Get a high-level overview of the BioCosm database: total nodes, writeups, companies, breakdowns by type/status/domain, validation scores, and recent pipeline runs.
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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?
While the description lists output contents, it lacks details on authorization, caching, or data freshness. Without annotations, more behavioral disclosure is expected.
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, information-dense sentence with no redundancy. The structure is efficient and front-loaded.
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?
With zero parameters and an output schema present, the description adequately covers the tool's function and return data, leaving no gaps.
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?
No parameters exist, so the description does not need to add parameter meaning. Baseline 4 is appropriate.
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 it retrieves a 'high-level overview' listing specific data elements (total nodes, writeups, companies, etc.), distinguishing it from sibling tools that handle individual entities.
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 aggregate overviews but does not explicitly state when to avoid this tool in favor of siblings like get_node or search_nodes.
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.