early_warning
Critical warnings: evidence expiry, score prediction, DORA deadline.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | No |
Critical warnings: evidence expiry, score prediction, DORA deadline.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | 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?
There are no annotations, so the description is the sole source of behavioral information. It does not disclose whether the tool is read-only, what output format to expect, whether it aggregates data from other tools, or any limits or failure modes. This is a significant gap for a tool that presumably returns warnings.
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 short and to the point, using a colon to list key topics, which is efficient. However, it is under-specified: it reads more like a headline than an explanation, lacking any verb or elaboration, so the brevity does not serve the purpose well.
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 tool has no annotations, no output schema, and one undocumented parameter, placing a high burden on the description. The current text only provides a topic list and leaves out return semantics, input meaning, and use context, rendering it insufficient for reliable use.
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 only parameter, entity_id, has no schema description and is not mentioned in the tool description. Since schema coverage is 0%, the agent has no semantic clue about what entity_id refers to (e.g., an evidence item, a case, a person), making correct invocation and parameter setting unreliable.
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 identifies the tool as dealing with 'critical warnings' for evidence expiry, score prediction, and DORA deadline, giving a specific scope. However, it lacks an explicit verb or action (e.g., 'retrieve' or 'generate'), which reduces clarity, and it does not distinguish how this differs from sibling tools like deadline_risk or evidence_decay.
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 usage guidance is provided; the description does not state when to use early_warning instead of deadline_risk, evidence_decay, predict_score, or other siblings. It does not mention prerequisites, exclusions, or alternatives, leaving the agent without decision criteria.
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.
Tools like deadline_risk, early_warning, and evidence_decay have overlapping concerns about deadlines and warnings, though descriptions provide some distinction. Similarly, predict_article, predict_entity, and predict_score all cover predictions but differ in scope. Some ambiguity remains.
All names use snake_case, but the naming pattern mixes verb phrases (predict_article, trend_analysis) with noun phrases (deadline_risk, health_check), lacking a consistent verb_noun or noun_verb structure. This inconsistency can confuse an agent.
12 tools is well within the optimal 3-15 range for a specialized predictive analytics server. Each tool appears to serve a distinct function without unnecessary bloat or deficiency.
The tool set covers core predictive needs: deadlines, warnings, decay, scores, scenarios, trends, and remediation velocity. Missing are raw data access or configuration tools, but for a read-only prediction server, the surface is reasonably complete.