Cassandra prediction hub: one MCP door over govcon forecasts (recompete, contractor signals).
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Average 4.7/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: list_domains enumerates the available prediction verticals, while predict executes a prediction for a given domain and entity. There is no overlap or ambiguity between them.
The naming is mostly consistent: list_domains follows the verb_noun pattern, while predict is a simple verb. The slight difference in style is minor and both names are intuitive.
With only 2 tools, the server is minimal but appropriately scoped for a focused prediction API. The list_domains helper complements the core predict action without unnecessary bloat.
The tool set covers the essential workflow: discovering available domains and making predictions. Minor gaps exist (e.g., no way to fetch historical predictions or get entity details), but they are not critical for the stated purpose.
list_domainsAInspectList Cassandra prediction verticals available via predict(domain, entity), with what each predicts and its data sources.
| 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 the full burden of behavioral disclosure. It states that the tool lists verticals and includes what each predicts and its data sources, making the read-only nature and output content transparent. It does not describe output format, but for a simple listing tool, this is adequate.
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 conveys the tool's purpose and output scope without unnecessary words. It is efficient and earns a perfect score for conciseness.
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 zero-parameter listing tool with no output schema, the description is complete. It explains what the list contains (vertical names, what each predicts, and data sources) and how it relates to predict, giving an AI agent all necessary context to invoke the tool 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 input schema has zero parameters, so there are no parameter semantics to explain. The description appropriately says nothing about parameters, and the baseline for zero-parameter tools is 4.
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 uses a specific verb 'List' and clearly identifies the resource: Cassandra prediction verticals. It further explains that these verticals are available via predict(domain, entity), which distinguishes it from the sibling tool predict by focusing on enumeration rather than prediction.
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 is used to discover prediction verticals and their details before calling predict. It does not explicitly state 'use this when you need to see available domains,' but the relationship to predict is clear, providing sufficient context for when to use this tool over its sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
predictAInspectCassandra 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses that the output is a leading-signal prediction, not a guarantee, and not an official action, as well as time frame ('weeks early'). It sets appropriate expectations about the non-deterministic nature of predictions, though it does not cover error handling or output structure.
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 moderately sized but front-loaded with a clear purpose statement and uses each sentence to convey unique information. It avoids redundancy and even includes a caveat about non-guarantee without fluff.
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 only 2 parameters, no output schema, and no annotations, the description covers the tool's purpose, the meaning of each domain, entity type, and the qualitative nature of the output. It also includes an important limitation note, making it complete for practical 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?
Adds significant meaning beyond the schema: domain descriptions explain each vertical's focus (e.g., 'recall' from complaint velocity, 'bio' from NIH→trial→SEC chain) and entity examples (Tesla Model Y, Moderna). Schema only provides minimal descriptions, so this enrichment is valuable.
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 it returns predictions about entities using leading signals, explicitly listing four verticals (recall, bio, recompete, ma) with examples. It distinguishes from sibling 'list_domains' by describing prediction behavior rather than just listing domains.
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?
Provides context by explaining how 'domain' selects the vertical and gives examples of entity names, but does not explicitly contrast with when to use 'list_domains' or state exclusions. It implies usage scenarios through the domain descriptions.
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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