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joshua-bailey

eiu-mcp-server

Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation3/5

    eiu_browse and eiu_search both support keyword-based series discovery, so agents may be unsure which to call when looking up series codes. eiu_browse is broader because it also covers geographies, while eiu_search is narrowly focused on series, but the boundary is somewhat blurred. eiu_get_data is clearly distinct.

    Naming Consistency4/5

    All tools share the eiu_ prefix and use lower_snake_case, making the naming pattern predictable. However, eiu_browse and eiu_search are bare verbs while eiu_get_data is verb_noun, which is a minor inconsistency.

    Tool Count4/5

    Three tools is a reasonable, compact set for a browse/search/fetch data workflow. The count is appropriate, though eiu_browse and eiu_search overlap enough that the set could arguably be reduced to two tools.

    Completeness4/5

    The core workflow of discovering geographies and series, then fetching EIU data, is covered without dead ends. Minor gaps exist around metadata details or available date ranges, but agents can complete the primary task effectively.

  • Average 4.4/5 across 3 of 3 tools scored. Lowest: 3.9/5.

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

    • No community issues in the last 6 months
    • 2 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.

  • 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?

    With no annotations, the description is the only source of behavioral context. It adds useful facts about cached reference data and the limit cap (max 100) beyond the schema, but it does not state read-only behavior, output characteristics, or error handling. Acceptable for a simple read-oriented search, but it is 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 concise and front-loaded: a one-sentence purpose, a clear usage pointer, and a compact Args block. No redundant wording or filler; every sentence contributes.

    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?

    The tool is simple, has an output schema to cover return values, and the description provides param details plus sequencing with eiu_get_data. It is slightly incomplete in not addressing the relationship to eiu_browse, but it contains the essential context for correct invocation.

    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 description coverage is 0%, but the description compensates by explaining both parameters: query with concrete examples and limit with default and max. It adds meaning beyond the schema, though it could be more precise about the interpretation of 'limit' (number of results vs. something else).

    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 states a clear verb and resource: search EIU series by keyword in cached reference data. It also notes the downstream use (finding series codes before calling eiu_get_data), but it does not explicitly differentiate from the sibling eiu_browse, so purpose is clear but sibling distinction is left implicit.

    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 gives explicit guidance: 'Use this to find series codes before calling eiu_get_data.' This establishes when to use the tool relative to eiu_get_data, but it does not mention alternatives such as eiu_browse or exclusions, so it falls short of full when/when-not coverage.

    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 carries the behavioral burden. It explains what happens with no filter versus with a filter and describes the show options. It does not mention output format or pagination, but the presence of an output schema reduces the need for that detail.

    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 compact and well-organized: a summary line, short usage guidance, and a clean args list. Every sentence contributes value, with no repetition or filler.

    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 two-parameter browse tool, the description is complete. It covers all parameters, defaults, and core behavior, and the output schema exists to document return values. The lack of explicit sibling routing is the only minor omission, but the intended use is clear.

    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 coverage is 0%, and the description fully compensates. It defines filter as an optional keyword with concrete examples, and show as selecting 'geographies', 'series', or 'both' with the default noted. This is meaningful semantic content 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 opens with a specific verb and resource: 'Browse available EIU geographies and data series.' This clearly distinguishes it from siblings like eiu_get_data and eiu_search, which presumably retrieve or search data rather than browse the catalog.

    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 gives clear operational context: 'Call with no filter to see all available geographies/series. Use filter to narrow results by keyword.' It explains how to use the tool and the effect of each parameter, though it does not explicitly state when to prefer eiu_search or eiu_get_data.

    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 provided, the description carries the full burden. It clearly implies a read-only fetch operation, states the prerequisite of finding valid codes first, and documents default date ranges and accepted frequency values. It could add error/rate-limit behavior, but the default date behavior and non-destructive nature are usefully conveyed.

    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 compact and well-structured. The high-level purpose is front-loaded, the prerequisite is stated in a single sentence, and the Args block is clean and easy to scan. No sentence is wasted or redundant.

    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 the tool has an output schema, return-value documentation is not needed. The description covers all five parameters, provides valid examples, explains the required discovery workflow, and states defaults. Everything an agent needs to invoke the tool correctly is present.

    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 fully compensates. It explains every parameter: ISO 2-char geography codes with examples, EIU series codes with examples, allowed frequency values, and date format/defaults for min_date and max_date. This is far 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 uses a specific verb ('Fetch') and a specific resource ('EIU forecast/indicator data') with clear scope by country and series codes. This distinguishes it well from the sibling tools eiu_browse and eiu_search, which are for finding codes rather than retrieving data.

    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?

    It explicitly explains the intended workflow: use eiu_browse or eiu_search first to find valid codes, then call this tool. This is clear guidance on when to use this tool versus the alternatives, leaving no ambiguity for the agent.

    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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  • Evaluate tool definition quality.

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