Drop Beacon

Server Quality Checklist

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Latest release: v0.2.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: check_availability focuses on product stock at retailers, get_brand_info on brand details, get_latest_drops on new releases, get_market_trends on market analytics, get_price_comparison on cross-retailer pricing, and search_products on product discovery. An agent can easily differentiate them based on their specific functions.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., check_availability, get_brand_info, search_products) with clear, descriptive verbs and nouns. There are no deviations in style or convention, making the set predictable and readable.
Tool Count5/5
With 6 tools, the server is well-scoped for its EDC market research purpose, covering key areas like product search, availability, pricing, brand info, trends, and new drops. Each tool earns its place without being overwhelming or insufficient for the domain.
Completeness4/5
The tool surface is nearly complete for EDC market research, covering search, availability, pricing, brand analysis, trends, and new releases. A minor gap exists in direct purchase or transaction capabilities, but agents can work around this using the provided links and data for core research workflows.
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
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- No high-severity vulnerability alerts
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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?
Annotations already cover key behavioral traits (read-only, non-destructive, idempotent, closed-world), so the bar is lower. The description adds some context by specifying the types of data returned (e.g., sell-through rates, top brands), but does not disclose additional behaviors like rate limits, authentication needs beyond the optional api_key in schema, or response format details. No contradiction with annotations exists.
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 appropriately sized with two sentences: the first front-loads the core functionality and data points, and the second provides usage context. Every sentence adds value without redundancy, though it could be slightly more structured (e.g., bullet points for clarity).
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 the tool's moderate complexity (3 parameters, no output schema), annotations provide safety and idempotency info, and schema covers parameters fully. The description adds purpose and usage context but lacks details on output format, error handling, or data freshness, leaving some gaps for an agent to invoke it correctly without trial and error.
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%, providing full parameter documentation. The description does not add any semantic details beyond the schema, such as explaining interactions between parameters (e.g., how 'category' affects other outputs) or default behaviors. Baseline 3 is appropriate since the schema handles the heavy lifting.
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 tool's purpose with specific verbs ('Get') and resources ('EDC market trend data'), listing concrete data points like sell-through rates, top brands, price distribution, and new product counts. It distinguishes itself from siblings by focusing on aggregated market analysis rather than specific product searches, availability checks, or brand details.
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 provides clear context for usage ('Useful for market analysis and understanding what's hot in the EDC space'), which implicitly guides when to use it. However, it does not explicitly state when not to use it or name alternatives among the sibling tools, such as using 'search_products' for granular queries instead of aggregated trends.
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?
Annotations already indicate this is a read-only, non-destructive, idempotent, and closed-world operation. The description adds useful context by specifying it checks 'currently in stock' status and returns retailer-specific data, which helps the agent understand the real-time nature and scope of results. However, it does not disclose additional behavioral traits like rate limits, authentication needs (beyond the optional API key in schema), or error handling, leaving 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by return details and usage context in two additional sentences. Every sentence earns its place by adding value—no redundancy or waste. It is appropriately sized for the tool's complexity.
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 moderate complexity, rich annotations (covering safety and idempotency), and 100% schema coverage, the description is mostly complete. It explains the tool's purpose, output, and usage context. However, without an output schema, it could benefit from more detail on return format (e.g., structure of per-retailer data), but the annotations help mitigate this 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?
Schema description coverage is 100%, so the schema already fully documents the two parameters (api_key and product). The description does not add any meaning beyond what the schema provides, such as explaining how the 'product' input is resolved or the impact of the optional API key. Baseline 3 is appropriate since the schema handles the parameter documentation adequately.
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 specific action ('Check if... is currently in stock'), identifies the resource ('EDC product'), and distinguishes it from siblings by focusing on real-time availability rather than brand info, drops, trends, price comparisons, or general product searches. It explicitly mentions what it returns ('per-retailer availability, prices, and direct purchase links'), which further clarifies its unique purpose.
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 provides clear context for when to use this tool ('Useful for finding where to buy a specific product'), which implies it's for checking stock status of known products. However, it does not explicitly state when not to use it or name alternatives among the sibling tools, such as using 'search_products' for broader discovery or 'get_price_comparison' for price-focused queries without stock checks.
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?
Annotations already provide strong behavioral hints (read-only, non-destructive, idempotent, closed-world), so the bar is lower. The description adds useful context about the specific data returned (e.g., price range, top 5 products) and the tool's purpose for research/comparison, but does not disclose additional behavioral traits like rate limits, authentication needs beyond the optional API key in the schema, or error handling.
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 front-loaded with the core purpose and detailed return data, followed by a concise usage context. Both sentences earn their place by providing essential information without redundancy or fluff, making it highly efficient and well-structured.
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 moderate complexity (brand info retrieval), rich annotations (covering safety and idempotency), and no output schema, the description is mostly complete. It details the returned data and usage context, but could improve by mentioning potential limitations (e.g., brand not found) or output format specifics to fully compensate for the lack of output schema.
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%, with clear documentation for both parameters (brand name/slug and optional API key). The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining format requirements or usage examples for the 'brand' parameter. Baseline score of 3 is appropriate when the schema fully covers 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 specific action ('Get detailed information') and resource ('an EDC brand'), and enumerates the exact data returned (product counts, price range, categories, top products). It distinguishes this tool from siblings by focusing on comprehensive brand-level data rather than availability checks, drops, trends, price comparisons, or product searches.
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 provides clear context for usage ('Useful for brand research and comparison'), which implicitly suggests when to use this tool. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools, such as using 'search_products' for individual product queries instead.
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?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the agent knows this is a safe, repeatable read operation. The description adds useful context about the time frame ('last 7 days') and data fields included (prices, brands, etc.), but does not disclose additional behavioral traits like rate limits, authentication needs beyond the optional api_key, or pagination 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?
The description is appropriately sized and front-loaded, with the core purpose stated first. Every sentence earns its place: the first defines the tool, the second details included data, and the third provides usage context. There is no wasted text, making it efficient and clear.
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 moderate complexity (3 parameters, no output schema) and rich annotations (readOnlyHint, idempotentHint), the description is mostly complete. It covers purpose, data fields, and usage context. However, it lacks details on output format or error handling, which would be helpful since there is no output schema, leaving a minor 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?
Schema description coverage is 100%, so the schema fully documents the three parameters (api_key, category, limit). The description does not add meaning beyond the schema, such as explaining parameter interactions or default behaviors, but it implies filtering by category and limiting results, aligning with the schema. Baseline 3 is appropriate when the schema does the heavy lifting.
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 tool's purpose: 'Get the latest EDC product drops and new releases from the last 7 days.' It specifies the verb ('Get'), resource ('EDC product drops and new releases'), and scope ('from the last 7 days'), distinguishing it from siblings like 'search_products' or 'get_market_trends' by focusing on recent releases.
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 provides clear context for usage: 'Useful for staying current on what's new in the EDC market.' It implies when to use this tool (for recent product updates) but does not explicitly state when not to use it or name alternatives among siblings, such as using 'search_products' for broader queries.
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?
Annotations already indicate read-only, non-destructive, idempotent, and closed-world behavior. The description adds value by specifying what data is returned (prices, availability, links) and that it identifies the best price, but doesn't disclose additional behavioral traits like rate limits, authentication needs (beyond the optional api_key in schema), or error handling. No contradiction with annotations exists.
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 front-loaded with the core purpose in the first sentence, followed by specific return details and a key feature (best price identification). Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.
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 moderate complexity, rich annotations, and 100% schema coverage, the description is mostly complete. It explains the output format (prices, availability, links) and key behavior (best price identification), compensating for the lack of an output schema. However, it could improve by mentioning limitations or typical use cases relative to siblings.
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%, with clear documentation for both parameters. The description doesn't add meaning beyond the schema, as it doesn't explain parameter usage, constraints, or examples. Baseline score of 3 is appropriate since the schema adequately covers parameter semantics without extra description input.
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 specific action ('Compare prices'), target resource ('EDC product across multiple retailers'), and scope ('returns each retailer's current price, compare-at price, availability status, and direct product links'). It distinguishes from siblings by focusing on price comparison rather than availability checks, brand info, market trends, or product searches.
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 usage context through 'compare prices for the same EDC product' and 'identifies the best available price,' suggesting it's for price shopping. However, it doesn't explicitly state when to use this tool versus alternatives like 'check_availability' or 'search_products,' nor does it provide exclusion criteria or prerequisites beyond the required product parameter.
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?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds context on the search scope (100,000+ products, 1,000+ brands) and return fields (titles, prices, etc.), but doesn't mention rate limits, auth needs beyond the optional api_key, or pagination 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?
The description is front-loaded with the core purpose in the first sentence, followed by return details and scope. Both sentences are informative with zero waste, efficiently covering search functionality, output, and dataset scale without redundancy.
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 moderate complexity (search with 5 optional params), rich annotations, and 100% schema coverage, the description is mostly complete. It lacks output schema, but describes return values (titles, prices, etc.) and scope. However, it doesn't detail error handling or exact response structure, leaving minor gaps.
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 schema fully documents all 5 parameters. The description mentions search by keyword, category, brand, or material, aligning with the schema but not adding extra meaning. With high schema coverage, baseline 3 is appropriate as the description doesn't compensate beyond what's already structured.
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 tool searches for EDC products using specific criteria (keyword, category, brand, material) and returns detailed product information. It distinguishes from siblings by focusing on broad search rather than availability checks, brand info, drops, trends, or price comparisons.
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 usage context by listing searchable attributes (keyword, category, brand, material) and mentioning the scope (100,000+ products, 1,000+ brands). However, it doesn't explicitly state when to use this versus alternatives like check_availability or get_price_comparison, though the focus on search is clear.
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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