Per-modality and per-source coverage stats (honest Phase-1 numbers).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| total | No | ||
| by_modality | No | Per-modality finding counts. | |
| curated_total | No | ||
| by_source_type | No | Per-source finding counts. | |
| query_metadata | Yes | Echo of the resolved query. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Per-modality and per-source coverage counts.",
+ "properties": {
+ "by_modality": {
+ "description": "Per-modality finding counts.",
+ "items": {
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "by_source_type": {
+ "description": "Per-source finding counts.",
+ "items": {
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "curated_total": {
+ "type": "integer"
+ },
+ "query_metadata": {
+ "description": "Echo of the resolved query.",
+ "type": "object"
+ },
+ "total": {
+ "type": "integer"
+ }
+ },
+ "required": [
+ "query_metadata"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive traits. The description adds the contextual caveat 'honest Phase-1 numbers', which signals data quality limitations or preliminary status, going beyond annotation-provided safety metadata.
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?
A single, front-loaded sentence delivers all essential information without redundancy, perfectly sized for the tool's simplicity.
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?
The tool has no parameters and an output schema exists, so little extra explanation is needed. The description adequately scopes the data breakdown, though it could clarify what 'source' means; this is a minor gap for a simple stats tool.
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?
With zero parameters and 100% schema coverage, there is nothing to document. Baseline for no-param tools is 4, and the description correctly avoids fabricating parameter details.
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 identifies the resource (coverage stats) and the dimensions of breakdown (per-modality and per-source), distinguishing it from sibling tools like search_* and get_model_card which target different data. Although no explicit verb is used, the tool name and content make retrieval unambiguous.
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?
The description implies usage for coverage statistics but provides no explicit when-to-use guidance or mention of alternative tools. It does not state when not to use it, leaving the agent to infer its niche from sibling names.
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.