apex_run_agent_log_triageRedact and summarize supplied logs into severity, repeated failure patterns, and short safe samples; secret values never leave the redactor. DATA ONLY, read-only, no HMAC required, no network access.
| Name | Required | Description | Default |
|---|---|---|---|
| logs | No | ||
| text | No |
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?
The description adds meaningful behavioral guarantees beyond the readOnlyHint annotation: 'secret values never leave the redactor', 'no HMAC required', and 'no network access'. These details help the agent understand safety and side-effect boundaries.
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 well-structured sentence that leads with the main action, describes outputs, and appends key safety constraints. Every clause provides value with no redundancy.
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 covers the purpose, output contents, and important behavioral constraints, but it leaves the `text` parameter entirely undocumented. With no output schema, more detail on the return structure would make it fully complete for an agent to invoke correctly.
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 schema has 0% description coverage, yet the description only references 'logs' implicitly, never explaining the `text` parameter. It does not add meaning beyond the parameter names, so the agent cannot distinguish between the two params or know how to use them.
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 states a specific verb ('Redact and summarize') and resource ('supplied logs'), and names the outputs: severity, repeated failure patterns, and short safe samples. This clearly distinguishes it from sibling tools like secret_scanner or data_profile.
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 the tool should be used when logs need redaction and summarization, but it does not explicitly say when to use it over alternatives or provide exclusions. The guidance is implied rather than direct.
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, and descriptions provide sufficient boundaries. Some run_* analytics tools (e.g., deflated_sharpe vs empyrical_metrics) could be conceptually confused, but their specific inputs and outputs minimize ambiguity.
All tools share the apex_ prefix, and the verb_noun pattern is consistent (get, query, run, submit). The 'agent_' subgroup within run tools introduces a minor irregularity, but it remains readily comprehensible.
With 24 tools, the server is on the heavy side, falling into the 16-25 range. Many run_* tools are similar in nature (pure calculations), but each appears to serve a specific purpose, so the count is borderline rather than excessive.
The server name implies a card store, yet the tool surface only supports reading and querying cards, with no create, update, or delete operations. This is a significant gap that prevents full lifecycle management, though the analytics side is fairly comprehensive.