Pokédex MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation4/5
The tools have mostly distinct purposes: fetch_pokemon retrieves detailed info, get_pokemon_encounters focuses on locations, get_type_effectiveness handles type interactions, and search_pokemon enables partial name matching. However, fetch_pokemon and search_pokemon could be slightly confused as both involve Pokémon lookup, but their descriptions clarify the distinction between exact and partial matching.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern with snake_case, using clear verbs like 'fetch', 'get', and 'search' paired with descriptive nouns. There are no deviations in naming conventions, making the set predictable and readable.
Tool Count4/5With 4 tools, the count is reasonable for a Pokédex server, covering core functionalities like info retrieval, location data, type analysis, and search. It is slightly lean but well-scoped, as each tool serves a distinct purpose without obvious bloat.
Completeness3/5The tools cover key aspects such as fetching details, encounters, type effectiveness, and searching, but there are notable gaps. For a Pokédex domain, missing operations like listing Pokémon by generation, evolution chains, or abilities limit coverage, though agents can work around this with the existing tools.
Average 2.9/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states the tool fetches 'detailed information', which implies a read-only operation, but doesn't specify what 'detailed' includes, potential rate limits, authentication needs, error handling, or response format. This leaves significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It's front-loaded with the core purpose and efficiently specifies the input method. Every part of the sentence contributes essential information, making it appropriately concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what 'detailed information' includes, how results are structured, or any behavioral traits like error cases. For a tool with rich sibling context and no structured support, more detail is needed to guide effective usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'name' documented as accepting 'name or ID'. The description adds no additional semantic context beyond this, such as examples, format constraints, or case sensitivity. With high schema coverage, the baseline is 3, and the description doesn't enhance it further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('fetch detailed information') and resource ('about a Pokémon'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'search_pokemon' or 'get_pokemon_encounters', which likely have overlapping functionality. A perfect score would require explicit distinction from these alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search_pokemon' or 'get_pokemon_encounters'. It mentions fetching by 'name or ID', but doesn't clarify if this is for exact matches only or how it differs from search functionality. No exclusions or prerequisites are mentioned.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe any behavioral traits such as whether it's a read-only operation, potential rate limits, error conditions, or what the output format might look like. This leaves significant gaps in understanding how the tool behaves beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently communicates the tool's purpose without any wasted words. It's front-loaded with the essential information and appropriately sized for a simple tool with one parameter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for effective tool use. It doesn't explain what 'location encounter information' entails (e.g., format, structure, or examples), nor does it address behavioral aspects like error handling or limitations. For a tool with no structured metadata, the description should provide more context about what to expect from the operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'name' parameter clearly documented as 'The name or ID of the Pokémon'. The description doesn't add any additional meaning beyond what the schema provides, but since schema coverage is high, the baseline score of 3 is appropriate as the schema adequately handles parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'location encounter information for a Pokémon', making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'fetch_pokemon' or 'search_pokemon' in terms of what specific information is retrieved versus those alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'fetch_pokemon' or 'search_pokemon'. It lacks any context about prerequisites, exclusions, or specific scenarios where this tool is preferred over others, leaving the agent to infer usage from the tool name alone.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves information, implying a read-only operation, but doesn't specify any behavioral traits such as error handling, rate limits, authentication needs, or what happens with invalid inputs. This leaves significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary details. It's front-loaded with the core action and resource, making it easy to understand at a glance, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of retrieving type effectiveness data, the description is incomplete. With no annotations, no output schema, and only basic parameter coverage, it lacks details on return values (e.g., format of strengths/weaknesses), error cases, or usage context. This makes it inadequate for an agent to fully understand how to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'name' documented as 'The name or ID of the type'. The description adds no additional meaning beyond this, such as examples or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('type effectiveness information'), specifying what information is retrieved (strengths, weaknesses, resistances). However, it doesn't explicitly differentiate from sibling tools like 'fetch_pokemon' or 'search_pokemon', which might also involve type data but focus on different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, nor does it relate to sibling tools like 'fetch_pokemon' that might overlap in domain but serve different purposes (e.g., retrieving Pokémon vs. type effectiveness).
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?
No annotations are provided, so the description carries full burden. It mentions 'partial name match', which hints at search behavior, but doesn't disclose other traits like pagination, rate limits, authentication needs, error handling, or what the return format looks like (especially with no output schema). For a search tool with zero annotation coverage, this is a significant gap in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration. Every word earns its place, 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.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a search operation with one parameter), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the search returns (e.g., list of Pokémon objects, error cases), behavioral traits, or usage context. For a tool with minimal structured data, the description should provide more completeness to aid the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'query' documented as 'Partial name to search for'. The description adds no additional meaning beyond this, as it essentially restates the schema's description. With high schema coverage, the baseline is 3, and the description doesn't compensate with extra details like examples or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search' and resource 'Pokémon', specifying 'by partial name match' which indicates the matching behavior. It distinguishes from siblings like 'fetch_pokemon' (likely by exact ID) and 'get_pokemon_encounters' (different resource), though it doesn't explicitly name alternatives. The purpose is specific but lacks explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'fetch_pokemon' or 'get_pokemon_encounters'. It implies usage for partial name searches but doesn't state exclusions (e.g., when exact ID is known) or prerequisites. No explicit when/when-not instructions are given.
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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