Ranked economic opportunities, market evidence, experiments, and margin intelligence for AI agents.
Scored across 3 tools
The three tools are fairly distinct: one ranks opportunities from existing evidence, one fetches live market data to derive opportunities, and one provides voice pricing benchmarks. There is slight overlap between economic_opportunities and live_market_scan since both surface opportunities, but their data sources differ enough to reduce confusion.
Tool names use a mix of descriptive phrases: economic_opportunities is a noun phrase, live_market_scan is adjective_noun_noun, and voice_margin_index is noun_noun_noun. They are readable and somewhat parallel in style, but they do not follow a consistent verb_noun or noun_verb pattern.
Three tools is on the low end for a server claiming to be an 'Economic Opportunity Intelligence' platform. The count is not absurdly thin, but it feels slightly under-scoped for the breadth implied by the server name.
The server covers opportunity ranking, live market scanning, and voice pricing benchmarks, but there are no tools for querying historical data, saving or tracking opportunities, or retrieving details on specific opportunities. The surface is too narrow for the stated purpose of economic opportunity intelligence.
Rank economic opportunities from current structured evidence.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full responsibility for behavioral disclosure. The description only says 'rank', which could imply a read-only operation, but it does not state whether there are side effects, what data is consumed, whether it modifies stored evidence, or any other operational constraints. This is a significant transparency gap for a tool with no annotation support.
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 that front-loads the core action ('Rank economic opportunities') and its data source ('current structured evidence'). There is no fluff or repetition, and every word contributes to the intent.
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 being a simple tool with no parameters, the description lacks any expression of output behavior or relationship to sibling tools. Because there is no output schema, the agent needs some indication of what 'ranked' returns (list, scores, order) and when to call this over the siblings; neither is provided. The sentence is too sparse to be fully actionable in a wider context.
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, which meets the baseline of 4. The description appropriately does not introduce fake parameter details, and the schema already declares an empty object, so nothing needs compensation.
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 states the verb 'rank' and the resource 'economic opportunities', and adds the source 'current structured evidence', making the core purpose clear. It doesn't explicitly differentiate from the sibling tools, but the phrasing implies a prioritization task distinct from a live scan or margin index.
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 given for when to choose this tool over the siblings 'live_market_scan' and 'voice_margin_index'. The phrase 'from current structured evidence' hints at a prerequisite, but it is not expanded into a clear when-to-use statement, alternatives are not named, and no exclusions are described.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch machine-readable market data live and derive business opportunities without manually entering the signal.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the tool fetches live data and derives opportunities, but it doesn't mention rate limits, latency, data format, or whether it is read-only. The phrase 'without manually entering the signal' hints at automation but not enough detail.
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?
One sentence, front-loaded with the action and resource, and no wasted words. It earns its place by explaining both the data fetch and the derived value.
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 tool with no output schema, the description is mostly complete. However, it doesn't describe what the output looks like or any constraints (e.g., market scope, frequency). Given the tool's autonomous nature, a bit more context on output format or behavior would help.
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 schema is trivially complete. The description adds the behavioral context of what the tool does with no input, which is sufficient. Baseline 4 for zero params is appropriate.
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 states a specific verb ('Fetch') and resource ('live market data'), and adds the purpose of deriving business opportunities. It is clear enough to distinguish from siblings like economic_opportunities and voice_margin_index, though it doesn't explicitly name them.
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: use this tool when you need live market data and business opportunities without manual signal entry. It does not explicitly state when not to use it or name alternatives, but the context is reasonably clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Structured AI voice pricing benchmarks.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations at all, the description carries the full burden of disclosing behavior, but it only says the output is a structured pricing benchmark. It does not explain what data is included, whether it is a snapshot or live, what units/currencies are used, or whether the call is read-only, so the tool's actual runtime behavior remains 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 short and free of redundancy, but it is not structured as a complete tool directive. The word 'Structured' adds little, and the lack of a verb makes the sentence weak even if concise.
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 itself is simple because it has no parameters, but with no output schema and no behavioral description, an agent still cannot know what result to expect. The description should at least say that it returns benchmark pricing data and perhaps the format or scope, which it does not.
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 there is no parameter meaning that the description needs to clarify. Consistent with the baseline for 0 params, the lack of parameter-specific documentation is not a defect.
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 a resource ('AI voice pricing benchmarks') but lacks an operational verb such as 'get' or 'returns', so it reads as a data label rather than a tool action. It is not a tautology, but it also doesn't make clear how this tool differs from the sibling tools like economic_opportunities or live_market_scan.
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 its siblings, and no exclusions or alternatives are mentioned. The description only implies that this is a benchmark resource, which is too weak to help an agent choose among the available tools.
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.
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