Dated schema/tool-count observations for an MCP server (a series when known): first_seen, last_observed, drift events. Facts from the register, not a score.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | e.g. io.github.owner/name |
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 carries the full burden. It discloses the tool is read-only and factual ('Facts from the register') and returns time-series data, but omits details like permissions, error behavior, or what happens for unknown IDs.
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 sentence with a clarifying fragment, no wasted words, and the key action and result are front-loaded. However, the phrase 'a series when known' is slightly vague, preventing a 5.
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?
In the absence of an output schema, the description lists the return fields (first_seen, last_observed, drift events) which is helpful. However, it doesn't specify the data structure (e.g., array or object) or define 'drift events', leaving some ambiguity.
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 description adds context that the 'id' parameter refers to an MCP server identifier, supplementing the schema's example. With 100% schema coverage, baseline is 3; the description provides minimal extra meaning beyond that.
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 returns dated observations (first_seen, last_observed, drift events) for an MCP server, which distinguishes it from sibling 'trust_score' by explicitly saying 'not a score'. However, it does not fully distinguish from 'dependency_risk', leaving some ambiguity.
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 guidance on when to use this tool versus siblings 'dependency_risk' or 'trust_score'. The description only explains what it does, not the appropriate context or exclusions.
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.
Each tool has a clearly distinct purpose: dependency_risk focuses on dependency mortality, schema_history tracks temporal changes, and trust_score computes a composite metric. No overlap or confusion possible.
All three tool names follow a consistent snake_case pattern with descriptive nouns (dependency_risk, schema_history, trust_score). The naming is predictable and clear.
Three tools is well-scoped for a server focused on analyzing MCP server registry data. Each tool covers a distinct aspect (risks, history, score), and no additional tools seem necessary.
The set covers the core evaluation dimensions (mortality risk, historical trends, and a combined score). A minor gap might be raw data access (e.g., listing all servers or raw vulnerability counts), but the scope is focused and complete for its stated purpose.