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rodcar

BCRP-MCP

by rodcar
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The three tools have clearly distinct purposes with no overlap: get_time_series_data retrieves actual time series data, search_time_series_by_group finds series within a specific group, and search_time_series_groups searches for group names. Each tool serves a different function in the data discovery and retrieval workflow.

    Naming Consistency5/5

    All tools follow a consistent snake_case naming pattern with clear verb_noun structure: get_time_series_data, search_time_series_by_group, and search_time_series_groups. The naming convention is predictable and readable throughout the set.

    Tool Count4/5

    Three tools is reasonable for a specialized BCRP data server, though it feels slightly minimal. The tools cover core discovery and retrieval workflows, but additional utilities like metadata lookup or data transformation might enhance completeness. The count is appropriate but could benefit from slight expansion.

    Completeness4/5

    The toolset provides good coverage for the BCRP time series domain with clear data retrieval and discovery capabilities. Minor gaps exist, such as no direct metadata lookup by code or data transformation tools, but agents can work effectively with the provided search and get operations for most common use cases.

  • Average 4.1/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 0 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

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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?

    With no annotations provided, the description carries full burden. It discloses the data source (BCRP database) and return format, but lacks information on rate limits, authentication requirements, error handling, or data freshness. It adequately describes the read-only nature but misses operational constraints.

    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 well-structured with clear sections (purpose, args, returns) and avoids redundancy. However, the opening sentence could be more front-loaded with key distinctions from siblings, and some details about the BCRP source could be condensed.

    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 3 parameters with 0% schema coverage and an output schema present, the description does an excellent job explaining parameter semantics and return format. It covers the essential 'what' and 'how', though it lacks context on when to use versus siblings and behavioral constraints like rate limits.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    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 fully. It provides detailed semantics for all three parameters: explains what time_series_code is, specifies date format variations (monthly vs daily) for start and end, and clarifies their roles in data retrieval. This adds significant value beyond the bare schema.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    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 data for a specific time series within a date range' and specifies it retrieves from the BCRP database. It distinguishes from siblings by focusing on data retrieval for a specific code rather than searching by group, though the distinction could be more explicit.

    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 usage by specifying date ranges and time series codes, but does not explicitly state when to use this tool versus the sibling tools (search_time_series_by_group, search_time_series_groups). No guidance on prerequisites or alternative scenarios is provided.

    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 provided, the description carries full burden. It discloses that the tool retrieves metadata (not actual data), describes the return format in detail, and mentions error handling. However, it doesn't cover important behavioral aspects like rate limits, authentication requirements, or whether this is a read-only operation.

    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 well-structured with clear sections (purpose, Args, Returns), front-loads the core functionality, and every sentence adds value. No redundant information or unnecessary elaboration.

    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 no annotations and no output schema, the description does an excellent job explaining the tool's purpose, parameters, and return format. It could be more complete by addressing behavioral aspects like authentication or rate limits, but for a search tool with one parameter, it provides substantial context.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema has 0% description coverage, so the description must compensate fully. It provides excellent parameter semantics: explains what 'time_series_group' represents, specifies it should match the 'Grupo de serie' field, and clarifies matching/containment logic. This adds substantial value beyond the bare 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 specific action ('search for time series within a specific group'), identifies the resource (BCRP database), and distinguishes from siblings by focusing on group-based filtering rather than data retrieval (get_time_series_data) or group searching (search_time_series_groups).

    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 about when to use this tool (searching within a specific group from BCRP database) but doesn't explicitly state when not to use it or name specific alternatives. The sibling tools suggest natural alternatives but aren't explicitly mentioned in the description.

    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 provided, the description carries full burden. It discloses that it returns a list of unique group names (not the data itself) and returns empty list for no matches, which is useful behavioral context. However, it doesn't mention performance characteristics, rate limits, authentication needs, or whether results are paginated/sorted.

    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 well-structured with a clear purpose statement, detailed parameter explanation, and return value description. Every sentence adds value - no wasted words. It's appropriately sized for a single-parameter search tool.

    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 operation), no annotations, and the presence of an output schema (which covers return values), the description is mostly complete. It explains the purpose, parameters, and basic behavior well. Minor gaps include lack of information about search algorithm (exact match, partial, case sensitivity) and result limitations.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage, the description fully compensates by explaining the 'keywords' parameter in detail: it's a list of strings, each keyword should be a single word without spaces, and multiple keywords can be used. This adds crucial meaning beyond the bare 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: 'Search for time series groups using one or multiple keywords' and specifies it searches the BCRP database. It distinguishes from siblings by focusing on groups rather than individual time series data (get_time_series_data) or searching within groups (search_time_series_by_group).

    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 about when to use this tool (searching for time series groups by keywords in the BCRP database), but doesn't explicitly state when not to use it or directly compare it to sibling tools. It implies usage through its specific focus on groups rather than data or within-group searches.

    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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