mcp-server-playground

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct resource and action: table exploration, product lookup, customer orders, sales aggregation, product ranking, review search, review insertion, and table listing. The description for search_reviews explicitly contrasts it with search_products, eliminating any ambiguity.
Naming Consistency3/5
Names are all lowercase with underscores, but conventions mix: four use verb_noun (describe_table, search_products, add_review, list_tables) while the rest are noun phrases (customer_orders, sales_summary, top_products). This is readable but not a uniform pattern.
Tool Count5/5
Eight tools is well-scoped for an e-commerce analytics/playground server. Each tool covers a distinct capability (exploration, search, reporting, review handling) without bloat or thinness.
Completeness4/5
The tool surface covers exploration, search, analytics, and review insertion comprehensively. Minor gaps exist (no update/delete for reviews, no CRUD for products/customers), but these are likely outside the server's read-oriented analytics purpose.
Average 4/5 across 8 of 8 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 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
This repository is licensed under MIT License.
This repository includes a README.md file.
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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 indicate readOnlyHint=false, and the description's statement 'This modifies the database' confirms the write behavior without adding new context. It does not disclose idempotency implications, error handling, or any side effects beyond the insertion itself.
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 concise and front-loaded with the core action, but the second sentence 'This modifies the database' is redundant with the already clear 'Insert' action. It could be trimmed to a single sentence without losing essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has four parameters and no output schema, so the description needs to provide substantial context about required fields, constraints, or return behavior. It only gives a minimal 'Insert a new review row' statement, leaving the agent to infer the purpose and implications of each parameter and the result of the operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description should compensate by explaining what the parameters represent, but it only mentions 'a product' and ignores customer_id, rating, and comment. The schema itself defines these parameters, yet the description fails to add any explanatory value about their meaning or how they map to the review insertion.
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 ('Insert') and the resource ('a new review row for a product'), making it easy for an agent to know exactly what the tool does. This distinctness is reinforced by the sibling tool 'search_reviews', which is read-oriented, whereas 'add_review' is explicitly an insertion.
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 implies when to use the tool (when adding a review) through the verb 'Insert', but it does not explicitly mention alternatives or exclusion criteria. There is no guidance on conditions like product existence or when to prefer 'search_reviews' for reading reviews.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, so the description does not need to repeat that. However, the description adds no additional behavioral context—it does not mention return format, pagination, sorting defaults, or any side effects. It is a bare-bones statement with no extra transparency beyond what annotations provide.
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 a single, front-loaded sentence that directly states the action and main attributes. Every word contributes meaning; there is no redundancy or filler. It is exceptionally concise while conveying the essential purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 8 parameters and no output schema, a one-sentence description is insufficient. It omits any mention of sorting, limit, or return shape, and does not provide guidance on how to combine filters. Although the schema covers parameter details, the description lacks the contextual richness needed to understand the tool's full behavior, especially in the absence of an 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?
The description lists key filter types (keyword, category, price range, stock availability) which roughly correspond to schema properties, and 'price range' usefully implies the pair of min_price and max_price. However, with 63% schema description coverage, the description adds only marginal meaning beyond the rich parameter descriptions already present in the schema. It does not explain the detailed semantics like keyword matching behavior.
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 function: 'Find products by keyword, category, price range or stock availability.' It names a specific resource (products) and the main filter dimensions, which distinguishes it from sibling tools like 'search_reviews' that target a different resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention sibling tools, use cases, or exclusions. While the resource and filters imply a product search use case, there is no direct comparison or exclusions, leaving the selection process to inference.
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?
Annotations already declare readOnlyHint=true, so safety is clear. The description adds useful behavioral context beyond annotations by stating lookup keys ('id, email or partial name') and the return characteristic 'line items already joined.' It does not disclose multi-match handling or output ordering, but these are less critical for a read-only lookup tool.
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?
Single sentence with no wasted words. The verb-led structure front-loads the main action, and the clause about line items adds key return information efficiently.
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?
For a simple read-only lookup tool with no output schema, the description provides adequate context: what resource is queried, by what keys, and what the result includes. Minor gaps include behavior for multiple customer matches and parameter precedence, but the description is still sufficient for basic usage.
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 50%; description partially compensates by naming 'id, email or partial name' as lookup criteria. However, email and customer_id formats are not described, and the description does not clarify behavior when multiple parameters are supplied or when partial names match multiple customers. Name semantics are fully covered in the schema.
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 uses specific verb 'look up' and resource 'customer' with clear return value: 'return their orders with line items already joined.' Clearly distinguishes from sibling tools like search_products and sales_summary, which are product/revenue-focused, not customer-focused.
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?
Description implies usage for retrieving a customer's order history but provides no explicit when-to-use guidance or alternatives. It does not compare against sibling tools like search_products or sales_summary, nor does it state exclusions or prerequisites.
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, so the main safety profile is covered. The description adds context about the ranking criteria, but it does not disclose behavioral details like sort order, handling of missing date ranges, or what data is included. With annotations, the bar is lower, and the description adds some value, but not rich behavioral context, justifying a 3.
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 a single, front-loaded sentence that covers all key aspects: the action (rank), the resource (products), the metrics (revenue/units/rating), and the time scope (date range). Every word earns its place, with no redundancy or fluff.
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?
For a simple read-only ranking tool, the description is mostly complete: it specifies the purpose, ranking metrics, and date range. It does not describe the return format, but the tool likely returns a ranked list, which is implied. The optional parameters and their defaults are defined in the schema, so the description covers the essential usage context.
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 does explain the 'metric' enum values (revenue, units, rating) and the 'from/to' date range, but it does not describe the 'limit' parameter at all. The parameter names provide some self-evident meaning, but a fuller explanation of all parameters would elevate the score.
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 function with a specific verb and resource: 'Rank products by revenue, units sold or average review rating over a date range.' It distinguishes itself from siblings like 'search_products' (rank vs. search) and 'sales_summary' (product-level ranking vs. aggregate summary).
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 the tool: when you need to rank products by one of three specific metrics within a date range. It does not explicitly mention alternatives or exclusions, but the context is unmistakable, aligning with a 'clear context, no exclusions' rating.
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?
The annotations already declare readOnlyHint=true, and the description aligns with this by using 'Show'. It adds a specific behavioral detail—returning three sample rows—which goes beyond the annotation and helps the agent set expectations. No contradictions or hidden side effects are implied.
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 a single, front-loaded sentence: 'Show the column definitions and three sample rows for one table.' It contains zero filler and immediately conveys the action, scope, and output format.
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 read-only tool with one well-documented parameter and safety annotations, the description fully covers the essential behavior: column definitions and sample rows. It's complete without needing to explain return values in detail, and the absence of an output schema is compensated by the description's clarity.
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 coverage is 100%, with the 'table' parameter having a helpful description including an example and a reference to list_tables. The tool description adds no further parameter information, but the schema already carries the burden, so the baseline 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 uses the specific verb 'Show' and identifies the resource as 'column definitions and three sample rows for one table', making the tool's function unambiguous. This clearly distinguishes it from sibling tools like list_tables, which lists table names, and the various search/query tools.
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?
While the description doesn't explicitly state 'when to use' or name alternatives, the context is clear: to inspect a table's structure. The parameter description adds useful guidance by recommending list_tables to discover valid names, providing a natural workflow. No competing sibling exists, so no exclusions are needed.
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?
The annotation readOnlyHint=true already signals this is a safe read operation. The description adds scope ('every table') and output detail ('with its row count'), which is useful. However, it does not disclose potential performance implications of counting rows across all tables, or whether any filtering/sorting is applied. With annotations covering safety, this is adequate but not rich.
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, immediately front-loads the primary action ('List every table'), and provides supplementary guidance ('Start here'). Every word earns its place with no redundancy.
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 zero-parameter, read-only listing tool, the description fully covers purpose, scope, output content, and usage context. No output schema exists, but the description's mention of 'row count' sufficiently indicates the return shape. Sibling tools are distinct and the entry-point guidance completes the context.
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 tool has zero parameters and schema coverage is 100%, so there is nothing for the description to add about parameters. Baseline for zero-parameter tools is 4, and the description appropriately focuses on behavior instead of 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 tool's action ('List every table'), the exact resource ('store database'), and the result ('with its row count'). This distinguishes it from sibling tools like describe_table (which likely describes a specific table's schema) and search_products (which searches data).
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?
'Start here when exploring' gives explicit contextual guidance for when to use this tool as an entry point. It does not explicitly enumerate when not to use it or name alternatives, but the context is clear enough to guide selection among siblings.
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?
Annotations already declare readOnlyHint=true, so the safety profile is covered. Description adds meaningful behavioral detail: cancellation exclusion rule and its exception when grouping by status, which is beyond the structured data.
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?
One concise sentence packs the primary action, parameters, and an edge case. No filler, front-loaded, and all information is relevant.
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?
With an output schema present and readOnlyHint annotation, the description adequately covers core behavior and an important edge case. It doesn't mention date-range basis or output format, but these are likely self-evident or handled by the output schema.
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 descriptions cover from/to, but group_by only has an enum. The description clarifies the behavioral impact of group_by=status (cancelled orders are included), adding semantic value that the schema alone does not convey.
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?
Clear verb 'Aggregate' with resource 'sales' and explicit scope (date range, grouping dimensions). The description immediately distinguishes it from sibling tools like top_products or search_products by focusing on aggregation rather than lookup or ranking.
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?
Provides clear context for when to use the tool (aggregating sales over a date range) but does not explicitly name alternatives or exclusions. Agent can infer from siblings, but no direct comparison is given.
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?
Annotations already declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds valuable context beyond that: it returns 'matching reviews with the product they are about', and the Japanese example clarifies the intended query type. No contradiction exists. However, it stops short of describing result ordering, pagination, or edge cases, so a 4 is appropriate.
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 exactly two sentences, front-loads the core function, and includes a usage example plus sibling differentiation. Every word is purposeful; there is no redundancy or filler, making it highly concise 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?
For a read-only search tool with 4 parameters, the description adequately covers what it does, when to use it, and what it returns. The schema supplies parameter details, and annotations cover safety. The only slight gap is the lack of mention of rating filters, but these are visible in the schema, so the overall context is complete enough for an agent to correctly invoke it.
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 75%, and the schema already provides detailed descriptions for query, min_rating, and max_rating, including the ANDing behavior and full-text index fallback for short queries. The tool description itself adds no parameter-level information, so it does not compensate for the undocumented limit parameter. Baseline of 3 is correct since the schema carries the semantic burden.
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 'Full-text search over review bodies', which is a specific verb+resource pairing that immediately identifies the tool's function. It further differentiates from sibling tool search_products by explicitly stating it searches review content rather than product names, making the purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use this for questions about what customers say', and contrasts it with search_products which 'only matches product names'. This provides clear when-to-use and alternative guidance, fulfilling the dimension fully.
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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