apex_run_agent_data_profileProfile supplied tabular rows (shape, nulls, types, numeric ranges, sensitive-name hints) without echoing raw values. DATA ONLY, read-only, no HMAC required, no network access.
| Name | Required | Description | Default |
|---|---|---|---|
| rows | 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?
The description adds behavioral context beyond the readOnlyHint annotation by stating it will not echo raw values and explicitly mentions operational traits: 'DATA ONLY', 'no HMAC required, no network access'. This provides valuable information about data handling and side-effect-free operation, which aligns with the annotations without contradiction.
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 two short, information-dense sentences. The first defines the action and outputs without unnecessary words; the second lists key constraints. Every phrase adds value, making it exceptionally concise and well-structured.
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?
Given the simplicity (one parameter, no output schema) and the presence of readOnlyHint, the description covers the essential context: purpose, behavior, constraints, and enough detail about expected output via the listed profiling aspects. It does not explain return format details, but since no output schema is provided, the natural-language list of what is profiled is sufficient for an agent to anticipate the result.
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?
With 0% schema description coverage, the description must compensate. It calls the parameter 'supplied tabular rows', which clarifies that the array of objects represents a table with rows, but it does not detail the expected shape of each object or constraints beyond what the schema already provides (min/max items). It adds some meaning but not enough to fully bridge the coverage gap.
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 verb 'Profile' and the resource 'supplied tabular rows', listing specific outputs (shape, nulls, types, numeric ranges, sensitive-name hints). It also distinguishes itself from sibling tools by emphasizing 'without echoing raw values' and 'DATA ONLY', making its scope distinct from other data analysis tools like apex_run_empyrical_metrics or apex_run_alphalens_factor_research.
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 profiling tabular data but does not explicitly state when to use this tool vs alternatives. It mentions constraints like 'no HMAC required, no network access' but provides no direct comparison or exclusion of other sibling tools, only an implied context.
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.