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ek-nath

openfda-mcp-server

by ek-nath
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools target distinct OpenFDA endpoints: drug labels and adverse events. There is no overlap in their purposes, making it clear which to use.

    Naming Consistency4/5

    Both names follow a verb_noun pattern, but one uses 'search' and the other uses 'get', which is a minor inconsistency. Otherwise the pattern is clear and predictable.

    Tool Count3/5

    With only two tools, the server feels thin for the OpenFDA domain, which has many potential endpoints. However, the two tools cover two common use cases, so the count is borderline acceptable.

    Completeness2/5

    OpenFDA offers many data types (e.g., recalls, enforcement reports, labeling for drugs and devices). Only drug labels and adverse events are covered, leaving significant gaps that would force agents to look elsewhere.

  • Average 3.7/5 across 2 of 2 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

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    With no annotations, the description carries the full burden but only adds the generic/brand nuance and the default limit (already in schema). It does not disclose output format, error behavior, rate limits, or any other behavioral traits beyond the basic search.

    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 concise and includes a structured arg list, but the arg list largely duplicates the schema. Still, it is free of unnecessary filler and front-loads the core purpose.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The description is adequate for a simple search tool, but it lacks explicit guidance on when to use it over the sibling tool and does not address any access or output considerations. Since an output schema exists, return values need not be explained, but the behavioral gaps hurt completeness.

    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%, so the description compensates by explaining the meaning of drug_name with an example and specifying the default for limit, adding 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 a specific action (search) and resource (OpenFDA drug labels), and mentions generic or brand name, distinguishing it from the sibling tool for adverse events.

    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?

    It provides clear context for when to use the tool (searching drug labels by name) but does not explicitly address alternatives or situations when the sibling tool should be used instead.

    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?

    No annotations are provided, so the description carries the burden of behavioral disclosure. The word 'Search' implies a read-only operation, which is a positive signal, but it does not disclose potential rate limits, API authentication requirements, pagination behavior, or error handling. Given that the description is minimal and lacks these details, a score of 3 reflects that it provides some behavioral context (read operation) but is far from comprehensive.

    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. It leads with the purpose, then lists arguments in a clear key-value format. Every sentence provides useful information without redundancy or excessive length. It is appropriately sized for a tool with only two parameters.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The tool has an output schema (context signal) so return values are likely covered there, reducing the description's burden. However, there are no annotations, and the description omits details such as pagination, rate limits, or usage constraints that would be valuable for a search tool interacting with an external API. It is adequate for basic invocation but not fully complete for a production 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?

    The schema has no descriptions for its parameters (0% coverage), so the description must compensate. It does so by explicitly defining both 'drug_name' and 'limit': 'drug_name: The name of the drug.' and 'limit: Number of results to return (default 5).' This adds meaning beyond the raw schema types and defaults, though the explanations are brief.

    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 function: 'Search OpenFDA for adverse events associated with a drug.' It uses a specific verb (Search) and resource (OpenFDA adverse events), which distinguishes it from the sibling tool 'search_drug_label' that focuses on drug labels. This is a clear, non-tautological purpose statement.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does 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. There is no mention of use cases, exclusions, or relationships to sibling tools like search_drug_label. A user would need external knowledge to decide which tool fits their need.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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