cassandra-predict
Server Details
Cassandra prediction hub: one MCP door over govcon forecasts (recompete, contractor signals).
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.6/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: list_domains enumerates available prediction verticals, while predict performs the actual prediction for an entity within a chosen domain. There is no overlap or ambiguity.
Both tool names follow a verb-oriented pattern: list_domains uses verb_noun, predict uses a single verb. The convention is consistent and intuitive for the server's purpose.
With only 2 tools, the server is minimal but well-scoped for a focused predictive service. The count is slightly low but justifiable given the single core action (predict) and necessary enumeration (list_domains).
The tool surface covers the essential workflow: discover available domains and then make a prediction. Minor gaps exist (e.g., no tool to get details on a specific domain beyond listing), but the core functionality is complete.
Available Tools
2 toolslist_domainsAInspect
List Cassandra prediction verticals available via predict(domain, entity), with what each predicts and its data sources.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description fully explains the tool's behavior: it lists available domains with predictions and data sources. As a simple read-only list, no further behavioral details are needed.
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?
Single sentence, front-loaded with the action, no redundant information.
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?
Despite no output schema, the description covers the output content (predictions and data sources) sufficiently for a simple list tool.
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 input schema has no parameters; the description adds value by explaining what the response contains (predictions and data sources) beyond just listing names.
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 lists Cassandra prediction verticals for use with predict(domain, entity), specifying what each predicts and its data sources. This distinguishes it from the sibling predict 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 implies usage when needing to discover available domains before calling predict. No explicit alternatives or exclusions, but context is clear given the sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
predictAInspect
Cassandra unified predictor. From leading signals in public data, returns what is about to happen to an entity — weeks early — across four verticals. domain selects the vertical: 'recall' (consumer product / vehicle / drug / device recall risk from complaint velocity), 'bio' (biotech NIH→trial→SEC catalyst chain), 'recompete' (federal contract recompete-winner shift), 'ma' (government contractor acquisition target). entity is the name to look up (e.g. 'Tesla Model Y', 'Moderna', a contractor or agency name). Returns a leading-signal prediction with lead time — NOT a guarantee, and NOT a claim of any official action.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Prediction vertical to route to. | |
| entity | Yes | Entity name / ticker / id to predict on. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses that the tool returns a 'leading-signal prediction with lead time' and clarifies limitations (not a guarantee or official action). It implies read-only behavior by mentioning 'public data'. No contradictions with schema or annotations (none present).
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 paragraph but remarkably concise. It first states the overall purpose, then explains both parameters with context, and ends with a caveat. Every sentence adds value, and it is front-loaded with the core action.
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, so the description should compensate by explaining return values. It mentions 'leading-signal prediction with lead time' but does not specify the structure or format of the response (e.g., JSON fields, confidence scores). For an agent to parse the output, more detail is needed.
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 100%, so baseline is 3. The description adds value by elaborating on the 'domain' parameter with examples of each vertical and by providing entity examples (e.g., 'Tesla Model Y'). This enriches the schema descriptions, which are more technical ('Prediction vertical to route to.').
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 purpose: 'Cassandra unified predictor' that 'returns what is about to happen to an entity — weeks early' across four verticals. It uses a specific verb ('returns' / 'predicts') and resource (prediction for an entity in a domain), and its sibling 'list_domains' has a different function, so differentiation is clear.
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 explains the four verticals and their use cases, e.g., 'recall: consumer product recall risk from complaint velocity'. It sets expectations with 'NOT a guarantee, and NOT a claim of any official action'. However, it does not explicitly state when to use this tool versus the sibling 'list_domains' beyond implied purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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