PredictOracle - 12 forecasting tools: time-series, scenario analysis, risk projections.
Scored across 12 tools
Tools are mostly distinct, but early_warning and risk_forecast both flag urgent risks and could be confused. predict_article vs predict_entity vs predict_score also overlap in granularity, though descriptions clarify their scope.
Naming is consistently snake_case but mixes verb_first (predict_article, predict_score) with noun_first patterns (risk_forecast, evidence_decay), and a standalone 'ping' breaks the pattern. No single clear convention.
12 tools is well-scoped for a predictive risk analysis server. Each tool addresses a distinct aspect (forecast, deadlines, decay, health) without redundancy or bloat.
The tool set covers the core lifecycle of prediction: current state, trajectories, scenarios, risk ranking, evidence expiry, remediation speed, and system health. No obvious gaps for its stated purpose.
Upcoming deadlines with compliance probability %.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | No | Optional for entity-specific probability |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the output ('Upcoming deadlines with compliance probability %') and does not mention side effects, permissions, return format, or how the optional entity_id affects behavior. While it doesn't contradict any annotations, it offers minimal transparency into the tool's behavior.
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 concise sentence with no padding or redundant information. It is front-loaded with the core purpose, and every word earns its place. Though brief, it is appropriately sized for a tool with a single optional parameter.
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 absence of annotations and output schema, the description is too sparse to be complete. It does not explain the output structure, how the optional entity_id modifies results, or how 'compliance probability' is calculated. The tool is similar to several siblings, and the description lacks sufficient detail for an agent to confidently select and invoke it.
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's single optional parameter is fully described ('Optional for entity-specific probability'), and the tool description adds nothing beyond it. With 100% schema description coverage, the baseline score is 3; the description does not elevate this further.
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 conveys the tool's output—upcoming deadlines with compliance probabilities—though it lacks an explicit verb like 'list' or 'get'. It distinguishes itself from sibling tools by focusing on deadlines and compliance percentage, which is unique among the list.
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 is provided on when to use this tool versus alternatives such as early_warning or risk_forecast. The description gives no context, prerequisites, or exclusions, leaving the agent to guess the appropriate use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Critical warnings: evidence expiry, score prediction, DORA deadline.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | No |
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.
Evidence expiry timeline: what expires when, status per evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries full responsibility for behavioral disclosure. It only says 'Expiry timeline' and 'status per evidence', which implies a read operation, but does not explicitly state safety, side effects, or data source. The wording is too vague to be considered transparent.
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, compact fragment that conveys a clear idea without extra words. It is efficiently phrased, though it sacrifices completeness for brevity. Structure via a colon is acceptable but could be more conventional.
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?
With no output schema, no annotations, and a single undocumented optional parameter, the description must do more to describe the tool's behavior. It only gives a high-level notion of a timeline and status, but does not explain what the actual response looks like, whether entity_id is needed to filter, or how to interpret 'status'. This is insufficient for reliable invocation.
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 defines one optional string parameter 'entity_id' with no description and 0% schema coverage. The tool description never mentions this parameter or explains what entity_id refers to (e.g., evidence ID, case ID). The description fails to compensate for the schema's lack of parameter documentation.
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 'Evidence expiry timeline: what expires when, status per evidence' clearly identifies the resource (evidence) and the focus (expiry timeline, status). It is distinct from siblings like risk_forecast and trend_analysis. However, it lacks an explicit action verb like 'get' or 'list', so it is not a full verb+resource statement.
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 is provided on when to use this tool versus the many siblings. There is no mention of alternatives, prerequisites, or exclusions, leaving the agent to infer context. This is a significant gap given the large sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Server status.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavioral traits. 'Server status.' reveals nothing about side effects, read-only nature, authentication requirements, return format, or error handling. It is completely opaque.
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 extremely terse, but this is under-specification rather than effective conciseness. It does not convey enough information to be useful, so the brevity fails to earn 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?
Without an output schema, the description must explain what the tool returns. 'Server status.' does not describe the response structure, possible status values, or any operational details, making the tool incomplete for an agent to use 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 tool has zero parameters, so the baseline is 4. The description adds no parameter information, but with no parameters to document, no additional semantics are required.
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 'Server status.' is a vague restatement of the tool name, essentially paraphrasing 'health_check' without providing a specific verb or resource. It does not distinguish this tool from siblings like 'ping', which could also check server availability.
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 offers no guidance on when to use this tool versus alternatives. There is no mention of appropriate contexts, exclusions, or complementary tools, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Connectivity test.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. 'Connectivity test.' offers no details on what the tool returns (e.g., success/failure, latency), timeout behavior, or side effects. It adds little beyond the tool name and is essentially a tautology.
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 highly concise and front-loaded, with no wasted words. 'Connectivity test.' efficiently states the purpose. However, its brevity borders on under-specification, but for a zero-parameter tool it is appropriately sized.
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 no output schema and no annotations, the description is the sole information source. It fails to explain expected results, error conditions, or what 'connectivity' refers to in the context of the sibling tools. This is insufficient for an AI agent to fully understand the tool's behavior.
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 tool has zero parameters and the schema confirms this, so the baseline score of 4 is appropriate. The description does not need to explain non-existent parameters, and the schema already covers the empty parameter structure.
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's function as a connectivity test. It uses a specific verb 'test' and resource 'connectivity', distinguishing it from sibling tools focused on drift detection and health checks. However, it could more explicitly contrast with the overlapping 'health_check' tool.
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 provides no guidance on when to use this tool versus alternatives. There is no mention of scenarios, prerequisites, or why one would choose ping over health_check or drift scans. This is a clear omission.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Predict status of a specific DORA article in N days.
| Name | Required | Description | Default |
|---|---|---|---|
| article | No | e.g. Art. 28 | |
| entity_id | No | ||
| horizon_days | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description bears full responsibility for behavioral disclosure. It does not explain what 'predict' entails (e.g., whether it's read-only, what the output format is, or any side effects). It simply restates the action without adding behavioral context.
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, concise sentence with no wasted words. It is front-loaded with the core action and resource.
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 three parameters and no output schema, but the description only vaguely defines the purpose. It does not explain what 'status' means, how entity_id relates, or what the response looks like, leaving significant gaps for an agent to infer.
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 only 33% schema description coverage, the description adds meaning to 'article' (specific article) and 'horizon_days' (N days), but leaves 'entity_id' unexplained. It partially compensates for the schema gaps but does not fully cover the third parameter.
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 predicts the status of a specific DORA article over a time horizon. It uses a specific verb ('Predict') and resource ('status of a specific DORA article'), distinguishing it from sibling tools like predict_entity or predict_score.
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 provides clear context for when to use the tool: when predicting article status over a number of days. It does not explicitly mention alternatives or exclusions, but the use case is clear enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full predictive risk profile: score trajectory, degradation timeline, risk level.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | No | ||
| horizon_days | No | Prediction horizon (default: 30) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It lists output components but does not state whether the tool is read-only, how scores are computed, what data sources are used, or any rate limits or side effects. The predictive nature implies read-only, but this is not explicitly stated.
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, front-loaded sentence that uses a colon to succinctly enumerate the key output components. There is zero waste; every word contributes to conveying the tool's purpose.
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 absence of annotations, output schema, and the existence of numerous sibling tools, this description is insufficient for an agent to fully understand the tool's behavior and decide when to invoke it. It provides only a high-level summary without addressing practical details like entity type support, horizon options, or comparison to alternative tools.
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 covers only 50% of parameters (entity_id has no description). The tool description does not compensate: it neither explains entity_id nor clarifies how horizon_days relates to the listed output components (score trajectory, degradation timeline). Required parameters are 0, but no guidance is given on defaults or optionality.
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 'Full predictive risk profile: score trajectory, degradation timeline, risk level' clearly indicates that the tool generates a comprehensive risk profile for an entity, listing specific output components. It distinguishes itself from siblings like predict_score or risk_forecast by emphasizing a 'full' profile, though it does not explicitly name alternatives.
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 provides no guidance on when to use this tool versus its many siblings (e.g., risk_forecast, early_warning, scenario_forecast). There is no mention of use cases, prerequisites, or exclusions, leaving the agent to infer appropriate usage solely from the name and vague description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Score trajectory: current, 7d, 14d, 30d, 60d, 90d with trend.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It discloses that the tool outputs a trajectory with specific time points and a trend, but it omits crucial behavior such as whether the operation is read-only, what data sources are used, or any limitations. This is insufficient for an agent to understand side effects or context.
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, front-loaded sentence that efficiently lists the time horizons and trend. It is appropriately concise for a simple tool, though the lack of detail limits usefulness. No wasted words, but the terseness borders on under-specification.
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 only one parameter and no annotations or output schema, but the description still leaves critical gaps: what 'score' refers to, how entity_id should be formatted, and what the response structure looks like. Given the numerous similar sibling tools, this lack of context makes the tool difficult to select and 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 only parameter, entity_id, is not explained in the description at all. With 0% schema description coverage, the meaning of entity_id remains completely undefined, leaving the agent to guess what identifier to provide. The description fails to add any value beyond the bare schema property.
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 'Score trajectory: current, 7d, 14d, 30d, 60d, 90d with trend' indicates the tool provides a score over time intervals, but it does not specify what entity or score type it applies to. It is distinguishable from siblings like predict_article and predict_entity only by the generic term 'score', leaving ambiguity about the resource being scored.
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 this tool instead of alternatives like trend_analysis or risk_forecast, nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
How fast are findings being resolved? Average close time, velocity rating.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It mentions the output metrics (average close time, velocity rating) but does not disclose how the calculation works, what entity_id does, or any potential side effects or limitations. Minimal behavioral insight beyond the bare metric.
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 very short and front-loaded with the core question. It wastes no words, effectively conveying the primary purpose. However, its extreme brevity leaves gaps that affect other dimensions.
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 there is no output schema and no annotations, the description should explain return values and parameter usage. It provides only the metric names, not interpretation, data scope, or the role of entity_id, making it incomplete for an agent to invoke properly.
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, is not mentioned in the description. With 0% schema description coverage, the description provides no meaning or context for the parameter, making it impossible to know if it is optional or how to use it.
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 measures how quickly findings are resolved, specifically mentioning average close time and velocity rating. This distinguishes it from sibling tools that focus on deadlines, trends, or predictions.
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 provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, contexts, or exclusions, relying solely on the tool's name and metric definition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Which articles will degrade first? Sorted by urgency.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description is the only source of behavioral info. It discloses that results are sorted by urgency, but nothing else: no read-only guarantee, side effects, input requirements, or interpretation of 'degrade.'
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 very concise with two short sentences. It is front-loaded with the key question and adds a sorting detail. However, its brevity comes at the cost of the completeness needed for a useful description.
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 output schema and minimal description. It does not explain what the output looks like, how urgency is determined, or how entity_id is used. Given the tool's apparent function, this is severely incomplete.
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, is not mentioned in the description. With 0% schema coverage, the description fails to explain what entity_id refers to or how it relates to the forecast, leaving the agent without crucial input semantics.
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 indicates the tool predicts which articles will degrade first, sorted by urgency. This is a clear concept, but it lacks a direct verb-resource structure and does not distinguish it from sibling tools like scenario_forecast or predict_article.
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. There is no mention of when to use this tool over alternatives, prerequisites, or exclusions, making it hard for an agent to choose it correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
What-if: no_testing, cloud_outage_30d, no_training, policy_expired, data_breach.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| scenario | No | no_testing|cloud_outage_30d|no_training|policy_expired|data_breach | |
| entity_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description was the only chance to disclose behavioral traits like side effects, permissions, or return format. It discloses none of these—only a list of scenario names, which provides no insight into the tool's behavior.
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 extremely short, but this is under-specification rather than conciseness. It consists of a single phrase and a list of scenario names without any explanatory structure. It does not earn its place as a meaningful descriptor.
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 tool has 3 parameters, no annotations, and no output schema, so the description must carry substantial contextual weight. It fails completely: no purpose, no parameter semantics, no usage context, and no behavioral detail. The tool is effectively undescribed.
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 description coverage is only 33%, and the description adds no meaning beyond the schema for any parameter. The scenario values are already listed in the schema, while 'days' and 'entity_id' remain completely unexplained. No compensation for the low coverage.
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 says 'What-if:' and lists scenario names, suggesting hypothetical scenario forecasting, but it lacks a clear verb or resource to explain what the tool actually does. It does not explicitly state whether it simulates, predicts, or assesses risk, nor does it differentiate from sibling tools like risk_forecast or predict_score.
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?
There is no guidance on when to use this tool versus the many sibling tools (e.g., risk_forecast, trend_analysis, predict_entity). The description gives no context for choosing scenario_forecast over alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Historical trend: audit events, score history.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It only hints at the subject matter (historical trends) without describing output format, pagination, authentication, or side effects. This is a significant gap for a query tool.
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 fragment, but it is under-specified rather than concisely complete. It omits essential details and does not earn its brevity.
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?
For a tool with no annotations, no output schema, and one undocumented parameter, the description should explain return structure and parameter semantics. It only names the data areas, making it inadequate for reliable use among many siblings.
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% and there is one parameter (entity_id) with no description. The description does not mention entity_id or explain its role, leaving the agent without any semantic guidance beyond the parameter name.
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 names the resource (audit events, score history) and implies a trend query, but lacks a specific verb like 'get' or 'retrieve.' It vaguely differentiates from prediction-focused siblings but doesn't clearly state its function.
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 is provided on when to use this tool versus alternatives like predict_score or health_check. The phrase 'Historical trend' implies a use case but offers no exclusions or context, leaving the agent to infer appropriateness.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
deadline_riskearly_warningevidence_decayhealth_checkpingpredict_articlepredict_entitypredict_scoreremediation_velocityrisk_forecastscenario_forecasttrend_analysisRiskOracle - 13-tool enterprise risk MCP: heatmaps, KRIs, scenario sims, Basel/ICAAP outputs.
Deterministic crypto, options, forex, on-chain, prediction, risk math. 70 tools.
Deterministic what-if & scenario simulation for AI agents: projections, sensitivity & break-even.
PriceOracle - 7 dynamic pricing tools: elasticity, A/B, segment pricing, competitor scan.
Add one secure layer between your agents and this server.
