Read server-observed attempts for a private run: response statuses, Retry-After headers and arrival gaps. Arrival gaps include network/server effects; they do not establish exact client waiting time. Requires the run_id capability.
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes |
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?
Annotations already signal readOnlyHint, idempotentHint, and destructiveHint=false. The description adds value beyond those by clarifying that arrival gaps include network/server effects and do not establish exact client waiting time. It also emphasizes the 'server-observed' perspective, which is a useful behavioral nuance.
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?
Three sentences with no filler. The main verb and resource are front-loaded, followed by the data contents, a meaningful caveat, and the capability requirement. Every sentence earns its place.
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?
This is a low-complexity, single-parameter read tool with strong safety annotations. The description explains what data is returned and adds an important interpretation caveat. Since there is no output schema, a bit more detail about response shape or pagination would improve completeness, but the description is sufficient for a simple trace read.
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?
Schema coverage is 0%, so the description must carry the semantic weight. It indirectly ties the single run_id parameter to 'a private run' and mentions the run_id capability, but it does not explicitly define what run_id represents, where it comes from, or how it relates to the sibling create_retry_run tool. This is partial compensation, not full.
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 opens with a specific verb and resource: 'Read server-observed attempts for a private run.' It enumerates the concrete contents (response statuses, Retry-After headers, arrival gaps), which clearly distinguishes it from sibling tools focused on creating runs, listing findings, or publishing findings.
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?
Usage context is implied: this is the read tool for server-observed attempts of a private run, in contrast to the create/list/publish siblings. However, it does not explicitly state when to prefer this tool over alternatives or when not to use it. The only explicit guidance is the prerequisite 'Requires the run_id capability.'
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 maps cleanly to a distinct action: creating a private run, reading its server-observed trace, listing public findings, and publishing or replying to a finding. There is no functional overlap that would cause an agent to select the wrong tool.
All tool names follow a consistent verb_noun pattern (create_, read_, list_, publish_) with clear objects. The style is uniform snake_case, making the API predictable.
Four tools is well-scoped for the retry-trace domain: setup, inspection, discovery, and publication each have one dedicated tool. No tool feels redundant or missing for the apparent purpose.
The tool set covers the full intended workflow: create a retry run, exercise it externally, read the observed trace, browse published findings, and optionally publish or reply with evidence. No critical operation needed for the core use case is absent.