Server Quality Checklist

Tool Scores

• find_new_hot_poolsA
  4.3/5.0
  Behavior4/5

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

  Annotations already cover read-only/open-world/non-destructive behavior, and the description adds the important caveat that appearing here is 'NOT a safety endorsement' and that new pools carry high risk. This is valuable non-obvious context beyond the annotations, though it doesn't elaborate on ordering, freshness, or rate-limit behavior.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Two crisp sentences, front-loaded with the action and return fields, with the risk caveat placed second. No wasted words and no repetition of schema or annotation data.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a simple two-parameter, zero-required discovery tool with an output schema, the description is complete: it states the purpose, output scope, risk posture, and the follow-up action. The only minor omission is explicit guidance on get_token_liquidity, which is a usage-guideline nuance rather than a completeness gap.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The description conveys no input-parameter meaning; chain is explained in the schema and limit is only self-evident from its name plus min/max/default constraints. With 50% schema coverage, the description doesn't add enough to lift parameter understanding beyond what the schema already offers.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  States a concrete action ('Scan a chain') and a specific resource ('newest and most active trading pools'), and lists the exact return fields. It also frames itself as 'Discovery only' and routes to assess_token_risk, distinguishing it from the risk-assessment sibling.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The 'Discovery only' line and the explicit instruction to send token_address to assess_token_risk for anything you intend to act on give clear when/when-not guidance. However, it never addresses get_token_liquidity as the targeted single-token liquidity lookup, so the sibling comparison is incomplete.

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

• assess_token_riskA
  4.4/5.0
  Behavior5/5

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

  The annotations only declare the tool read-only, idempotent, and non-destructive. The description goes far beyond this by explaining the critical semantics of 'unknown', the meaning of 'confidence', the data_gaps output, and the tool's blind spots (off-chain risk, team behavior, governance rugs). This is exactly the kind of behavioral context an agent needs to avoid misusing the result.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is front-loaded with the tool's purpose and return value, then moves through coverage, critical caveats, and limitations. It is somewhat long but earns its length given the safety-critical nature of the tool. A small amount of redundancy exists between 'Reports observable on-chain risk only' and the later 'cannot see off-chain risk' phrasing.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's complexity, the presence of an output schema, and the safety-sensitive use case, the description provides everything needed: what checks are performed, how to interpret non-obvious outputs like 'unknown' and 'confidence', and what the tool cannot detect. No critical operational context is missing.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The input schema already provides 100% parameter descriptions, including detailed guidance on chain_hint and verbose. The tool description does not add new parameter-level meaning, but it doesn't need to because the schema field descriptions are already thorough. This matches the baseline for high schema coverage.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly identifies the tool as a pre-trade safety check that outputs a risk verdict, a 0-100 score, and supporting signals. It names the specific resource (token) and the action (assess risk), and the coverage list makes its scope unambiguous.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description explicitly tells the agent when to use the tool: before buying, holding, or recommending a token. It also gives important when-not guidance: 'unknown' must not justify a trade, 'low' means no fatal signal found, and off-chain risks are out of scope. It does not explicitly compare against sibling tools like get_token_liquidity or find_new_hot_pools, but the usage context is otherwise clear.

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

• get_token_liquidityA
  4.5/5.0
  Behavior5/5

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

  The description richly discloses behavior beyond the annotations: it explains failure modes, what null liquidity means, that 'unavailable' does not indicate zero liquidity, and what 'drained' implies for tradability. This is exactly the interpretive context an agent needs and goes well beyond the readOnlyHint/idempotentHint annotations.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is compact but information-dense, with the core purpose front-loaded and status semantics following in a structured, readable sequence. Every sentence adds value by preventing a specific misinterpretation, so the length is justified and efficient.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's moderate complexity, full schema coverage, rich annotations, and presence of an output schema, the description is complete. It covers the main data returned, the critical status field, edge cases, and the meaning of null values — everything an agent needs to correctly interpret results.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 100%, so the baseline is 3. The description does not add new parameter-level detail beyond the schema, but the schema already documents 'address' and 'chain_hint' sufficiently, including the fork-disambiguation purpose of chain_hint. No compensation is needed.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description opens with a specific verb and resource: "Liquidity snapshot for a token's primary trading pair," and lists concrete outputs such as price, 24h volume, pair count, and chains. This clearly distinguishes it from sibling tools like find_new_hot_pools or assess_token_risk, which target discovery and risk assessment rather than current liquidity measurement.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description gives clear operational guidance: check 'status' before trusting numbers, and interprets each status value so the agent knows how to act on 'unavailable', 'not_found', 'unpriced', and 'drained'. It does not explicitly name sibling alternatives or state when to prefer them, but it provides strong context for correct usage.

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