Report BioCosm's actual data coverage so you never mistake missing data for a real-world zero. An empty or absent field on a node means "not in BioCosm's data," never a true zero. BioCosm is AI-generated and may contain errors.
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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral transparency burden. It adds the caveat that BioCosm is AI-generated and may contain errors, but does not elaborate on the tool's behavior beyond reporting coverage, such as whether it is read-only or the scope of coverage.
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 extremely concise with two sentences. The first sentence delivers the purpose and benefit, and the second adds a necessary caveat. Every word earns its place, with no wasted text.
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 description is fairly complete given the tool's simplicity and the existence of an output schema. It explains the critical interpretation of coverage data, though it could be slightly more explicit about what the coverage report contains (e.g., per field or per node).
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 no parameters, and schema coverage is 100% (trivially). According to the rubric, the baseline for 0 parameters is 4, and the description adds no further parameter information since none are needed.
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's purpose: 'Report BioCosm's actual data coverage'. It uses a specific verb and resource, and distinguishes itself from sibling tools like get_node or get_stats by focusing on coverage rather than node content or statistics.
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 explains when to use the tool implicitly: 'so you never mistake missing data for a real-world zero'. This provides clear context on the tool's utility, but it does not explicitly state when not to use it or mention alternatives among siblings.
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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Tools have mostly distinct purposes, though get_node and get_pos_score both provide probability-of-success data (get_node includes a summary, get_pos_score gives detailed factors). Descriptions clarify the difference, so confusion is minimal.
Naming follows a verb_noun pattern but mixes verbs: 'find', 'get', 'list', 'search'. Within the 'get' group, all are consistent. The mix is not chaotic and remains predictable.
13 tools cover the biomedical domain well without being excessive. Each tool has a clear purpose, from searching and listing to fetching detailed node data, statistics, and specialized reports.
For a read-only database, the tool set is complete. It supports search, filtered queries, detailed node retrieval, specialized data (PoS, freshness, staleness), and statistics. No obvious gaps for typical query needs.