aero-allocator

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct aspect of Aerodrome allocation: history, demand prediction, mechanism status, vote calldata preparation, allocation recommendation, and pool scanning. There is no overlap; descriptions clearly differentiate their purposes.
Naming Consistency4/5
Most tool names follow a clear verb_noun pattern (predict_demand, scan_pools, prepare_vote_calldata, recommend_allocation), but two are noun phrases (pool_history, predictive_allocation_status). The naming style remains consistent with snake_case and descriptive terms.
Tool Count5/5
With 6 tools, the server is well-scoped for its domain. Each tool serves a necessary function in the allocation workflow, neither too few to be incomplete nor too many to be unwieldy.
Completeness5/5
The tool set covers the full lifecycle: scanning for overview, historical data, demand prediction, allocation recommendation, and vote calldata construction. There are no obvious gaps for the intended purpose of optimizing Aero vote allocation.
Average 4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 14 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool returns weights summing to 100% and mentions a 25% concentration cap for voter_roi. However, it does not state whether the tool is read-only, whether it requires authentication, or any side effects. The refresh parameter is not explained, which is a gap for behavioral understanding.
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 two sentences long, front-loading the purpose and then detailing the objectives. Every sentence adds value, and there is no redundant or extraneous information. It is optimally concise for the information provided.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters, no annotations, and no output schema, the description is moderately complete. It explains the output (weights summing to 100%), covers the two modes, and mentions the concentration cap. However, it lacks explanation for refresh and maxPools, and does not detail the return format beyond the sum constraint. Additional context on these gaps would improve completeness.
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 0%, so the description must compensate. It explains the objective parameter in detail (the two enum values and their behaviors), but does not explain the refresh boolean or maxPools integer parameters. For a 3-parameter tool, covering only one well is partial but the most critical parameter is covered, so a middle score is appropriate.
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 states that the tool produces concrete incentive-allocation recommendations across Aerodrome pools, and explains the two possible objectives. This distinguishes it from sibling tools like pool_history (historical data) and predict_demand (demand prediction), making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the two objectives (protocol_efficiency and voter_roi) and their behaviors, providing some guidance on which to choose. However, it does not explicitly state when to use this tool over siblings or provide exclusions/alternatives. The guidance is implicit in the objective descriptions but lacks completeness.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden. It states the tool reports status (read-only) but does not explicitly confirm it has no side effects or require special permissions. The behavior is implied but not fully transparent.
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 a single sentence that is front-loaded and free of unnecessary words. It efficiently communicates the tool's purpose without redundancy, earning a high score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (zero parameters, no output schema, no nested objects), the description provides sufficient context. It describes the tool's function and the specific mechanism it belongs to, making it complete for an agent to understand its utility.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters and 100% coverage, so the description's role is to explain what information the tool returns. It adds meaning beyond the schema by specifying that it reports whether 'live contracts are wired into this server', which clarifies the output's semantic content.
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 states that the tool reports the status of the direct Predictive Allocation submission path, specifically whether live contracts are wired into the server. It distinguishes from sibling tools (e.g., pool_history, recommend_allocation) by being a status check rather than a data query or action tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use guidance is given. The description implies it should be used to check system connectivity, but it does not specify when this is preferable to alternatives or mention prerequisites. This is acceptable for a simple read-only tool, but could be more helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 ordering and the in-progress epoch, but does not mention read-only nature, authentication requirements, or rate limits. For a read-only historical tool, this is adequate but not comprehensive.
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?
A single, well-structured sentence that front-loads the purpose and includes all key details without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is sufficient for a simple tool with two parameters and no output schema. It explains what data is returned and ordering, though it could mention that it returns rows or a list.
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 covers 50% of parameters (pool with description). The description adds context that pool refers to 'one Aerodrome pool', but does not add meaning for the 'epochs' parameter beyond schema constraints. Baseline 3 is appropriate.
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 states it provides per-epoch history for one Aerodrome pool, listing specific data types (votes, AERO emissions, trading fees, bribes/incentives) and ordering (newest first). This distinguishes it from sibling tools that focus on predictions, allocation, or scanning.
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 implies when to use (when historical pool data is needed) but does not explicitly state when not to use or mention alternatives. However, the context of sibling tools makes the usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses data caching (~5 min) and mentions the key output field. However, it does not state whether the tool is read-only, permissions needed, or potential side effects. It provides moderate transparency.
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 sentences with precise language. No redundant words. Purpose is stated upfront, followed by key output explanation and caching note.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description adequately explains what the tool does and the main output for a 3-parameter tool with no output schema. It could benefit from a brief note on return structure or error conditions, but overall it's sufficient for agent understanding.
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 67% (limit and refresh have descriptions, sortBy lacks description but enum values are self-explanatory). The description adds minimal extra parameter meaning beyond schema, mostly contextualizing the output rather than parameters.
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 states the action ('Forecast... and compare') and resource ('top Aerodrome pools'). It distinguishes from siblings (pool_history, recommend_allocation, etc.) by specifying it's about next-epoch demand vs current allocation.
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 explains the key output (predictiveEdgePct) and its interpretation (positive edge = under-incentivized), giving context for when to use. It implicitly suggests this tool for identifying under-incentivized pools, but lacks explicit when-not-to-use or alternative comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavior. It describes the tool as scanning for live data and sorting by staked TVL. It does not explicitly state read-only nature or any side effects, but the verb 'scan' implies a read operation. The description adds value over no description but lacks explicit behavioral guarantees.
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 two sentences long with no wasted words. It front-loads the core action and output fields, then provides usage guidance. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no output schema, the description lists the main return fields (live TVL, staked TVL, fee tier, emissions) and states the sort order. It also mentions the platform (Aerodrome on Base) and ties to sibling tools implicitly. A minor gap is not describing each field in detail, but for a scan tool this is sufficient.
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%, meaning the input schema already documents parameters (limit, minTvlUsd) with descriptions and defaults. The tool description does not add additional meaning to these parameters beyond what is in the schema, so baseline score of 3 is appropriate.
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 states the action ('Scan'), the resource ('Aerodrome (Base) gauge-enabled pools'), and the returned fields (live TVL, staked TVL, fee tier, emissions). It distinguishes itself from siblings by positioning as a market overview tool before predicting demand.
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 explicit usage context: 'Use this for a market overview before predicting demand.' This implies it is a preliminary step to predictive tools like predict_demand. It does not specify when not to use it, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description bears full burden. It discloses the tool does not sign transactions and returns data for external signing, and notes voting constraints. This adequately discloses behavioral traits beyond basic purpose.
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 sentences plus a note. Front-loads the action and output format, then adds constraints. Every sentence is informative with no wasted words.
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?
Covers input, output, usage constraints, and the tool's role in a broader signing flow. No output schema exists, but description explains return values. Sibling tool names confirm differentiation. Complete for decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50% (veNftId has description, allocations does not). Description adds meaning by summarizing parameters as 'veAERO NFT id + pool weights', clarifying the allocation structure. It doesn't detail constraints like maxItems, but the schema covers those.
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?
Description clearly states the tool builds unsigned calldata for a specific function (Aerodrome Voter.vote()), specifying verb, resource, and input. Sibling tools are about prediction and scanning, so this tool's distinct purpose is evident.
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?
Description implies usage context for voting calldata preparation, and includes important constraints (once per epoch, not final hour). It does not explicitly contrast with siblings, but the context is clear enough given sibling tool names.
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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