paging. While not terse, it is well-structured and avoids redundancy, making it efficient for an agent to parse.

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 (SQL query over staged tables with bounds and paging), the description covers all essential aspects: purpose, table naming conventions, an example query, behavior when limits are hit, how to page, and the runtime requirement. The presence of an output schema is handled by the description explaining the bound fields, so nothing critical 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 the baseline is 3. The tool description adds value by giving an example SQL statement and clarifying that tables are referenced by names returned by chembl_get_bioactivities, which reinforces the schema. However, the parameter descriptions in the schema already convey most of the necessary meaning, so the incremental contribution is moderate.

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 ('Run a read-only SQL SELECT over the bioactivity rows...'), and immediately distinguishes itself from the sibling tool by stating 'across the FULL set, not the inline preview'. It also names the staged tables and views, making the tool's role 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?

The description explicitly tells the agent when to use this tool versus the preview provided by chembl_get_bioactivities, and points to chembl_dataframe_describe for discovering staged tables. It also gives concrete paging instructions ('append e.g. LIMIT 500 OFFSET 500 and re-call') and clarifies that aggregate computations should be done here.

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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