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AIweather-Anurag

OTTASIA

by AIweather-Anurag
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.3

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: search_titles for initial lookup, whats_new_on for new arrivals, and where_to_watch for availability. No overlap, so agents can easily select the correct tool.

    Naming Consistency2/5

    The naming pattern is inconsistent: one tool uses verb_noun (search_titles), while the other two use phrase-style names (whats_new_on, where_to_watch). This mixed convention may confuse agents expecting a uniform pattern.

    Tool Count4/5

    Three tools is on the lower end of the well-scoped range (3-15). While the set covers core functionality, it feels slightly minimal but still acceptable for a focused streaming guide server.

    Completeness2/5

    The tool set lacks obvious operations like browsing a provider's full catalog or filtering by genre/rating. Agents cannot answer queries such as 'What sci-fi movies are on Netflix in India?' without workarounds, indicating significant gaps.

  • Average 4.4/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 1 commit 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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?

    No annotations provided; description notes returns list with overviews+links and that limit affects response speed. No mention of auth, rate limits, or side effects. Adequate but not exhaustive.

    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?

    Front-loaded with purpose, followed by usage, provider list, country list, return info, and pairing hint. Country list is somewhat long but necessary. No wasted sentences.

    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?

    No output schema, but description explains return format and suggests pairing with where_to_watch. For a simple list tool with 3 well-defined parameters, it covers essential 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?

    Schema coverage is 100%; description adds context by listing common provider slugs, valid country codes, default country (IN), and limit range with performance hint. Adds value beyond 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?

    The description clearly states it lists latest movies and shows added to a specific streaming service and country. It distinguishes from siblings by mentioning pairing with where_to_watch for 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?

    Includes explicit examples and contexts like 'what's new on Netflix in India' and suggests pairing with where_to_watch. Lacks explicit when-not-to-use or contrast with search_titles.

    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 provided, so description carries full burden. It describes return fields (year, type, overview, link) and implies read-only operation via 'Search'. Lacks explicit safety or side-effect statement but is adequate.

    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?

    Three sentences covering purpose, usage context, and sibling distinction. No wasted words, front-loaded with key information.

    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?

    Given no output schema, description adequately covers return values (candidates with year, type, overview, link). Also provides usage guidance and differentiation from sibling, making it complete for a search tool.

    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%, and description adds minimal new meaning beyond the schema's parameter descriptions. It mentions 'country-aware OTTASIA link' relating to country param, but does not elaborate on q or limit beyond 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 states 'Search OTTASIA's catalog for movies or TV shows by name', clearly indicating verb and resource. It distinguishes from sibling tool 'where_to_watch' by specifying when to use each.

    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?

    Explicitly states when to use ('when a user asks find me X' or ambiguous queries) and when not to use ('Use where_to_watch instead when user has specific title and country'). Provides clear alternatives.

    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 fully explains the tool's behavior: returns matched title, year, provider list grouped by category, and includes a link to OTTASIA. It does not mention any side effects, authentication, or rate limits, but as a read-only tool this is acceptable.

    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 concise and well-structured: first sentence states purpose, then usage condition, then output format, then example streamers. Every sentence adds value without 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?

    The description covers all necessary information: input parameters (title and country with examples), output format (matched title, year, providers grouped, link), and specific countries and streamers. No missing details for an AI agent to use the tool correctly.

    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 100% with good descriptions already. The description adds extra context such as example titles ('Squid Game', 'RRR') and streaming services (Netflix, Amazon Prime, etc.), which helps the agent understand typical use cases beyond 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?

    The description clearly states the tool's purpose: 'Find which streaming services carry a specific movie or TV show in a specific Asian or Middle Eastern country.' It specifies the exact region and distinguishes from sibling tools like search_titles and whats_new_on by focusing on streaming availability in 30 specific markets.

    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 explicitly instructs when to use: 'Use this tool whenever a user asks where can I watch X in Y for any of these 30 markets' and lists the countries. It also provides examples of streamers. However, it does not explicitly mention when not to use or directly contrast with siblings.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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