movieplexx-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation4/5
Tools are mostly distinct: film_history covers performance history, get_film and list_films cover films, list_showtimes covers performances, and search_films covers search. However, list_films with upcoming filter and search_films could be confused for similar film queries.
Naming Consistency4/5Four tools follow verb_noun pattern (get_film, list_films, list_showtimes, search_films), but film_history is a noun_noun exception, causing minor inconsistency.
Tool Count5/5With 5 tools, the set is well-scoped for a movie information server, fitting the ideal range of 3-15 tools for focused functionality.
Completeness4/5The server covers key read operations: listing, searching, detail, showtimes, and history. Minor gaps include lacking a dedicated performance detail tool, but the set is largely complete for its domain.
Average 3.6/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 21 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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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 the full burden. It only mentions filtering and the only_upcoming flag. It does not disclose important behavioral traits like pagination, default ordering, result limits, or what happens when no filters are applied (e.g., does it return all future showtimes?). For a list tool, this is insufficient.
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 very concise, with two clear sentences that front-load the main action and immediately follow with the optional filters. No wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (3 optional params) and the presence of an output schema, the description is adequate but lacks details about default behavior, pagination, and order. An agent could use it correctly but might have questions about edge cases.
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?
Schema description coverage is 0%, so the description must clarify parameter meanings. It explains 'date' as ISO date and 'only_upcoming' to hide past performances. However, 'film_slug' is not defined; the agent might not know what a slug is. Partial compensation for missing schema descriptions.
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 lists performances with optional filters. It distinguishes from sibling tools like film_history and search_films by focusing on showtimes, though 'performances' could be more specific (e.g., film showtimes).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied: use to list showtimes filtered by date or film slug, with an option to hide past performances. However, there is no explicit guidance on when to use this tool vs alternatives, or when not to use it. Siblings are different enough that context provides clarity, but the description does not directly address selection criteria.
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 must fully disclose behavior. It states it lists films (read-only), but lacks important details such as whether pagination is used, ordering, or any side effects. This is minimal for a listing tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose, and contains no extraneous information. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and an output schema, the description is adequate but misses contextual details such as the default value of 'only_current' (true) and whether the list is paginated or sorted. The output schema may fill some gaps, but the description could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for the only parameter. The description adds clear meaning: 'With only_current, restrict to films with an upcoming performance,' which explains the boolean parameter's effect.
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 'List all known films' which is a specific verb and resource. It distinguishes from sibling tools like 'search_films' by indicating it's a full listing, but does not explicitly differentiate from 'list_showtimes' or 'get_film'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by describing the 'only_current' parameter for filtering to upcoming performances, but it does not provide explicit guidance on when to use this tool vs. alternatives like 'search_films' or when not to use it.
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?
With no annotations, the description carries the full burden. It mentions 'append-only', implying read-only behavior, but lacks details on authentication, rate limits, or error handling. The term 'scrape history' provides some context but could be more explicit.
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 sentence with no unnecessary words. It is front-loaded with the main verb and resource, making it easy to digest.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (1 required param, no enums, output schema exists), the description adequately conveys the purpose. However, it could specify that it returns a list of historical entries and that the operation is safe.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. While 'film_slug' is self-explanatory, the description adds no extra meaning or examples. It does not clarify how to obtain the slug or its expected format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Return'), the resource ('append-only scrape history for a film's performances'), and provides specific context ('sold-out / status drift'). This distinguishes it from sibling tools like 'get_film' or 'list_showtimes'.
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 does not explicitly state when to use this tool versus alternatives. It implies it is for historical data, but no direct guidance is given, such as 'Use list_showtimes for current showtimes'.
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 bears full responsibility for behavioral disclosure. It only states case-insensitive substring matching but omits details like result limits, pagination, ordering, or whether the search returns full film objects or summaries.
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?
A single concise sentence that efficiently conveys the tool's purpose and search capabilities with no extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple nature of the tool (one parameter, no nested objects) and the existence of an output schema, the description provides essential information. However, it could be slightly more complete by noting that the search is across multiple fields, which it does.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description explains that the 'query' parameter is searched across title, genre, director, and distributor. This adds significant meaning beyond the bare parameter name in the schema, though it does not mention that the parameter is required (already in schema).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches films by multiple criteria (title, genre, director, distributor) with case-insensitive substring matching. It distinguishes from sibling tools like get_film (specific film), list_films (listing all), and film_history (history) by indicating it's a search across multiple fields.
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?
There is no guidance on when to use this tool versus alternatives like list_films or get_film. The description does not mention any prerequisites, limitations, or scenarios where this tool is preferred.
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?
With no annotations, the description discloses the expected behavior: returns a full record or null if unknown. This is sufficient for a simple read operation, though it does not cover edge cases or auth requirements.
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 sentence with no wasted words. It front-loads the verb and quickly conveys the essential purpose and return behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and an output schema, the description sufficiently covers the action, input, and output (full record or null). The presence of an output schema further clarifies the return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'slug' but does not explain what a film slug is or provide examples. With 0% schema description coverage, this adds minimal meaning beyond the parameter name and type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('Return'), the resource ('full film record'), and the input ('one slug'), and implies it is for a single film, distinguishing it from sibling tools like list_films or search_films.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have a specific slug and want a single record, but does not provide explicit guidance on when not to use it or compare with alternatives like search_films for fuzzy lookups.
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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