mcp-server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description must disclose behavior. It does describe the output content ('global supply concentration + US import reliance + a refined vs mined production read') and notes the data source, but it does not explicitly state that the operation is read-only, nor does it mention rate limits, error handling, or any side effects. The factual description helps but leaves some gaps.

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?

A single, front-loaded sentence that states the core function and key output components, followed by a brief note on data source. No wasted words, though the free-data remark is minor.

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 simple one-parameter tool with no output schema, the description adequately explains what the tool returns and its data source. It misses any mention of limitations or error conditions, but given the simplicity, it is fairly complete.

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 the parameter 'commodity' already explains what to provide. The tool description does not add additional meaning about the parameter beyond its role as input. Baseline 3 is appropriate since the schema handles the 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 states it provides a 'supply signal' for a commodity and enumerates the specific components (global supply concentration, US import reliance, refined vs mined production). This clearly distinguishes it from the unrelated sibling tools (finance/crypto). Lacks an explicit verb like 'get' or 'fetch', but the purpose is unambiguous.

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 when to use the tool (when you need critical-minerals supply data) and mentions the data is 'Free USGS/public data,' but gives no explicit conditions or alternatives. Since siblings are all unrelated, there is no need for differentiation, but no formal usage context is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the behavioral burden. It discloses the computed output (signal rating and percent upside), the methodology source (fundamentals + consensus), and implies a read-only analytical operation. It does not detail edge cases or response shape, but for a single-parameter signal tool this is reasonably 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?

One sentence efficiently states the resource, the expected output, and the methodology. It is front-loaded with the core purpose and contains no filler or redundant detail.

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 one required parameter and no output schema, and the description covers the main return concepts: signal classification and percent upside. It is complete enough for invocation, though exact output field names or formatting are not specified.

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%, and the parameter 'ticker' is already well documented as a US stock ticker. The description only restates the ticker concept without adding new parameter semantics, so the baseline score of 3 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 names a specific resource (US equity signal) and a specific verb/outcome (buy/neutral/sell signal plus percent upside to analyst price target). It clearly distinguishes itself from the sibling tools, which are about other domains like minerals, labor, and crypto-related signals.

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 clearly implies when to use it: for a US equity ticker requiring a fundamentals/consensus-based signal. It does not explicitly name alternatives or state when not to use it, but the domain is clear enough for basic routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full responsibility for behavioral disclosure. It states the data source (BLS) and lists the included metrics, but omits critical details like the output format (snapshot vs. time series), update frequency, units, or any caveats. This is a minimal disclosure that does not fully inform an agent about what to expect from the invocation.

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, well-structured sentence. It front-loads the primary purpose ('US labor-market signal') and then lists the specific data points. Every word adds value with no redundancy or filler.

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 low complexity (no parameters) and absence of an output schema, the description is moderately complete. It enumerates the metrics but does not specify the return structure (e.g., an object with keys), units, or whether historical series are included. This leaves some ambiguity for an agent attempting to interpret the result.

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 is empty (0 parameters), so there are no parameter semantics to document. Per the evaluation rubric, 0 params grants a baseline of 4. The description correctly avoids redundantly describing parameters.

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 identifies the tool as a US labor-market signal and enumerates five specific metrics (nonfarm payrolls, unemployment rate, wage growth, JOLTS openings/quits, participation). This makes the tool's purpose concrete and distinguishes it from siblings like equity_signal or token_scan, which cover unrelated 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?

The description implies usage for labor-market data but does not explicitly state when to choose this tool over alternatives. There are no exclusions or guidance on context (e.g., 'use for macro labor analysis'). The clear scope and zero parameters make it obvious enough, but explicit guidance is absent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. It explains that the tool aggregates specific sources and returns a signal plus conviction, which is meaningful. However, it does not describe the expected output format, scale, or any limitations such as data coverage or recency.

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, tightly worded sentence that front-loads the tool's purpose and immediately follows with concrete detail about inputs and outputs. There is no filler or redundant content.

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 is simple—one required parameter, no output schema—and the description adequately conveys what data is consumed and what is produced. It would be more complete if it specified the nature or range of the signal/conviction, but an agent can correctly invoke the tool with the provided information.

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 already fully documents the single ticker parameter, including an example, so the description adds only marginal reinforcement by calling out 'US stock' in the prose. This aligns with the baseline of 3 for high schema 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 clearly states what the tool does: it aggregates multiple smart-money data sources into an accumulation/distribution signal with conviction, and scopes it to US stocks. It is distinguishable from generic signal tools by its explicit data-source list, though it does not name or contrast a specific sibling.

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 when to use it—when a composite smart-money view of a US stock is needed—but provides no explicit guidance on when not to use it or how it compares to alternatives like equity_signal. Usage context is inferable but not directly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the output format ('signal-first report') but does not explicitly state that the operation is read-only or mention any authentication/rate-limit requirements. For an analysis tool, the read-only nature is implied but not explicit, leaving a gap.

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, information-dense sentence that front-loads the core purpose and lists specific risk factors, followed by the output type. There is zero redundancy or filler.

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 a simple two-parameter schema that is fully described, the description tells the agent what the tool does, what chains it supports, and what kind of output to expect. Nothing crucial is missing for correct 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?

Schema description coverage is 100%, and both parameters (address, chain) are adequately described in the schema. The description adds only that it supports EVM or Solana, which is already reflected in the chain enum. No additional semantic value beyond the schema is provided.

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 resource (crypto token) and the exact analyses performed (honeypot/rug risk, ownership, liquidity, holder concentration). It clearly distinguishes itself from sibling tools by being the only one focused on token due-diligence.

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 clearly scopes usage to EVM or Solana tokens, which tells an agent when this tool applies. It does not explicitly mention alternatives, but the sibling names (critical_minerals, equity_signal, etc.) make the token-specific domain unambiguous, so the agent can infer when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description carries the full burden of explaining behavior. It implies a read-only query through terms like 'history,' but it does not explicitly state whether the operation is read-only, what the response contains structurally, or any access requirements or limitations.

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 sentence with no filler. It front-loads the core concept and communicates the essential scope and window in a very small number of words.

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 relatively simple two-parameter tool, the description captures the core functionality. However, with no annotations, no output schema, and no usage guidance, an agent is left without information about return format, read-only guarantees, or when to prefer this tool over 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 description coverage is 100%, with both 'days' and 'wallet' already explained. The description adds context about the output (PnL and trade history) but does not need to elaborate on parameter semantics because the schema already covers them.

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 (a Base wallet address), the data (realized/unrealized PnL and trade history), and the time scope (lookback window). It is clear enough to identify the tool's purpose, though it lacks an explicit verb and does not directly distinguish itself from siblings.

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 smart_money or token_scan. There are no stated conditions, exclusions, or context clues beyond the inherent scope implied by 'Base wallet address.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.