nullary

list_top_targets

Read-onlyIdempotent

Coverage browse: the most heavily-pursued ('graveyard') targets, ranked by recorded negative findings. Optional family filter (kinase, gpcr, protease, nuclear_receptor, ion_channel, transporter, phosphatase, other).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of findings to return (1–100, default 25).
`family`	No	target family filter (optional)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`query_metadata`	Yes	Echo of the resolved query.

TDQS

A4.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful context about the data source ('negative findings') and the 'graveyard' concept, but does not reveal internal ranking mechanics or other behavioral nuances. This is a moderate addition beyond annotations.

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 a single, front-loaded sentence that efficiently communicates purpose, ranking basis, and an optional filter. Every token earns its place; no filler or redundancy.

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 simple list-tool nature, the presence of an output schema, strong annotations, and only two parameters, the description is sufficiently complete. It explains what is listed, how it is ranked, and the filtering option, leaving no critical gap for an agent to invoke the tool correctly.

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 coverage is 100%, so the baseline is 3. The description enriches the 'family' parameter by enumerating valid filter values (kinase, gpcr, protease, etc.) which are not present as an enum in the schema, adding meaningful semantics 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 states a specific resource ('graveyard' targets ranked by negative findings) and a clear action (browse/list). It distinguishes itself from sibling search tools by presenting a coverage-browsing view rather than a detailed search, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly implies a use case: when you want to browse the most heavily-pursued targets by negative findings, optionally filtered by family. However, it does not explicitly mention when not to use this tool or name alternatives, so it stops short of full exclusion guidance.

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

B3/5.0

Disambiguation4/5

Most tools have clearly distinct purposes by targeting specific failure types (e.g., ADMET, ADC, bispecific). However, the high number of similarly named 'search_failed_*' and 'search_*_failures' could cause confusion without careful reading, and subtle overlaps exist (e.g., search_failed_adcs vs search_adc_linker_failures are related but distinct).

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case. The verbs 'search_', 'get_', and 'list_' are used appropriately and predictably, with no mixing of conventions.

Tool Count4/5

With 35 tools, the server is on the high side but still appropriate for the broad domain of pharmaceutical failure data across many modalities. Each tool covers a specific niche, though some consolidation might be possible.

Completeness5/5

The tool surface is remarkably comprehensive, covering failures across small molecules, antibodies, ADCs, bispecifics, PROTACs, oligonucleotides, peptides, vaccines, CRISPR, and more. It includes meta-queries for indicators and targets, leaving no obvious dead ends for agents exploring failure data.

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