Summary of the Layer-1 inactivity-scoring model registry: how many per-target models, split by family, and median scaffold-split ROC-AUC.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| query_metadata | Yes | Echo of the resolved query. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool read-only and idempotent, so the description adds value by specifying the exact summary metrics. It does not discuss output format details, but for a zero-parameter summary tool this is an acceptable level of disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the tool's purpose and packs the key output dimensions into an efficient list. Every word contributes to understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter, read-only summary tool with rich output schema and clear annotations, the description fully covers what the agent needs to know. It explains the domain and the computed statistics, leaving no ambiguity about the tool's scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema provides complete coverage with nothing to describe. Per the rubric, a zero-parameter tool earns a baseline of 4, and the description does not introduce any confusing parameter-related language.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool summarizes the Layer-1 inactivity-scoring model registry with specific metrics (per-target counts, family split, median ROC-AUC). It distinguishes itself from sibling tools like get_model_card by focusing on aggregate registry statistics rather than individual model details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or alternative guidance is provided. The description implies a high-level overview use case, but does not mention when to prefer this over get_model_card or search-based tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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).
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.
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.
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.
